From 346f794df34133a41458298ebbf9cecbe577eef9 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 15:56:05 -0600
Subject: [PATCH 01/27] fix(fpkm): update imports for zFPKM calculation
 improvements

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 60457fb9..a7e44e20 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -1,19 +1,23 @@
 from __future__ import annotations
 
 import asyncio
+import csv
 import functools
+import io
 import json
 import re
 import sys
+from collections.abc import Sequence
 from dataclasses import asdict, dataclass, field
-from io import TextIOWrapper
 from itertools import chain
 from pathlib import Path
 from typing import Final, Literal, cast
 
-import aiofiles
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
+import pandera.pandas as pa
+import pandera.typing.pandas as pat
 from fast_bioservices.biothings.mygene import MyGene
 from fast_bioservices.pipeline import gene_symbol_to_ensembl_and_gene_id
 from loguru import logger

From 985c6f23ec033a84cf01b5caa8f2af677ab9a7d3 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:01:36 -0600
Subject: [PATCH 02/27] fix(fpkm): use Salmon quantification instead of STAR
 quantification

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 428 ++++++++++++++++++---------------
 1 file changed, 230 insertions(+), 198 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index a7e44e20..6835423e 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -27,71 +27,45 @@
 
 
 @dataclass
-class _STARinformation:
-    num_unmapped: list[int]
-    num_multimapping: list[int]
-    num_no_feature: list[int]
-    num_ambiguous: list[int]
+class _QuantInformation:
     gene_names: list[str]
     count_matrix: pd.DataFrame
-
-    @property
-    def num_genes(self) -> int:
-        return len(self.count_matrix)
+    sample_name: str
+    filepath: Path
 
     @classmethod
-    async def build_from_tab(cls, filepath: Path) -> _STARinformation:
-        if filepath.suffix != ".tab":
+    def build_from_sf(cls, filepath: Path) -> _QuantInformation:
+        if filepath.suffix != ".sf":
             _log_and_raise_error(
-                f"Building STAR information requires a '.tab' file; received: '{filepath}'",
+                f"Building quantification information requires a '.sf' file; received: '{filepath}'",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
         if not filepath.exists():
             _log_and_raise_error(
-                f"Unable to find the .tab file '{filepath}'",
+                f"Unable to find the .sf file: {filepath}",
                 error=FileNotFoundError,
                 level=LogLevel.ERROR,
             )
 
-        async with aiofiles.open(filepath) as i_stream:
-            # Cannot use `asyncio.gather()` here because the order of execution is not guaranteed
-            unmapped = await i_stream.readline()
-            multimapping = await i_stream.readline()
-            no_feature = await i_stream.readline()
-            ambiguous = await i_stream.readline()
-
-            num_unmapped = [int(i) for i in unmapped.removesuffix("\n").split("\t")[1:]]
-            num_multimapping = [int(i) for i in multimapping.removesuffix("\n").split("\t")[1:]]
-            num_no_feature = [int(i) for i in no_feature.removesuffix("\n").split("\t")[1:]]
-            num_ambiguous = [int(i) for i in ambiguous.removesuffix("\n").split("\t")[1:]]
-
-        df = await _read_file(
-            path=filepath,
+        sample_name = filepath.stem.removesuffix("_quant.genes.sf")
+        df = pd.read_csv(
+            io.StringIO(filepath.read_text()),
             sep="\t",
-            header=None,
-            skiprows=4,
-            names=[
-                "ensembl_gene_id",
-                "unstranded_rna_counts",
-                "first_read_transcription_strand",
-                "second_read_transcription_strand",
-            ],
+            names=["ensembl_gene_id", "length", "effective_length", "tpm", sample_name],
         )
-        return _STARinformation(
-            num_unmapped=num_unmapped,
-            num_multimapping=num_multimapping,
-            num_no_feature=num_no_feature,
-            num_ambiguous=num_ambiguous,
-            gene_names=df["ensembl_gene_id"].values.tolist(),
+        return cls(
+            gene_names=df["ensembl_gene_id"].to_list(),
             count_matrix=df,
+            sample_name=sample_name,
+            filepath=filepath,
         )
 
 
 @dataclass
 class _StudyMetrics:
     study_name: str
-    count_files: list[Path]
+    quant_files: list[Path]
     strand_files: list[Path]
     __sample_names: list[str] = field(default_factory=list)
     __num_samples: int = 0
@@ -105,24 +79,24 @@ def num_samples(self):
         return self.__num_samples
 
     def __post_init__(self):
-        self.__num_samples = len(self.count_files)
-        self.__sample_names = [f.stem for f in self.count_files]
+        self.__num_samples = len(self.quant_files)
+        self.__sample_names = [f.stem for f in self.quant_files]
 
-        if len(self.count_files) != len(self.strand_files):
+        if len(self.quant_files) != len(self.strand_files):
             _log_and_raise_error(
                 (
                     f"Unequal number of count files and strand files for study '{self.study_name}'. "
-                    f"Found {len(self.count_files)} count files and {len(self.strand_files)} strand files."
+                    f"Found {len(self.quant_files)} count files and {len(self.strand_files)} strand files."
                 ),
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
 
-        if self.num_samples != len(self.count_files):
+        if self.num_samples != len(self.quant_files):
             _log_and_raise_error(
                 (
                     f"Unequal number of samples and count files for study '{self.study_name}'. "
-                    f"Found {self.num_samples} samples and {len(self.count_files)} count files."
+                    f"Found {self.num_samples} samples and {len(self.quant_files)} count files."
                 ),
                 error=ValueError,
                 level=LogLevel.ERROR,
@@ -145,7 +119,7 @@ def __post_init__(self):
                 level=LogLevel.ERROR,
             )
 
-        self.count_files.sort()
+        self.quant_files.sort()
         self.strand_files.sort()
         self.__sample_names.sort()
 
@@ -153,39 +127,68 @@ def __post_init__(self):
 @dataclass(slots=True)
 class SampleConfiguration:
     sample_name: str
-    fragment_length: float
+    effective_lengths: pd.DataFrame
+    mean_effective_length: float
     layout: str
     strand: str
     study: str
     library_prep: str
 
+    def __post_init__(self):
+        if len(self.effective_lengths.columns) > 2:
+            _log_and_raise_error(
+                message=f"Effective lengths dataframe for sample '{self.sample_name}' has more than 2 columns, expected 'name' and 'effective_length'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+        if "name" not in self.effective_lengths.columns:
+            _log_and_raise_error(
+                message=f"Effective lengths dataframe for sample '{self.sample_name}' is missing 'name' column",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+        if "effective_length" not in self.effective_lengths.columns:
+            _log_and_raise_error(
+                message=f"Effective lengths dataframe for sample '{self.sample_name}' is missing 'effective_length' column",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
-async def _read_text(path: Path | None, *, default: str, lower: bool = False) -> str:
-    if path is None:
-        return default
-    async with aiofiles.open(path) as f:
-        txt = (await f.read()).strip()
-    return txt.lower() if lower else txt
+    @classmethod
+    def to_dataframe(cls, samples: list[SampleConfiguration]) -> tuple[pd.DataFrame, pd.DataFrame]:
+        """Convert a list of SampleConfiguration to a dataframe.
+
+        :param samples: The list of SampleConfiguration objects to convert.
+        :return: A tuple of dataframes:
+            [0]: The sample configuration as a dataframe
+            [1]: The effective lengths as a separate data frame with `same_name` as columns
+        """
+        config = pd.DataFrame(
+            columns=["sample_name", "mean_effective_length", "layout", "strand", "study", "library_prep"]
+        )
 
+        genes = set()
+        for s in samples:
+            genes.update(s.effective_lengths["name"].to_list())
 
-def _sample_name_from_filepath(file: Path) -> str:
-    return re.search(r".+_S\d+R\d+(r\d+)?", file.stem).group()
+        lengths = pd.DataFrame(data=np.float64(0.0), columns=[s.sample_name for s in samples], index=list(genes))
+        for sample in samples:
+            ids: list[str] = sample.effective_lengths["name"].to_list()
+            data: npt.NDArray[np.floating] = sample.effective_lengths["effective_length"].to_numpy(dtype=np.float64)
+            lengths.loc[ids, sample.sample_name] = data
 
+        return config, lengths
 
-def _require_one(paths: list[Path], kind: Literal["layout", "strand", "preparation", "fragment"], label: str) -> Path | None:
-    if len(paths) == 1:
-        return paths[0]
-    if len(paths) == 0:
-        return None
-    _log_and_raise_error(
-        f"Multiple matching {kind} files for {label}, make sure there is only one copy for each replicate in COMO_input",
-        error=ValueError,
-        level=LogLevel.ERROR,
-    )
-    return None  # explicit return None to satisfy type-check
+
+def _sample_name_from_filepath(file: Path) -> str:
+    return re.search(r".+_S\d+R\d+(r\d+)?", file.stem).group()
 
 
-def _require_one(paths: list[Path], kind: Literal["layout", "strand", "preparation", "fragment"], label: str) -> Path | None:
+def _require_one(
+    paths: list[Path],
+    kind: Literal["layout", "strand", "preparation", "fragment"],
+    label: str,
+) -> Path | None:
     if len(paths) == 1:
         return paths[0]
     if len(paths) == 0:
@@ -195,22 +198,28 @@ def _require_one(paths: list[Path], kind: Literal["layout", "strand", "preparati
         error=ValueError,
         level=LogLevel.ERROR,
     )
-    return None  # explicit return None to satisfy type-check
 
 
 def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
-    gene_count_dir = Path(data_dir, "geneCounts")
+    quant_dir = Path(data_dir, "quantification")
     strand_dir = Path(data_dir, "strandedness")
 
-    gene_counts_directories: list[Path] = sorted([p for p in gene_count_dir.glob("*") if not p.name.startswith(".")])
+    if not quant_dir.exists():
+        raise FileNotFoundError(f"Quantification directory not found: {quant_dir}")
+
+    if not strand_dir.exists():
+        raise FileNotFoundError(f"Strandedness directory not found: {strand_dir}")
+
+    quantification_directories: list[Path] = sorted([p for p in quant_dir.glob("*") if not p.name.startswith(".")])
     strandedness_directories: list[Path] = sorted([p for p in strand_dir.glob("*") if not p.name.startswith(".")])
 
-    if len(gene_counts_directories) != len(strandedness_directories):
+    if len(quantification_directories) != len(strandedness_directories):
         _log_and_raise_error(
             (
-                f"Unequal number of gene count directories and strandedness directories. "
-                f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."
-                f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}"
+                f"Unequal number of quantification directories and strandedness directories. "
+                f"Found {len(quantification_directories)} quantification directories and "
+                f"{len(strandedness_directories)} strandedness directories."
+                f"\nQuantification directory: {quant_dir}\nStrandedness directory: {strand_dir}"
             ),
             error=ValueError,
             level=LogLevel.ERROR,
@@ -218,49 +227,34 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
 
     # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information
     study_metrics: list[_StudyMetrics] = []
-    for gene_dir, strand_dir in zip(gene_counts_directories, strandedness_directories, strict=True):
-        count_files = list(gene_dir.glob("*.tab"))
+    for quant, strand_dir in zip(quantification_directories, strandedness_directories, strict=True):
+        quant_files = list(quant.glob("*_quant.genes.sf"))
         strand_files = list(strand_dir.glob("*.txt"))
-        if len(count_files) == 0:
-            _log_and_raise_error(f"No count files found for study '{gene_dir.stem}'.", error=ValueError, level=LogLevel.ERROR)
+        if len(quant_files) == 0:
+            _log_and_raise_error(f"No quant found for study '{quant.stem}'.", error=ValueError, level=LogLevel.ERROR)
         if len(strand_files) == 0:
             _log_and_raise_error(
-                f"No strandedness files found for study '{gene_dir.stem}'.",
+                f"No strandedness files found for study '{quant.stem}'.",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
 
         study_metrics.append(
             _StudyMetrics(
-                study_name=gene_dir.stem,
-                count_files=count_files,
+                study_name=quant.stem,
+                quant_files=quant_files,
                 strand_files=strand_files,
             )
         )
     return study_metrics
 
 
-async def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Path]):
+def _process_first_multirun_sample(strand_file: Path, all_quant_files: list[Path]):
     sample_count = pd.DataFrame()
-    all_star_information: list[_STARinformation] = await asyncio.gather(*[_STARinformation.build_from_tab(file) for file in all_counts_files])
-
-    for star_information in all_star_information:
-        strand_information = strand_file.read_text().rstrip("\n").lower()
-
-        if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
-            _log_and_raise_error(
-                (
-                    f"Unrecognized Strand Information: {strand_information}; "
-                    f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
-                ),
-                error=ValueError,
-                level=LogLevel.ERROR,
-            )
+    quant_information: list[_QuantInformation] = [_QuantInformation.build_from_sf(f) for f in all_quant_files]
 
-        if strand_information == "none":
-            strand_information = "unstranded_rna_counts"
-
-        run_counts = star_information.count_matrix[["ensembl_gene_id", strand_information]]
+    for info in quant_information:
+        run_counts = info.count_matrix[["ensembl_gene_id", info.sample_name]]
         run_counts.columns = ["ensembl_gene_id", "counts"]
         sample_count = run_counts if sample_count.empty else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer")
 
@@ -274,63 +268,48 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
     return count_sums
 
 
-async def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str):
-    star_information = await _STARinformation.build_from_tab(counts_file)
-    strand_information = strand_file.read_text().rstrip("\n").lower()
-
-    if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
-        _log_and_raise_error(
-            (
-                f"Unrecognized Strand Information: {strand_information}; "
-                f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
-            ),
-            error=ValueError,
-            level=LogLevel.ERROR,
-        )
 
-    if strand_information == "none":
-        strand_information = "unstranded_rna_counts"
+def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str):
+    quant_information = _QuantInformation.build_from_sf(counts_file)
+    return quant_information.count_matrix
 
-    sample_count = star_information.count_matrix[["ensembl_gene_id", strand_information]]
-    sample_count.columns = ["ensembl_gene_id", sample_name]
-    return sample_count
 
-
-async def _prepare_sample_counts(
+def _prepare_sample_counts(
     sample_name: str,
     counts_file: Path,
     strand_file: Path,
-    all_counts_files: list[Path],
-) -> pd.DataFrame | Literal["SKIP"]:
+    all_quant_files: list[Path],
+) -> pd.DataFrame | None:
     # Test if the counts_file is the first run in a multi-run smaple
     if re.search(r"R\d+r1", counts_file.as_posix()):
-        return await _process_first_multirun_sample(strand_file=strand_file, all_counts_files=all_counts_files)
-    elif re.search(r"R\d+r\d+", counts_file.as_posix()):
-        return "SKIP"
+        return _process_first_multirun_sample(strand_file=strand_file, all_quant_files=all_quant_files)
+    elif re.search(r"R\d+r[2-9]+", counts_file.as_posix()):
+        return None
     else:
-        return await _process_standard_replicate(counts_file, strand_file, sample_name)
+        return _process_standard_replicate(counts_file, strand_file, sample_name)
 
 
-async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame:
+def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame:
     adjusted_index = 0
-    counts: pd.DataFrame | Literal["SKIP"] = await _prepare_sample_counts(
+    counts: pd.DataFrame | None = _prepare_sample_counts(
         sample_name=metrics.sample_names[0],
-        counts_file=metrics.count_files[0],
+        counts_file=metrics.quant_files[0],
         strand_file=metrics.strand_files[0],
-        all_counts_files=metrics.count_files,
+        all_quant_files=metrics.quant_files,
     )
 
     for i in range(1, metrics.num_samples):
-        new_counts = await _prepare_sample_counts(
+        new_counts = _prepare_sample_counts(
             sample_name=metrics.sample_names[i],
-            counts_file=metrics.count_files[i],
+            counts_file=metrics.quant_files[i],
             strand_file=metrics.strand_files[i],
-            all_counts_files=metrics.count_files,
+            all_quant_files=metrics.quant_files,
         )
-        if isinstance(new_counts, str) and new_counts == "SKIP":
+        if new_counts is None:
             adjusted_index += 1
             continue
 
+        assert isinstance(counts, pd.DataFrame)  # noqa: S101
         counts: pd.DataFrame = counts.merge(new_counts, on="ensembl_gene_id", how="outer")
         counts = counts.fillna(value=0)
 
@@ -340,14 +319,19 @@ async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame:
             old_col_name = counts.columns[i + 1 - adjusted_index]
             counts.rename(columns={old_col_name: new_sample_name}, inplace=True)
 
+    if counts is None:
+        raise ValueError(f"No valid counts were processed for study '{metrics.study_name}'")
+
     return counts
 
 
 async def _write_counts_matrix(
     *,
     config_df: pd.DataFrame,
+    fragment_lengths: pd.DataFrame,
     como_context_dir: Path,
     output_counts_matrix_filepath: Path,
+    output_fragment_lengths_filepath: Path,
     rna: RNAType,
 ) -> pd.DataFrame:
     """Create a counts matrix file by reading gene counts table(s).
@@ -362,16 +346,22 @@ async def _write_counts_matrix(
         A pandas DataFrame representing the final counts matrix.
     """
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
-    counts: list[pd.DataFrame] = await asyncio.gather(*[_create_sample_counts_matrix(metric) for metric in study_metrics])
-    rna_specific_sample_names = set(config_df.loc[config_df["library_prep"].str.lower() == rna.value.lower(), "sample_name"].tolist())
+    counts: list[pd.DataFrame] = [_create_sample_counts_matrix(metric) for metric in study_metrics]
+    rna_specific_sample_names = set(
+        config_df.loc[config_df["library_prep"].str.lower() == rna.value.lower(), "sample_name"].tolist()
+    )
 
     final_matrix: pd.DataFrame = functools.reduce(lambda left, right: pd.merge(left, right, on="ensembl_gene_id", how="outer"), counts)
     final_matrix.fillna(value=0, inplace=True)
-    final_matrix.iloc[:, 1:] = final_matrix.iloc[:, 1:].astype(np.uint64)
+    final_matrix.iloc[:, 1:] = final_matrix.iloc[:, 1:].astype(int)
     final_matrix = cast(pd.DataFrame, final_matrix[["ensembl_gene_id", *rna_specific_sample_names]])
 
     output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
+    output_fragment_lengths_filepath.parent.mkdir(parents=True, exist_ok=True)
+
     final_matrix.to_csv(output_counts_matrix_filepath, index=False)
+    fragment_lengths[rna_specific_sample_names].to_csv(output_fragment_lengths_filepath, index=True)
+
     logger.success(f"Wrote gene count matrix for '{rna.value}' RNA at '{output_counts_matrix_filepath}'")
     return final_matrix
 
@@ -383,9 +373,9 @@ async def _create_config_df(  # noqa: C901
     gene_count_dirname: str = "geneCounts",
     layout_dirname: str = "layouts",
     strandedness_dirname: str = "strandedness",
-    fragment_sizes_dirname: str = "fragmentSizes",
+    quantification_dir: str = "quantification",
     prep_method_dirname: str = "prepMethods",
-) -> pd.DataFrame:
+) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Create configuration sheet.
 
     The configuration file created is based on the gene counts matrix.
@@ -398,15 +388,17 @@ async def _create_config_df(  # noqa: C901
         gene_count_dirname: Name of the subdirectory containing gene count files.
         layout_dirname: Name of the subdirectory containing layout files.
         strandedness_dirname: Name of the subdirectory containing strandedness files.
-        fragment_sizes_dirname: Name of the subdirectory containing fragment size files.
+        quantification_dir: Name of the subdirectory containing Salmon's quantification files.
         prep_method_dirname: Name of the subdirectory containing library preparation method files.
 
     Returns:
-        A pandas DataFrame representing the configuration sheet.
+        [0]: A pandas DataFrame representing the configuration sheet.
+        [1]: Fragment lengths for downstream calculations
     """
     label_regex: Final = re.compile(r"(?P<study>S\d{1,3})(?P<rep>R\d{1,3})(?P<run>r\d{1,3})?")
-    gene_counts: list[Path] = list((como_context_dir / gene_count_dirname).rglob("*.tab"))
-    if not gene_counts:
+    quant_files: list[Path] = list((como_context_dir / quantification_dir).rglob("*.genes.sf"))
+    # gene_counts: list[Path] = list((como_context_dir / gene_count_dirname).rglob("*.tab"))
+    if not quant_files:
         _log_and_raise_error(
             f"No gene count files found in '{gene_count_dirname}'",
             error=FileNotFoundError,
@@ -416,7 +408,7 @@ async def _create_config_df(  # noqa: C901
     auxillary_directories = {
         "layout": como_context_dir / layout_dirname,
         "strand": como_context_dir / strandedness_dirname,
-        "fragment": como_context_dir / fragment_sizes_dirname,
+        "quantification": como_context_dir / quantification_dir,
         "prep": como_context_dir / prep_method_dirname,
     }
     aux_lookup: dict[str, dict[str, Path]] = {kind: {} for kind in auxillary_directories}
@@ -430,15 +422,15 @@ async def _create_config_df(  # noqa: C901
                     aux_lookup[kind][m.group(0)] = p
 
     rows: list[SampleConfiguration] = []
-    for gene_count_path in sorted(gene_counts):
-        m = label_regex.search(gene_count_path.as_posix())
-        if not m:
+    for quant_file in sorted(quant_files):
+        m = label_regex.search(quant_file.as_posix())
+        if m is None:
             _log_and_raise_error(
-                f"Filename '{gene_count_path.name}' does not match contextName_SXRYrZ.tab pattern",
+                f"Filename '{quant_file.name}' does not match contextName_SXRYrZ.tab pattern",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
-        label = m.group(0)
+        label = m.group()
         study_number = m["study"]
         rep_number = m["rep"]
         sample_id = f"{context_name}_{study_number}{rep_number}"
@@ -447,41 +439,50 @@ async def _create_config_df(  # noqa: C901
         strand_path = _require_one([aux_lookup["strand"].get(label)], "strand", label)
         prep_path = _require_one([aux_lookup["prep"].get(label)], "preparation", label)
 
-        layout, strand, prep = await asyncio.gather(
-            *[
-                _read_text(layout_path, default="UNKNOWN"),
-                _read_text(strand_path, default="UNKNOWN"),
-                _read_text(prep_path, default="total", lower=True),
-            ],
-        )
+        layout = layout_path.read_text().rstrip()
+        strand = strand_path.read_text().rstrip()
+        prep = prep_path.read_text().rstrip()
         if prep not in {"total", "mrna"}:
             _log_and_raise_error(
                 f"Prep method must be 'total' or 'mrna' (got '{prep}') for {label}",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
+        if layout == "":
+            _log_and_raise_error(
+                message=f"No layout file found for '{label}'.",
+                error=FileNotFoundError,
+                level=LogLevel.WARNING,
+            )
 
-        fragment_label = f"{context_name}_{label}_fragment_size.txt"
-        frag_paths = [p for p in aux_lookup["fragment"].values() if p.name == fragment_label]
-        if not frag_paths and prep.lower() != RNAType.TRNA.value.lower():
-            logger.warning(f"No fragment file for '{label}'; defaulting to 100 bp (needed for zFPKM).")
-            mean_frag = 100.0
-        elif len(frag_paths) == 1 and layout == "single-end":
-            mean_frag = 0.0
-        else:  # 1-N files, paired end
-            dfs: list[pd.DataFrame] = cast(
-                typ=list[pd.DataFrame],
-                val=await asyncio.gather(*[_read_file(f, sep="\t", on_bad_lines="skip") for f in frag_paths]),
+        quant_paths = [p for p in aux_lookup["quantification"].values() if p.name == f"{sample_id}_quant.genes.sf"]
+        if (
+            not quant_paths
+            and layout in ["paired-end", "", None]
+            and prep.lower() in [RNAType.TRNA.value.lower(), RNAType.MRNA.value.lower()]
+        ):
+            _log_and_raise_error(
+                message=f"No quantification file found for '{label}'; defaulting to 100 bp (needed for zFPKM).",
+                error=FileNotFoundError,
+                level=LogLevel.WARNING,
             )
-            for df in dfs:
-                df["meanxcount"] = df["frag_mean"] * df["frag_count"]
-                counts = np.array([df["frag_count"].sum() for df in dfs])
-                means = np.array([(df["meanxcount"] / df["frag_count"].sum()).sum() for df in dfs])
-                mean_frag = float(np.average(means, weights=counts))
+        elif len(quant_paths) == 1 and layout == "single-end":
+            effective_len = pd.DataFrame({"Name": [], "EffectiveLength": []})
+            mean_effective_len = 0.0  # cannot compute FPKM for single-ended data
+        else:
+            df = _read_file(quant_file)
+            df.columns = [c.lower() for c in df.columns]
+            df = df.rename(columns={"effectivelength": "effective_length"})
+
+            effective_len = df[["name", "effective_length"]]
+            effective_len["effective_length"] = effective_len["effective_length"].astype(np.float64)
+            mean_effective_len: float = effective_len["effective_length"].sum() / len(df)
+
         rows.append(
             SampleConfiguration(
                 sample_name=sample_id,
-                fragment_length=mean_frag,
+                effective_lengths=effective_len,
+                mean_effective_length=mean_effective_len,
                 layout=layout,
                 strand=strand,
                 study=study_number,
@@ -489,8 +490,7 @@ async def _create_config_df(  # noqa: C901
             )
         )
 
-    df = pd.DataFrame.from_records([asdict(r) for r in rows]).sort_values("sample_name", ignore_index=True)
-    return df
+    return SampleConfiguration.to_dataframe(rows)
 
     # 6-3-25: Intentionally left commented-out code to test its replacement
     # gene_counts_dir = como_context_dir / gene_count_dirname
@@ -735,14 +735,17 @@ async def _process_como_input(
     output_config_filepath: Path,
     como_context_dir: PATH_TYPE,
     output_counts_matrix_filepath: Path,
+    output_fragment_lengths_filepath: Path,
     rna: RNAType,
 ) -> None:
-    config_df = await _create_config_df(context_name, como_context_dir=como_context_dir)
+    config_df, fragment_lengths = _create_config_df(context_name, como_context_dir=como_context_dir)
 
     await _write_counts_matrix(
         config_df=config_df,
+        fragment_lengths=fragment_lengths,
         como_context_dir=como_context_dir,
         output_counts_matrix_filepath=output_counts_matrix_filepath,
+        output_fragment_lengths_filepath=output_fragment_lengths_filepath,
         rna=rna,
     )
     with pd.ExcelWriter(output_config_filepath) as writer:
@@ -756,6 +759,8 @@ async def _process(
     output_gene_info_filepath: Path,
     como_context_dir: Path | None,
     input_matrix_filepath: list[Path] | None,
+    output_trna_fragment_lengths_filepath: Path | None,
+    output_mrna_fragment_lengths_filepath: Path | None,
     output_trna_config_filepath: Path | None,
     output_mrna_config_filepath: Path | None,
     output_trna_matrix_filepath: Path | None,
@@ -764,29 +769,50 @@ async def _process(
     cache: bool,
     create_gene_info_only: bool,
 ):
-    rna_types: list[tuple[RNAType, Path, Path]] = []
-    if output_trna_config_filepath:
-        rna_types.append((RNAType.TRNA, output_trna_config_filepath, output_trna_matrix_filepath))
-    if output_mrna_config_filepath:
-        rna_types.append((RNAType.MRNA, output_mrna_config_filepath, output_mrna_matrix_filepath))
+    rna_types: list[tuple[RNAType, Path, Path, Path]] = []
+    if output_trna_config_filepath is not None and output_trna_fragment_lengths_filepath is not None:
+        rna_types.append(
+            (
+                RNAType.TRNA,
+                output_trna_config_filepath,
+                output_trna_matrix_filepath,
+                output_trna_fragment_lengths_filepath,
+            )
+        )
+    if output_mrna_config_filepath is not None and output_mrna_fragment_lengths_filepath is not None:
+        rna_types.append(
+            (
+                RNAType.MRNA,
+                output_mrna_config_filepath,
+                output_mrna_matrix_filepath,
+                output_mrna_fragment_lengths_filepath,
+            )
+        )
 
     # if provided, iterate through como-input specific directories
     if not create_gene_info_only:
-        tasks = []
-        for rna, output_config_filepath, output_matrix_filepath in rna_types:
-            tasks.append(
-                asyncio.create_task(
-                    _process_como_input(
-                        context_name=context_name,
-                        output_config_filepath=output_config_filepath,
-                        como_context_dir=como_context_dir,
-                        output_counts_matrix_filepath=output_matrix_filepath,
-                        rna=rna,
-                    )
-                )
+        if como_context_dir is None:
+            _log_and_raise_error(
+                message="como_context_dir must be provided if create_gene_info_only is False",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+        if output_trna_fragment_lengths_filepath is None:
+            _log_and_raise_error(
+                message="output_fragment_lengths_filepath must be provided if create_gene_info_only is False",
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
-        await asyncio.gather(*tasks)
+        for rna, out_config, out_matrix, out_frag_len in rna_types:
+            _process_como_input(
+                context_name=context_name,
+                output_config_filepath=out_config,
+                como_context_dir=como_context_dir,
+                output_counts_matrix_filepath=out_matrix,
+                output_fragment_lengths_filepath=out_frag_len,
+                rna=rna,
+            )
 
     # create the gene info filepath based on provided data
     input_files = []
@@ -811,6 +837,8 @@ async def rnaseq_preprocess(
     output_gene_info_filepath: Path,
     como_context_dir: Path | None = None,
     input_matrix_filepath: Path | list[Path] | None = None,
+    output_trna_fragment_lengths_filepath: Path | None = None,
+    output_mrna_fragment_lengths_filepath: Path | None = None,
     output_trna_metadata_filepath: Path | None = None,
     output_mrna_metadata_filepath: Path | None = None,
     output_trna_count_matrix_filepath: Path | None = None,
@@ -829,6 +857,8 @@ async def rnaseq_preprocess(
     :param context_name: The context/cell type being processed
     :param taxon: The NCBI taxonomy ID
     :param output_gene_info_filepath: Path to the output gene information CSV file
+    :param output_trna_fragment_lengths_filepath: Path to the output tRNA fragment lengths CSV file (if in "create" mode)
+    :param output_mrna_fragment_lengths_filepath: Path to the output mRNA fragment lengths CSV file (if in "create" mode)
     :param output_trna_metadata_filepath: Path to the output tRNA config file (if in "create" mode)
     :param output_mrna_metadata_filepath: Path to the output mRNA config file (if in "create" mode)
     :param output_trna_count_matrix_filepath: The path to write total RNA count matrices
@@ -860,9 +890,11 @@ async def rnaseq_preprocess(
         input_matrix_filepath=input_matrix_filepath,
         output_gene_info_filepath=output_gene_info_filepath,
         output_trna_config_filepath=output_trna_metadata_filepath,
-        output_mrna_config_filepath=output_mrna_metadata_filepath,
         output_trna_matrix_filepath=output_trna_count_matrix_filepath,
+        output_trna_fragment_lengths_filepath=output_trna_fragment_lengths_filepath,
+        output_mrna_config_filepath=output_mrna_metadata_filepath,
         output_mrna_matrix_filepath=output_mrna_count_matrix_filepath,
+        output_mrna_fragment_lengths_filepath=output_mrna_fragment_lengths_filepath,
         cache=cache,
         create_gene_info_only=create_gene_info_only,
     )

From d35006351f966852431827fd58e7bbd00f3221e4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:02:29 -0600
Subject: [PATCH 03/27] chore: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 38 ++++++++++++++++++++--------------
 1 file changed, 22 insertions(+), 16 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 6835423e..7fd9ac78 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -256,7 +256,9 @@ def _process_first_multirun_sample(strand_file: Path, all_quant_files: list[Path
     for info in quant_information:
         run_counts = info.count_matrix[["ensembl_gene_id", info.sample_name]]
         run_counts.columns = ["ensembl_gene_id", "counts"]
-        sample_count = run_counts if sample_count.empty else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer")
+        sample_count = (
+            run_counts if sample_count.empty else sample_count.join(run_counts, on=["ensembl_gene_id"], how="outer")
+        )
 
     # Set na values to 0
     sample_count = sample_count.fillna(value="0")
@@ -336,14 +338,13 @@ async def _write_counts_matrix(
 ) -> pd.DataFrame:
     """Create a counts matrix file by reading gene counts table(s).
 
-    Args:
-        config_df: Configuration DataFrame containing sample information.
-        como_context_dir: Path to the COMO_input directory containing gene count files.
-        output_counts_matrix_filepath: Path where the output counts matrix CSV will be saved.
-        rna: RNAType enum indicating whether to process 'trna' or 'mrna' samples.
-
-    Returns:
-        A pandas DataFrame representing the final counts matrix.
+    :param config_df: Configuration DataFrame containing sample information.
+    :param fragment_lengths: DataFrame containing effective lengths for each gene and sample, used for zFPKM normalization.
+    :param como_context_dir: Path to the COMO_input directory containing gene count files.
+    :param output_counts_matrix_filepath: Path where the output counts matrix CSV will be saved.
+    :param output_fragment_lengths_filepath: Path where the output fragment lengths CSV will be saved.
+    :param rna: RNAType enum indicating whether to process 'trna' or 'mrna' samples.
+    :return: A pandas DataFrame representing the final counts matrix.
     """
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
     counts: list[pd.DataFrame] = [_create_sample_counts_matrix(metric) for metric in study_metrics]
@@ -363,6 +364,7 @@ async def _write_counts_matrix(
     fragment_lengths[rna_specific_sample_names].to_csv(output_fragment_lengths_filepath, index=True)
 
     logger.success(f"Wrote gene count matrix for '{rna.value}' RNA at '{output_counts_matrix_filepath}'")
+
     return final_matrix
 
 
@@ -675,9 +677,9 @@ async def read_ensembl_gene_ids(file: Path) -> list[str]:
             return data["ensembl_gene_id"].tolist()
         try:
             conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
-        except json.JSONDecodeError:
+        except json.JSONDecodeError as e:
             _log_and_raise_error(
-                f"Got a JSON decode error for file '{counts_matrix_filepaths}'",
+                f"Got a JSON decode error for file '{counts_matrix_filepaths}' ({e})",
                 error=ValueError,
                 level=LogLevel.CRITICAL,
             )
@@ -724,7 +726,7 @@ async def read_ensembl_gene_ids(file: Path) -> list[str]:
     # we would set `entrez_gene_id` to int here as well, but not all ensembl ids are mapped to entrez ids,
     #   and as a result, there are still "-" values in the entrez id column that cannot be converted to an integer
 
-    gene_info: pd.DataFrame = cast(pd.DataFrame, gene_info.sort_values(by="ensembl_gene_id"))
+    gene_info = gene_info.sort_values(by="ensembl_gene_id")
     output_filepath.parent.mkdir(parents=True, exist_ok=True)
     gene_info.to_csv(output_filepath, index=False)
     logger.success(f"Gene Info file written at '{output_filepath}'")
@@ -733,7 +735,7 @@ async def read_ensembl_gene_ids(file: Path) -> list[str]:
 async def _process_como_input(
     context_name: str,
     output_config_filepath: Path,
-    como_context_dir: PATH_TYPE,
+    como_context_dir: Path,
     output_counts_matrix_filepath: Path,
     output_fragment_lengths_filepath: Path,
     rna: RNAType,
@@ -845,7 +847,7 @@ async def rnaseq_preprocess(
     output_mrna_count_matrix_filepath: Path | None = None,
     cache: bool = True,
     log_level: LogLevel | str = LogLevel.INFO,
-    log_location: str | TextIOWrapper = sys.stderr,
+    log_location: str | io.TextIOWrapper = sys.stderr,
     *,
     create_gene_info_only: bool = False,
 ) -> None:
@@ -880,8 +882,12 @@ async def rnaseq_preprocess(
     input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None
     output_trna_metadata_filepath = output_trna_metadata_filepath.resolve() if output_trna_metadata_filepath else None
     output_mrna_metadata_filepath = output_mrna_metadata_filepath.resolve() if output_mrna_metadata_filepath else None
-    output_trna_count_matrix_filepath = output_trna_count_matrix_filepath.resolve() if output_trna_count_matrix_filepath else None
-    output_mrna_count_matrix_filepath = output_mrna_count_matrix_filepath.resolve() if output_mrna_count_matrix_filepath else None
+    output_trna_count_matrix_filepath = (
+        output_trna_count_matrix_filepath.resolve() if output_trna_count_matrix_filepath else None
+    )
+    output_mrna_count_matrix_filepath = (
+        output_mrna_count_matrix_filepath.resolve() if output_mrna_count_matrix_filepath else None
+    )
 
     await _process(
         context_name=context_name,

From 748225015dbeb73ad29fb3c79d4d9ac23f0a7cfd Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:02:45 -0600
Subject: [PATCH 04/27] chore: fill with integers for faster processing

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 7fd9ac78..eebe1a78 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -261,14 +261,13 @@ def _process_first_multirun_sample(strand_file: Path, all_quant_files: list[Path
         )
 
     # Set na values to 0
-    sample_count = sample_count.fillna(value="0")
+    sample_count = sample_count.fillna(value=0)
     sample_count["counts"] = sample_count["counts"].astype(float)
 
-    count_sums = sample_count.groupby("ensembl_gene_id", as_index=False)["counts"].mean()
-    count_sums["counts"] = np.ceil(count_sums["counts"].astype(np.uint32))
-    count_sums.columns = ["ensembl_gene_id", _sample_name_from_filepath(strand_file)]
-    return count_sums
-
+    count_avg = sample_count.groupby("ensembl_gene_id", as_index=False)["counts"].mean()
+    count_avg["counts"] = np.ceil(count_avg["counts"].astype(int))
+    count_avg.columns = ["ensembl_gene_id", _sample_name_from_filepath(strand_file)]
+    return count_avg
 
 
 def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str):

From 155c8221ccea3b4b5961151ab79a8ccaf5100d54 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:03:46 -0600
Subject: [PATCH 05/27] chore: remove unnecessary async function usage

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index eebe1a78..be0e714a 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -326,7 +326,7 @@ def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame:
     return counts
 
 
-async def _write_counts_matrix(
+def _write_counts_matrix(
     *,
     config_df: pd.DataFrame,
     fragment_lengths: pd.DataFrame,
@@ -367,7 +367,7 @@ async def _write_counts_matrix(
     return final_matrix
 
 
-async def _create_config_df(  # noqa: C901
+def _create_config_df(  # noqa: C901
     context_name: str,
     /,
     como_context_dir: Path,
@@ -670,7 +670,7 @@ async def _create_gene_info_file(
     """
 
     async def read_ensembl_gene_ids(file: Path) -> list[str]:
-        data = await _read_file(file, h5ad_as_df=False)
+        data = _read_file(file, h5ad_as_df=False)
         if isinstance(data, pd.DataFrame):
             data: pd.DataFrame
             return data["ensembl_gene_id"].tolist()
@@ -731,7 +731,7 @@ async def read_ensembl_gene_ids(file: Path) -> list[str]:
     logger.success(f"Gene Info file written at '{output_filepath}'")
 
 
-async def _process_como_input(
+def _process_como_input(
     context_name: str,
     output_config_filepath: Path,
     como_context_dir: Path,
@@ -741,7 +741,7 @@ async def _process_como_input(
 ) -> None:
     config_df, fragment_lengths = _create_config_df(context_name, como_context_dir=como_context_dir)
 
-    await _write_counts_matrix(
+    _write_counts_matrix(
         config_df=config_df,
         fragment_lengths=fragment_lengths,
         como_context_dir=como_context_dir,

From f7b3a0672325b9893282c71a6bfd3184012f8ba0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:04:03 -0600
Subject: [PATCH 06/27] fix: remove non existant genes from conversion

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index be0e714a..6defb860 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -684,6 +684,7 @@ async def read_ensembl_gene_ids(file: Path) -> list[str]:
             )
 
         # Remove NA values from entrez_gene_id dataframe column
+        conversion = conversion[~conversion["ensembl_gene_id"].isna()]
         return conversion["ensembl_gene_id"].tolist()
 
     logger.info("Fetching gene info - this can take up to 5 minutes depending on the number of genes and your internet connection")

From 0e4a2c3d9de8572c3d8bcb99a22aed4f60f58590 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:04:57 -0600
Subject: [PATCH 07/27] refactor: use more explicit (albeit longer) code to
 create gene_info dataframe object

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 65 ++++++++++++++++++++++------------
 1 file changed, 43 insertions(+), 22 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 6defb860..f9f1b6ce 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -351,7 +351,9 @@ def _write_counts_matrix(
         config_df.loc[config_df["library_prep"].str.lower() == rna.value.lower(), "sample_name"].tolist()
     )
 
-    final_matrix: pd.DataFrame = functools.reduce(lambda left, right: pd.merge(left, right, on="ensembl_gene_id", how="outer"), counts)
+    final_matrix: pd.DataFrame = functools.reduce(
+        lambda left, right: pd.merge(left, right, on="ensembl_gene_id", how="outer"), counts
+    )
     final_matrix.fillna(value=0, inplace=True)
     final_matrix.iloc[:, 1:] = final_matrix.iloc[:, 1:].astype(int)
     final_matrix = cast(pd.DataFrame, final_matrix[["ensembl_gene_id", *rna_specific_sample_names]])
@@ -687,42 +689,61 @@ async def read_ensembl_gene_ids(file: Path) -> list[str]:
         conversion = conversion[~conversion["ensembl_gene_id"].isna()]
         return conversion["ensembl_gene_id"].tolist()
 
-    logger.info("Fetching gene info - this can take up to 5 minutes depending on the number of genes and your internet connection")
+    logger.info(
+        "Fetching gene info - this can take up to 5 minutes depending on the number of genes and your internet connection"
+    )
 
-    ensembl_ids: set[str] = set(chain.from_iterable(await asyncio.gather(*[read_ensembl_gene_ids(f) for f in counts_matrix_filepaths])))
+    ensembl_ids: set[str] = set(
+        chain.from_iterable(await asyncio.gather(*[read_ensembl_gene_ids(f) for f in counts_matrix_filepaths]))
+    )
     gene_data: list[dict[str, str | int | list[str] | list[int] | None]] = await MyGene(cache=cache).query(
         items=list(ensembl_ids),
         taxon=taxon,
         scopes="ensemblgene",
     )
-    gene_info: pd.DataFrame = pd.DataFrame(
-        data=None,
-        columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "size"]),
-        index=pd.Index(data=list(range(len(ensembl_ids)))),
-    )
+
+    n = len(gene_data)
+    all_gene_symbols: list[str] = ["-"] * n
+    all_entrez_ids: list[str | int] = ["-"] * n
+    all_ensembl_ids: list[str] = ["-"] * n
+    all_sizes: list[int] = [-1] * n
+
+    def _avg_pos(value: int | list[int] | None) -> int:
+        if value is None:
+            return 0
+        if isinstance(value, list):
+            return int(sum(value) / len(value)) if value else 0
+        return int(value)
 
     for i, data in enumerate(gene_data):
         data: dict[str, str | int | list[str] | list[int] | None]
-        ensembl_genes: str | list[str] = cast(str | list[str], data.get("ensembl.gene", "-"))
-        start_pos: int | list[int] = cast(int | list[int], data.get("genomic_pos.start", 0))
-        end_pos: int | list[int] = cast(int | list[int], data.get("genomic_pos.end", 0))
 
-        avg_start: int | float = sum(start_pos) / len(start_pos) if isinstance(start_pos, list) else start_pos
-        avg_end: int | float = sum(end_pos) / len(end_pos) if isinstance(end_pos, list) else end_pos
-        size: int = int(avg_end - avg_start)
+        start = _avg_pos(data.get("genomic_pos.start", 0))
+        end = _avg_pos(data.get("genomic_pos.end", 0))
+        size = end - start
+
+        ensembl_id: int = data.get("ensembl.gene", "-")
+        all_ensembl_ids[i] = (
+            ",".join(map(str, ensembl_id)) if isinstance(ensembl_id, list) and ensembl_id else ensembl_id
+        )
+        all_gene_symbols[i] = str(data.get("symbol", "-"))
+        all_entrez_ids[i] = str(data.get("entrezgene", "-"))
+        all_sizes[i] = size if size > 0 else -1
 
-        gene_info.at[i, "gene_symbol"] = data.get("symbol", "-")
-        gene_info.at[i, "entrez_gene_id"] = data.get("entrezgene", "-")
-        gene_info.at[i, "ensembl_gene_id"] = ",".join(ensembl_genes) if isinstance(ensembl_genes, list) else ensembl_genes
-        gene_info.at[i, "size"] = size if size > 0 else -1
+    gene_info: pd.DataFrame = pd.DataFrame(
+        {
+            "ensembl_gene_id": all_ensembl_ids,
+            "gene_symbol": all_gene_symbols,
+            "entrez_gene_id": all_entrez_ids,
+            "size": all_sizes,
+        }
+    )
 
-    gene_info["size"] = gene_info["size"].astype(str)  # replace no-length values with "-" to match rows where every value is "-"
-    gene_info["size"] = gene_info["size"].replace("-1", "-")
-    gene_info = cast(pd.DataFrame, gene_info[~(gene_info == "-").all(axis=1)])  # remove rows where every value is "-"
+    # remove rows where every gene size value is -1 (not available)
+    gene_info = gene_info[~(gene_info == -1).all(axis=1)]
 
     gene_info["ensembl_gene_id"] = gene_info["ensembl_gene_id"].str.split(",")  # extend lists into multiple rows
     gene_info = gene_info.explode(column=["ensembl_gene_id"])
-    gene_info["size"] = gene_info["size"].astype(int)
     # we would set `entrez_gene_id` to int here as well, but not all ensembl ids are mapped to entrez ids,
     #   and as a result, there are still "-" values in the entrez id column that cannot be converted to an integer
 

From ab66599d1c280acaec9f56143721f7ab07fad738 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:05:20 -0600
Subject: [PATCH 08/27] chore: import required modules

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index a3e496fe..042e8145 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -1,34 +1,33 @@
 from __future__ import annotations
 
 import itertools
-import multiprocessing
 import sys
-import time
 from collections import namedtuple
 from collections.abc import Callable
-from concurrent.futures import Future, ProcessPoolExecutor, as_completed
 from dataclasses import dataclass, field
 from enum import Enum
 from pathlib import Path
 from typing import NamedTuple, TextIO, cast
 
-import matplotlib.pyplot as plt
+import anndata as ad
+import boolean
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
-import seaborn as sns
+import scanpy as sc
 import sklearn
 import sklearn.neighbors
+from anndata.compat import XDataArray
+from anndata.experimental.backed import Dataset2D
 from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
-from pandas import DataFrame
+from scipy import sparse
+from zfpkm import zFPKM, zfpkm_plot
 
-from como.data_types import FilteringTechnique, LogLevel, PeakIdentificationParameters, RNAType
-from como.density import density
+from como.data_types import FilteringTechnique, LogLevel, RNAType
 from como.migrations import gene_info_migrations
-from como.peak_finder import find_peaks
 from como.project import Config
-from como.utils import _log_and_raise_error, _num_columns, _read_file, _set_up_logging
+from como.utils import _log_and_raise_error, _read_file, _set_up_logging
 
 
 class _FilteringOptions(NamedTuple):

From 95654b342ac2683fb05a8e93ce7a9d8eabeaab2c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:07:20 -0600
Subject: [PATCH 09/27] refactor: optional argument for fragment data

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 042e8145..32a84fed 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -149,12 +149,13 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
 
 
 async def _build_matrix_results(
+    matrix: pd.DataFrame | sc.AnnData,
     *,
-    matrix: pd.DataFrame,
     gene_info: pd.DataFrame,
     metadata_df: pd.DataFrame,
+    fragment_df: pd.DataFrame | None,
     taxon: int,
-) -> _ReadMatrixResults:
+) -> tuple[NamedMetrics, list[int]]:
     """Read the counts matrix and returns the results.
 
     Arg:

From dec37b0c4f1973f17f4ddc08c07208e99882768a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:09:45 -0600
Subject: [PATCH 10/27] refactor: improve handling for single cell data

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 173 ++++++++++++++++++++++++++++------------
 1 file changed, 120 insertions(+), 53 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 32a84fed..e9cfc6e4 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -49,14 +49,14 @@ class LayoutMethod(Enum):
 class _StudyMetrics:
     study: str
     num_samples: int
-    count_matrix: pd.DataFrame
-    fragment_lengths: npt.NDArray[float]
+    count_matrix: pd.DataFrame | sc.AnnData
+    fragment_lengths: npt.NDArray[np.floating] | None
     sample_names: list[str]
     layout: list[LayoutMethod]
     entrez_gene_ids: npt.NDArray[int]
     gene_sizes: npt.NDArray[int]
     __normalization_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
-    __z_score_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
+    __z_score_matrix: pd.DataFrame | sc.AnnData | None = field(default=None)
     __high_confidence_entrez_gene_ids: list[str] = field(default_factory=list)
 
     def __post_init__(self):
@@ -77,11 +77,11 @@ def normalization_matrix(self, value: pd.DataFrame) -> None:
         self.__normalization_matrix = value
 
     @property
-    def z_score_matrix(self) -> pd.DataFrame:
+    def z_score_matrix(self) -> pd.DataFrame | sc.AnnData | None:
         return self.__z_score_matrix
 
     @z_score_matrix.setter
-    def z_score_matrix(self, value: pd.DataFrame) -> None:
+    def z_score_matrix(self, value: pd.DataFrame | sc.AnnData) -> None:
         self.__z_score_matrix = value
 
     @property
@@ -111,9 +111,10 @@ class _ReadMatrixResults(NamedTuple):
 
 
 def k_over_a(k: int, a: float) -> Callable[[npt.NDArray], bool]:
-    """Return a function that filters rows of an array based on the sum of elements being greater than or equal to A at least k times.
+    """Filter rows of an array based on the sum of elements being greater than or equal to A at least k times.
 
-    This code is based on the `kOverA` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/kOverA
+    This code is based on the `kOverA` function found in R's `genefilter` package
+    https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/kOverA
 
     :param k: The minimum number of times the sum of elements must be greater than or equal to A.
     :param a: The value to compare the sum of elements to.
@@ -121,7 +122,7 @@ def k_over_a(k: int, a: float) -> Callable[[npt.NDArray], bool]:
     """
 
     def filter_func(row: npt.NDArray) -> bool:
-        return np.sum(row >= a) >= k
+        return bool(np.sum(row >= a) >= k)
 
     return filter_func
 
@@ -168,20 +169,35 @@ async def _build_matrix_results(
     """
     conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
 
-    # If all columns are empty, it is indicative that the incorrect taxon id was provided
-    if all(conversion[col].eq("-").all() for col in conversion.columns):
-        logger.critical(f"Conversion of Ensembl Gene IDs to Entrez IDs and Gene Symbols was empty - is '{taxon}' the correct taxon ID for this data?")
-
-    # 2025-NOV-3: commented out `conversion` types to evaluate if it can be skipped
-    # conversion["ensembl_gene_id"] = conversion["ensembl_gene_id"].str.split(",")
-    # conversion = conversion.explode("ensembl_gene_id")
-    # conversion.reset_index(inplace=True, drop=True)
-    # conversion = conversion[conversion["entrez_gene_id"] != "-"]  # drop missing entrez IDs
-    # conversion["entrez_gene_id"] = conversion["entrez_gene_id"].astype(int)  # float32 is needed because np.nan is a float
-
-    # merge_on should contain at least one of "ensembl_gene_id", "entrez_gene_id", or "gene_symbol"
-    merge_on: list[str] = list(set(matrix.columns).intersection(conversion.columns))
-    if not merge_on:
+        matrix.var = matrix.var.reset_index(drop=False, names=["gene_symbol"])
+        conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=matrix.var["gene_symbol"].tolist(), taxon=taxon)
+    else:
+        if "ensembl_gene_id" not in matrix.columns:
+            _log_and_raise_error(
+                message="'ensembl_gene_id' column not found in the provided DataFrame.",
+                error=ValueError,
+                level=LogLevel.CRITICAL,
+            )
+        conversion: pd.DataFrame = await ensembl_to_gene_id_and_symbol(
+            ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon
+        )
+    # If the entrez gene id column is empty, it is indicative that the incorrect taxon id was provided
+    if conversion["entrez_gene_id"].eq("-").all():
+        logger.critical(
+            f"Conversion of Ensembl Gene IDs to Entrez IDs and Gene Symbols was empty - "
+            f"is '{taxon}' the correct taxon ID for this data?"
+        )
+    conversion["ensembl_gene_id"] = conversion["ensembl_gene_id"].str.split(",")
+    conversion = conversion.explode("ensembl_gene_id")
+    conversion = conversion[conversion["entrez_gene_id"] != "-"]
+    conversion["entrez_gene_id"] = conversion["entrez_gene_id"]
+    conversion = conversion.reset_index(drop=False)
+
+    # conversion_merge_on should contain at least one of "ensembl_gene_id", "entrez_gene_id", or "gene_symbol"
+    conversion_merge_on: list[str] = list(
+        set(matrix.columns if isinstance(matrix, pd.DataFrame) else matrix.var.columns) & set(conversion.columns)
+    )
+    if not conversion_merge_on:
         _log_and_raise_error(
             (
                 "No columns to merge on, unable to find at least one of `ensembl_gene_id`, `entrez_gene_id`, or `gene_symbol`. "
@@ -190,48 +206,91 @@ async def _build_matrix_results(
             error=ValueError,
             level=LogLevel.ERROR,
         )
-    if "entrez_gene_id" in matrix.columns:
-        matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
-    matrix = matrix.merge(conversion, on=merge_on, how="left")
 
-    # drop rows that have `0` in `entrez_gene_id` column
-    # matrix = matrix[matrix["entrez_gene_id"] != 0].reset_index(drop=True, inplace=False)
-    # gene_info = gene_info[gene_info["entrez_gene_id"] != 0].reset_index(drop=True, inplace=False)
+    if isinstance(matrix, pd.DataFrame):
+        if "entrez_gene_id" in matrix.columns:
+            matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
+        matrix = matrix.merge(conversion, on=conversion_merge_on, how="left")
+    elif isinstance(matrix, sc.AnnData):
+        if "entrez_gene_id" in matrix.var.columns:
+            matrix.var["entrez_gene_id"] = matrix.var["entrez_gene_id"].astype(int)
+        matrix.var = matrix.var.merge(conversion, on=conversion_merge_on, how="left")
 
     gene_info = gene_info_migrations(gene_info)
-    # gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int)
+    gene_info = gene_info[gene_info["entrez_gene_id"] != "-"]
+    gene_info.loc[:, "entrez_gene_id"] = gene_info.loc[:, "entrez_gene_id"].astype(int)
 
-    counts_matrix = matrix.merge(
-        gene_info[["entrez_gene_id", "ensembl_gene_id"]],
-        on=["entrez_gene_id", "ensembl_gene_id"],
-        how="inner",
+    gene_info_merge_on: list[str] = list(
+        set(matrix.columns if isinstance(matrix, pd.DataFrame) else matrix.var.columns) & set(gene_info.columns)
     )
 
-    gene_info = gene_info.merge(
-        counts_matrix[["entrez_gene_id", "ensembl_gene_id"]],
-        on=["entrez_gene_id", "ensembl_gene_id"],
-        how="inner",
-    )
+    if "entrez_gene_id" in gene_info_merge_on:
+        gene_info = gene_info[~gene_info["entrez_gene_id"].isna()]
+        gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int)
+
+        if isinstance(matrix, pd.DataFrame):
+            matrix = matrix[~matrix["entrez_gene_id"].isna()]
+            matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
+        elif isinstance(matrix, sc.AnnData):
+            if isinstance(matrix.var, XDataArray):
+                raise TypeError("Expected matrix.var object to be 'pd.DataFrame', got 'anndata.compat.XDataArray'")
+            matrix = matrix[:, ~matrix.var["entrez_gene_id"].isna()]
+            matrix.var["entrez_gene_id"] = matrix.var["entrez_gene_id"].astype(int)
+
+    if isinstance(matrix, pd.DataFrame):
+        matrix = matrix.merge(gene_info, on=gene_info_merge_on, how="inner")
+    elif isinstance(matrix, sc.AnnData):
+        if not isinstance(matrix.var, pd.DataFrame):
+            raise TypeError(f"Expected matrix.var object to be 'pd.DataFrame', got '{type(matrix.var)}'")
+        matrix.var["original_index"] = matrix.var.index
+        new_var = matrix.var.merge(gene_info, on=gene_info_merge_on, how="inner")
+        new_matrix = matrix[:, new_var["original_index"]].copy()
+        new_matrix.var = new_var
+        new_matrix.var = new_matrix.var.drop(columns=["original_index"])
+        new_matrix.var.reset_index(drop=True)
+        matrix = new_matrix
+
+        non_duplicates = ~matrix.var.duplicated(subset=matrix.var.columns, keep="first")
+        matrix = matrix[:, non_duplicates].copy()
 
-    entrez_gene_ids: npt.NDArray[int] = gene_info["entrez_gene_id"].to_numpy()
     metrics: NamedMetrics = {}
     for study in metadata_df["study"].unique():
-        study_sample_names = metadata_df[metadata_df["study"] == study]["sample_name"].tolist()
-        layouts = metadata_df[metadata_df["study"] == study]["layout"].tolist()
+        study_sample_names: list[str] = metadata_df[metadata_df["study"] == study]["sample_name"].tolist()
+        layouts: list[str] = metadata_df[metadata_df["study"] == study]["layout"].tolist()
+
+        if isinstance(matrix, pd.DataFrame):
+            subset = matrix.set_index(keys=["entrez_gene_id"], drop=True)
+            subset = subset[subset.columns.intersection(study_sample_names)]
+            subset.index = subset.index.astype(int)
+            entrez_gene_ids = subset.index.to_numpy(copy=False)
+            gene_sizes = matrix["size"].to_numpy(dtype=int, copy=False)
+        elif isinstance(matrix, sc.AnnData):
+            # matrix.var = matrix.var.set_index(keys=["entrez_gene_id"], drop=True)
+            subset = matrix[matrix.obs_names.intersection(study_sample_names)]
+            entrez_gene_ids = subset.var["entrez_gene_id"].to_numpy(dtype=int)
+            gene_sizes = subset.var["size"].to_numpy(dtype=int)
+        else:
+            _log_and_raise_error(
+                message=f"Matrix must be a pandas DataFrame or scanpy AnnData object, got: '{type(matrix)}'.",
+                error=TypeError,
+                level=LogLevel.CRITICAL,
+            )
+
+        frag_lengths = None
+        if fragment_df is not None:
+            frag_lengths = fragment_df["effective_length"].to_numpy(dtype=np.float64)
         metrics[study] = _StudyMetrics(
-            count_matrix=cast(pd.DataFrame, counts_matrix[counts_matrix.columns.intersection(study_sample_names)]),
-            fragment_lengths=metadata_df[metadata_df["study"] == study]["fragment_length"].values.astype(float),
+            count_matrix=subset,
+            fragment_lengths=frag_lengths,
             sample_names=study_sample_names,
             layout=[LayoutMethod(layout) for layout in layouts],
             num_samples=len(study_sample_names),
             entrez_gene_ids=entrez_gene_ids,
-            gene_sizes=gene_info["size"].values.astype(int),
+            gene_sizes=gene_sizes,
             study=study,
         )
-        metrics[study].fragment_lengths[np.isnan(metrics[study].fragment_lengths)] = 0
-        metrics[study].count_matrix.index = pd.Index(entrez_gene_ids, name="entrez_gene_id")
 
-    return _ReadMatrixResults(metrics=metrics, entrez_gene_ids=gene_info["entrez_gene_id"].tolist())
+    return metrics, gene_info["entrez_gene_id"].astype(int).tolist()
 
 
 def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
@@ -243,13 +302,21 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
     Returns:
         A dictionary of study metrics with TPM calculated.
     """
-    for sample in metrics:
-        count_matrix = metrics[sample].count_matrix
-
-        gene_sizes = pd.Series(metrics[sample].gene_sizes, index=count_matrix.index)
-        adjusted_counts = count_matrix.add(1e-6)
+    for sample, metric in metrics.items():
+        if isinstance(metric.count_matrix, sc.AnnData):
+            adata = metric.count_matrix
+            gene_sizes = pd.Series(metric.gene_sizes, index=adata.var_names)
+            counts_df = pd.DataFrame(
+                data=np.asarray(adata.X.toarray() if sparse.issparse(adata.X) else adata.X),
+                index=adata.var_names,
+                columns=adata.obs_names,
+            )
+        else:
+            counts_df = metric.count_matrix
+            gene_sizes = pd.Series(metric.gene_sizes)
 
-        tpm_matrix = adjusted_counts.divide(gene_sizes, axis=0)  # (count + 1) / gene_length
+        adjusted_counts = counts_df.add(1e-6)
+        tpm_matrix = adjusted_counts.div(gene_sizes, axis=0)  # (count + 1) / gene_length
         tpm_matrix = tpm_matrix.div(tpm_matrix.sum(axis=0), axis=1)  # normalize by total
         tpm_matrix = tpm_matrix.mul(1e6)  # scale to per-million
         metrics[sample].normalization_matrix = tpm_matrix

From fc1d45f0cb30359979e5a621c8ab88c57b637c1c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:10:00 -0600
Subject: [PATCH 11/27] chore: generalize data type input

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index e9cfc6e4..3ba71f6d 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -53,8 +53,8 @@ class _StudyMetrics:
     fragment_lengths: npt.NDArray[np.floating] | None
     sample_names: list[str]
     layout: list[LayoutMethod]
-    entrez_gene_ids: npt.NDArray[int]
-    gene_sizes: npt.NDArray[int]
+    entrez_gene_ids: npt.NDArray[np.integer]
+    gene_sizes: npt.NDArray[np.integer]
     __normalization_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
     __z_score_matrix: pd.DataFrame | sc.AnnData | None = field(default=None)
     __high_confidence_entrez_gene_ids: list[str] = field(default_factory=list)

From e1505d153bff3254ef3d7e1a559a41da0ce72080 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:10:11 -0600
Subject: [PATCH 12/27] chore: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 3ba71f6d..c8c2e207 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -146,7 +146,11 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
             level=LogLevel.CRITICAL,
         )
 
-    return data.apply(filter_func, axis=1).values if isinstance(data, pd.DataFrame) else np.apply_along_axis(filter_func, axis=1, arr=data)
+    return (
+        data.apply(filter_func, axis=1).to_numpy()
+        if isinstance(data, pd.DataFrame)
+        else np.apply_along_axis(filter_func, axis=1, arr=data)
+    )
 
 
 async def _build_matrix_results(
@@ -159,15 +163,14 @@ async def _build_matrix_results(
 ) -> tuple[NamedMetrics, list[int]]:
     """Read the counts matrix and returns the results.
 
-    Arg:
-        matrix: The gene counts matrix to process
-        metadata_df: The configuration dataframe related to the current context
-        taxon: The NCBI Taxon ID
-
-    Returns:
-        A dataclass `ReadMatrixResults`
+    :param matrix: The gene counts matrix to process
+    :param metadata_df: The configuration dataframe related to the current context
+    :param taxon: The NCBI Taxon ID
+    :returns: A dataclass `ReadMatrixResults`
     """
-    conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
+    if isinstance(matrix, sc.AnnData):
+        if not isinstance(matrix.var, pd.DataFrame):
+            raise TypeError("AnnData.var is expected to be a pandas.DataFrame")
 
         matrix.var = matrix.var.reset_index(drop=False, names=["gene_symbol"])
         conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=matrix.var["gene_symbol"].tolist(), taxon=taxon)
@@ -325,7 +328,7 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
 
 
 def _calculate_fpkm(metrics: NamedMetrics, scale: float = 1e6) -> NamedMetrics:
-    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary.
+    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each i in the metrics dictionary.
 
     Args:
         metrics: A dictionary of study metrics to calculate FPKM for.

From 849ba2e64da91e05cbf7ffd5eb7aa13fb80a38fc Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:11:52 -0600
Subject: [PATCH 13/27] chore: simplify FPKM/RPKM calculations; properly
 compute per-gene FPKM scores

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 62 +++++++++++------------------------------
 1 file changed, 17 insertions(+), 45 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index c8c2e207..ce8de24b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -338,53 +338,25 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: float = 1e6) -> NamedMetrics:
         A dictionary of study metrics with FPKM calculated.
     """
     for study in metrics:
-        matrix_values = []
-
-        for sample in range(metrics[study].num_samples):
-            layout = metrics[study].layout[sample]
-            count_matrix: npt.NDArray[float] = metrics[study].count_matrix.iloc[:, sample].values
-            gene_lengths = (
-                metrics[study].fragment_lengths[sample].astype(float)
-                if layout == LayoutMethod.paired_end
-                else metrics[study].gene_sizes.astype(float)
+        matrix_values: dict[str, npt.NDArray[np.floating]] = {}
+        count_matrix = metrics[study].count_matrix
+        if not isinstance(count_matrix, pd.DataFrame):
+            _log_and_raise_error(
+                message="FPKM cannot be performed on scanpy.AnnData objects!",
+                error=TypeError,
+                level=LogLevel.CRITICAL,
             )
-            gene_lengths_kb = gene_lengths / 1000.0
-
-            match layout:
-                case LayoutMethod.paired_end:  # FPKM
-                    total_fragments = count_matrix.sum(axis=0)
-                    if total_fragments == 0:
-                        fragments_per_kilobase_million = np.nan
-                    else:
-                        counts_per_million = total_fragments / scale
-                        fragments_per_kilobase = count_matrix / gene_lengths_kb
-                        fragments_per_kilobase_million = fragments_per_kilobase / counts_per_million
-                    matrix_values.append(fragments_per_kilobase_million)
-                case LayoutMethod.single_end:  # RPKM
-                    reads_per_kilobase = count_matrix / gene_lengths_kb
-                    total_reads = count_matrix.sum(axis=0)
-                    counts_per_million = total_reads / scale
-                    reads_per_kilobase_million = reads_per_kilobase / counts_per_million
-                    matrix_values.append(reads_per_kilobase_million)
-                case _:
-                    _log_and_raise_error(
-                        (
-                            f"Invalid normalization method specified ''. "
-                            f"Must be one of '{LayoutMethod.paired_end.value}' or '{LayoutMethod.single_end.value}'."
-                        ),
-                        error=ValueError,
-                        level=LogLevel.ERROR,
-                    )
-
-        # Transpose is needed because values were appended as rows
-        fpkm_matrix = pd.DataFrame(matrix_values).T
-        fpkm_matrix.index = metrics[study].count_matrix.index
-        fpkm_matrix.columns = metrics[study].sample_names
-
-        fpkm_matrix = fpkm_matrix[~pd.isna(fpkm_matrix)]
-        metrics[study].normalization_matrix = fpkm_matrix
-        metrics[study].normalization_matrix.columns = metrics[study].count_matrix.columns
 
+        study_counts = count_matrix.to_numpy(dtype=int, copy=False)
+        for i in range(metrics[study].num_samples):
+            layout = metrics[study].layout[i]
+            sample_name = metrics[study].sample_names[i]
+            length = metrics[study].fragment_lengths if layout == LayoutMethod.paired_end else metrics[study].gene_sizes
+            counts = study_counts[:, i]
+            mapped_reads = counts.sum()
+            matrix_values[sample_name] = ((counts * 1e9) / (length * mapped_reads)).astype(int)
+
+        metrics[study].normalization_matrix = pd.DataFrame(matrix_values, index=metrics[study].entrez_gene_ids)
     return metrics
 
 

From 3234413e2bdd7361e4b10e560d712f6874a18793 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:12:28 -0600
Subject: [PATCH 14/27] refactor: move zfpkm calculation to external package

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 169 +---------------------------------------
 1 file changed, 3 insertions(+), 166 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index ce8de24b..7f12b057 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -360,171 +360,6 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: float = 1e6) -> NamedMetrics:
     return metrics
 
 
-def _zfpkm_calculation(col_fpkm: pd.Series, min_peak_height: float, min_peak_distance: int):
-    """ZFPKM Transformations.
-
-    This function reproduces R's `zFPKM::zFPKM` function.
-
-    References:
-        1) zFPKM implementation in R: https://github.com/ronammar/zFPKM
-        2) zFPKM publication: https://doi.org/10.1186/1471-2164-14-778
-
-    Args:
-        col_fpkm: The raw FPKM values to perform zFPKM on
-        min_peak_distance: Minimum distance between peaks; passed on to `find_peaks` function
-        min_peak_height: Minimum height of peaks; passed on to `find_peaks` function
-
-    Returns:
-        A named tuple containing the zFPKM values, density estimate, mean (mu), standard deviation, and maximum FPKM value.
-    """
-    # Ignore np.log2(0) errors; we know this will happen, and are removing non-finite values in the density calculation
-    # This is required in order to match R's zFPKM calculations, as R's `density` function removes NA values.
-    with np.errstate(divide="ignore", invalid="ignore"):
-        log2fpkm: npt.NDArray[float] = np.log2(col_fpkm.values).astype(float)
-    d = density(log2fpkm)
-
-    peaks: pd.DataFrame = find_peaks(d.y_grid, min_peak_height=min_peak_height, min_peak_distance=min_peak_distance)
-    peak_positions = d.x_grid[peaks["peak_idx"].astype(int).tolist()]
-
-    sd = 1.0
-    mu = 0.0
-    fpkm_at_mu = 0.0
-    if peak_positions.size > 0:
-        mu = float(peak_positions.max())
-        u = float(log2fpkm[log2fpkm > mu].mean())
-        fpkm_at_mu = float(d.y_grid[int(peaks.loc[np.argmax(peak_positions), "peak_idx"])])
-        sd = float((u - mu) * np.sqrt(np.pi / 2))
-    zfpkm = pd.Series((log2fpkm - mu) / sd, dtype=float, name=col_fpkm.name, index=col_fpkm.index)
-    return _ZFPKMResult(zfpkm=zfpkm, density=Density(d.x_grid, d.y_grid), mu=mu, std_dev=sd, fpkm_at_mu=fpkm_at_mu)
-
-
-def zfpkm_transform(
-    fpkm_df: pd.DataFrame,
-    min_peak_height: float = 0.02,
-    min_peak_distance: int = 1,
-    update_every_percent: float = 0.1,
-    remove_na: bool = True,
-) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
-    """Perform zFPKM calculation/transformation.
-
-    Args:
-        fpkm_df: A DataFrame containing FPKM values with genes as rows and samples as columns.
-        min_peak_height: Minimum height of peaks; passed on to `find_peaks` function.
-        min_peak_distance: Minimum distance between peaks; passed on to `find_peaks` function.
-        update_every_percent: Frequency of progress updates as a decimal between 0 and 1 (e.g., 0.1 for every 10%).
-        remove_na: Whether to remove NaN & blank values from the input DataFrame before processing.
-
-    Returns:
-        A tuple containing:
-            - A dictionary of intermediate results for each sample.
-            - A DataFrame of zFPKM values with the same shape as the input fpkm_df.
-    """
-    if update_every_percent > 1:
-        logger.warning(f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - will convert to percentage")
-        update_every_percent /= 100
-
-    total_samples = _num_columns(fpkm_df)
-    update_per_step: int = int(np.ceil(total_samples * update_every_percent))
-
-    # Get at least 1 core and at most cpu_count() - 2
-    cores = max(min(multiprocessing.cpu_count() - 2, total_samples), 1)
-    logger.debug(f"zFPKM transforming {len(fpkm_df.columns)} sample(s) containing {len(fpkm_df):,} genes(s) using {cores} core(s)")
-    logger.debug(f"Will update every {update_per_step:,} steps (~{update_every_percent:.1%} of {total_samples:,})")
-
-    chunk_time = time.time()
-    start_time = time.time()
-    log_padding = len(str(f"{total_samples:,}"))
-    zfpkm_series: list[pd.Series] = []
-    results: dict[str, _ZFPKMResult] = {}
-
-    slim_fpkm_df: pd.DataFrame = cast(pd.DataFrame, fpkm_df[fpkm_df.index != "-"] if remove_na else fpkm_df)
-    with ProcessPoolExecutor(max_workers=cores) as pool:
-        futures: list[Future[_ZFPKMResult]] = [
-            pool.submit(
-                _zfpkm_calculation,
-                col_fpkm=fpkm_df[column],
-                min_peak_height=min_peak_height,
-                min_peak_distance=min_peak_distance,
-            )
-            for column in slim_fpkm_df
-        ]
-
-        for i, future in enumerate(as_completed(futures)):
-            result = future.result()
-            key = str(result.zfpkm.name)
-            results[key] = result
-            zfpkm_series.append(result.zfpkm)
-
-            if i != 0 and ((i + 1) % update_per_step == 0 or (i + 1) == total_samples):
-                current_time = time.time()
-                chunk = current_time - chunk_time
-                total_time = current_time - start_time
-                chunk_num = f"{i + 1:,}"
-                logger.debug(
-                    f"Processed {chunk_num:>{log_padding}} of {total_samples:,} - "
-                    f"chunk took {chunk:.1f} seconds - "
-                    f"running for {total_time:.1f} seconds"
-                )
-                chunk_time = current_time
-
-    zfpkm_df = pd.DataFrame({series.name: series for series in zfpkm_series}, index=fpkm_df.index)
-    return results, zfpkm_df
-
-
-def zfpkm_plot(results: dict[str, _ZFPKMResult], *, output_png_dirpath: Path, plot_xfloor: int = -4, subplot_titles: bool = True) -> None:
-    """Plot the log2(FPKM) density and fitted Gaussian for each sample.
-
-    Args:
-        results: A dictionary of intermediate results from zfpkm_transform.
-        output_png_dirpath: Output directory location
-        subplot_titles: Whether to display facet titles (sample names).
-        plot_xfloor: Lower limit for the x-axis.
-        subplot_titles: Whether to display facet titles (sample names).
-
-    """
-    to_concat: list[pd.DataFrame] = []
-    for name, result in results.items():
-        stddev: float = float(result.std_dev)
-        x: npt.NDArray[float] = result.density.x.flatten()
-        y: npt.NDArray[float] = result.density.y.flatten()
-
-        fitted: npt.NDArray[float] = np.exp(-0.5 * ((x - result.mu) / stddev) ** 2) / (stddev * np.sqrt(2 * np.pi))
-        fpkm_at_mu: float = result.fpkm_at_mu
-        max_fitted: float = float(fitted.max())
-        scale_fitted: float = fitted * fpkm_at_mu / max_fitted
-        to_concat.append(pd.DataFrame({"sample_name": name, "log2fpkm": x, "fpkm_density": y, "zfpkm_density": scale_fitted}))
-
-    mega_df = pd.concat(to_concat, ignore_index=True)
-    mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "zfpkm_density"])
-    mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
-
-    fig: plt.Figure
-    axes: list[plt.Axes]
-    fig, axes = plt.subplots(nrows=len(results), ncols=1, figsize=(8, 4 * len(results)))
-    if len(results) == 1:
-        axes = [axes]
-
-    for i, sample_name in enumerate(results):
-        sample_data = mega_df[mega_df["sample_name"] == sample_name]
-        axis = axes[i]
-
-        for source_type in sample_data["source"].unique():
-            group = sample_data[sample_data["source"] == source_type]
-            sns.lineplot(data=group, x="log2fpkm", y="density", label=source_type, ax=axis)
-
-        if subplot_titles:
-            axis.set_title(f"Sample: {sample_name}")
-        axis.set_xlim(plot_xfloor, sample_data["log2fpkm"].max())
-        axis.set_xlabel("log2(FPKM)")
-        axis.set_ylabel("density [scaled]")
-        axis.legend(title="Source")
-
-    output_png_dirpath.mkdir(parents=True, exist_ok=True)
-    sample_name: str = next(iter(results.keys()))[:-2]  # Go from 'control1hr_S1R1' to 'control1hr_S1'
-    plt.tight_layout()
-    plt.savefig(Path(output_png_dirpath, f"{sample_name}_zfpkm_density.png"))
-
-
 def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
     """Calculate the z-score for each sample in the metrics dictionary.
 
@@ -536,7 +371,9 @@ def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
     """
     for sample in metrics:
         log_matrix = np.log(metrics[sample].normalization_matrix)
-        z_matrix = pd.DataFrame(data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names)
+        z_matrix = pd.DataFrame(
+            data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names
+        )
         metrics[sample].z_score_matrix = z_matrix
     return metrics
 

From f90c38837478f288f6d8e4522345b9cd48ac9420 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:13:04 -0600
Subject: [PATCH 15/27] chore: use np.bool for boolean array

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 7f12b057..32be1a87 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -472,17 +472,19 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
         min_func = k_over_a(min_samples, 0.9)
         top_func = k_over_a(top_samples, 0.9)
 
-        min_genes: npt.NDArray[bool] = genefilter(boolean_expression, min_func)
-        top_genes: npt.NDArray[bool] = genefilter(boolean_expression, top_func)
+        min_genes: npt.NDArray[np.bool] = genefilter(boolean_expression, min_func)
+        top_genes: npt.NDArray[np.bool] = genefilter(boolean_expression, top_func)
 
         # Only keep `entrez_gene_ids` that pass `min_genes`
         metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes, strict=True) if keep]
-        metric.gene_sizes = np.array(gene for gene, keep in zip(gene_size, min_genes, strict=True) if keep)
+        metric.gene_sizes = np.asarray(gene for gene, keep in zip(gene_size, min_genes, strict=True) if keep)
         metric.count_matrix = cast(pd.DataFrame, metric.count_matrix.iloc[min_genes, :])
         metric.normalization_matrix = cast(pd.DataFrame, metrics[sample].normalization_matrix.iloc[min_genes, :])
 
         keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes, strict=True) if keep]
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes, strict=True) if keep]
+        metric.high_confidence_entrez_gene_ids = [
+            gene for gene, keep in zip(entrez_ids, keep_top_genes, strict=True) if keep
+        ]
 
     metrics = calculate_z_score(metrics)
 

From 8253a7d34d96645f89869d05c10d1120783b537f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:13:19 -0600
Subject: [PATCH 16/27] chore: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 32be1a87..8b10d480 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -466,8 +466,10 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
         top_samples = round(n_top * len(tpm_matrix.columns))
 
         tpm_quantile = tpm_matrix[tpm_matrix > 0]
-        quantile_cutoff = np.quantile(a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0)  # Compute quantile across columns
-        boolean_expression = pd.DataFrame(data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns).astype(int)
+        quantile_cutoff = np.quantile(a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0)
+        boolean_expression = pd.DataFrame(
+            data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns
+        ).astype(int)
 
         min_func = k_over_a(min_samples, 0.9)
         top_func = k_over_a(top_samples, 0.9)

From c52d2e845add7166cad1367b468b41badfd18830 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:13:47 -0600
Subject: [PATCH 17/27] feat: allow setting negative zFPKM results to 0

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 8b10d480..4b482a82 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -502,6 +502,7 @@ def zfpkm_filter(
     min_peak_height: float,
     min_peak_distance: int,
     output_png_dirpath: Path | None,
+    force_negative_to_zero: bool = False,
 ) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample.
 
@@ -513,6 +514,8 @@ def zfpkm_filter(
         min_peak_height: Minimum peak height for zFPKM peak identification.
         min_peak_distance: Minimum peak distance for zFPKM peak identification.
         output_png_dirpath: Optional directory path to save zFPKM plots.
+        force_negative_to_zero: Should negative values be forcibly set to 0?
+            This could happen as a result of normalization producing negative near-zero values (e.g., -0.001)
 
     Returns:
         A dictionary of filtered study metrics.

From e2e6350c30f9a432e56988a6b162d6576a80e108 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:14:29 -0600
Subject: [PATCH 18/27] feat: simplification to use external zfpkm package

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 30 +++++++++++++++++++-----------
 1 file changed, 19 insertions(+), 11 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 4b482a82..80e11df6 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -529,34 +529,38 @@ def zfpkm_filter(
         metric: _StudyMetrics
         # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead
         matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix
+        if not isinstance(matrix, pd.DataFrame):
+            raise TypeError(f"Expected a pandas.DataFrame for zFPKM filtering, got: '{type(matrix)}'")
 
         # TODO: 2025-OCT-31: Re-evaluate whether to remove rows with all 0 counts
         # matrix = matrix[matrix.sum(axis=1) > 0]  # remove rows (genes) that have no counts across all samples
 
-        results, zfpkm_df = zfpkm_transform(
-            fpkm_df=matrix,
-            min_peak_height=min_peak_height,
-            min_peak_distance=min_peak_distance,
-        )
-        zfpkm_df[(matrix == 0) | (zfpkm_df.isna())] = -4
+        matrix.replace(to_replace=np.nan, value=0.0, inplace=True)
+        if force_negative_to_zero:
+            matrix[matrix < 0] = 0.0
+
+        zfpkm_df, zfpkm_results = zFPKM(matrix)
 
-        if len(results) > 10 and not force_zfpkm_plot:
+        if len(zfpkm_results) > 25 and not force_zfpkm_plot:
             logger.warning(
-                "Not plotting zFPKM results because more than 10 plots would be created. "
+                "Not plotting zFPKM results because more than 25 plots would be created. "
                 "If you would like to plot them anyway, set 'force_zfpkm_plot' to True"
             )
         elif output_png_dirpath is None:
             logger.critical("Output zFPKM PNG filepath is None, set a path to plot zFPKM graphs")
         else:
-            zfpkm_plot(results, output_png_dirpath=output_png_dirpath)
+            sample_name = zfpkm_results[0].name.split("_")[0]  # go from 'control1hr_S1R1' to 'control1hr'
+            zfpkm_plot(zfpkm_results, save_filepath=output_png_dirpath / f"{sample_name}_zfpkm_density.png")
 
         metric.z_score_matrix = zfpkm_df
 
         # determine which genes are expressed
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))
         min_func = k_over_a(min_samples, cut_off)
-        min_genes: npt.NDArray[bool] = genefilter(zfpkm_df, min_func)
-        metric.entrez_gene_ids = [gene for gene, keep in zip(zfpkm_df.index, min_genes, strict=True) if keep]
+        min_genes: npt.NDArray[np.bool] = genefilter(zfpkm_df, min_func)
+        metric.entrez_gene_ids = np.asarray(
+            [g_id for g_id, keep in zip(zfpkm_df.index, min_genes, strict=True) if keep], dtype=int
+        )
 
         # determine which genes are confidently expressed
         top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns))
@@ -578,6 +582,7 @@ def filter_counts(
     zfpkm_min_peak_height: float,
     zfpkm_min_peak_distance: int,
     output_zfpkm_plot_dirpath: Path | None = None,
+    force_negative_to_zero: bool = False,
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique.
 
@@ -591,6 +596,8 @@ def filter_counts(
         zfpkm_min_peak_height: Minimum peak height for zFPKM peak identification.
         zfpkm_min_peak_distance: Minimum peak distance for zFPKM peak identification.
         output_zfpkm_plot_dirpath: Optional filepath to save the zFPKM plot.
+    :param force_negative_to_zero: Should negative values be forcibly set to 0?
+            This could happen as a result of normalization producing negative near-zero values (e.g., -0.001)
 
     Returns:
         A dictionary of filtered study metrics.
@@ -609,6 +616,7 @@ def filter_counts(
                 min_peak_height=zfpkm_min_peak_height,
                 min_peak_distance=zfpkm_min_peak_distance,
                 output_png_dirpath=output_zfpkm_plot_dirpath,
+                force_negative_to_zero=force_negative_to_zero,
             )
         case FilteringTechnique.UMI:
             # UMI filtering is the same as zFPKM filtering without calculating FPKM

From 2ad9887222b578e3ca5b0fd9720f380804c23a27 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:17:29 -0600
Subject: [PATCH 19/27] feat: allow providing the fragment size filepath (from
 rnaseq preprocessing)

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 109 ++++++++++++++++++++++++----------------
 1 file changed, 67 insertions(+), 42 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 80e11df6..104e68cb 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -586,16 +586,17 @@ def filter_counts(
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique.
 
-    Args:
-        context_name: The name of the context being processed.
-        metrics: A dictionary of study metrics to filter.
-        technique: The filtering technique to use.
-        filtering_options: Options for filtering the count matrix.
-        prep: The RNA preparation type.
-        force_zfpkm_plot: Whether to force plotting of zFPKM results even if there are many samples.
-        zfpkm_min_peak_height: Minimum peak height for zFPKM peak identification.
-        zfpkm_min_peak_distance: Minimum peak distance for zFPKM peak identification.
-        output_zfpkm_plot_dirpath: Optional filepath to save the zFPKM plot.
+    :param context_name: The name of the context being processed.
+    :param metrics: A dictionary of study metrics to filter.
+    :param technique: The filtering technique to use.
+    :param filtering_options: Options for filtering the count matrix.
+    :param prep: The RNA preparation type.
+    :param force_zfpkm_plot: Whether to force plotting of zFPKM results even if there are many samples.
+    :param zfpkm_min_peak_height: Minimum peak height for zFPKM peak identification.
+    :param zfpkm_min_peak_distance: Minimum peak distance for zFPKM peak identification.
+    :param umi_target_sum: The target sum for UMI normalization.
+    :param umi_perform_normalization: Whether to perform normalization before UMI filtering.
+    :param output_zfpkm_plot_dirpath: Optional filepath to save the zFPKM plot.
     :param force_negative_to_zero: Should negative values be forcibly set to 0?
             This could happen as a result of normalization producing negative near-zero values (e.g., -0.001)
 
@@ -642,6 +643,7 @@ async def _process(
     rnaseq_matrix_filepath: Path,
     metadata_df: pd.DataFrame,
     gene_info_df: pd.DataFrame,
+    fragment_df: pd.DataFrame | None,
     prep: RNAType,
     taxon: int,
     replicate_ratio: float,
@@ -656,18 +658,12 @@ async def _process(
     output_boolean_activity_filepath: Path,
     output_zscore_normalization_filepath: Path,
     output_zfpkm_plot_dirpath: Path | None,
+    force_negative_to_zero: bool,
 ):
     """Save the results of the RNA-Seq tests to a CSV file."""
     output_boolean_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
 
-    rnaseq_matrix: pd.DataFrame = await _read_file(rnaseq_matrix_filepath, h5ad_as_df=True)
-
-    if rnaseq_matrix_filepath.suffix == ".h5ad":
-        conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=rnaseq_matrix["gene_symbol"].tolist(), taxon=taxon)
-        conversion.reset_index(inplace=True)
-        rnaseq_matrix = rnaseq_matrix.merge(conversion, how="left", on="gene_symbol")
-        rnaseq_matrix.replace(to_replace=pd.NA, value="-")
-
+    rnaseq_matrix: pd.DataFrame | sc.AnnData = _read_file(rnaseq_matrix_filepath, h5ad_as_df=False)
     filtering_options = _FilteringOptions(
         replicate_ratio=replicate_ratio,
         batch_ratio=batch_ratio,
@@ -676,16 +672,14 @@ async def _process(
         high_batch_ratio=high_batch_ratio,
     )
 
-    read_counts_results: _ReadMatrixResults = await _build_matrix_results(
-        matrix=rnaseq_matrix,
+    metrics, entrez_gene_ids = await _build_matrix_results(
+        rnaseq_matrix,
         gene_info=gene_info_df,
         metadata_df=metadata_df,
+        fragment_df=fragment_df,
         taxon=taxon,
     )
-
-    metrics = read_counts_results.metrics
-
-    metrics: NamedMetrics = filter_counts(
+    metrics = filter_counts(
         context_name=context_name,
         metrics=metrics,
         technique=technique,
@@ -695,25 +689,41 @@ async def _process(
         zfpkm_min_peak_height=zfpkm_min_peak_height,
         zfpkm_min_peak_distance=zfpkm_min_peak_distance,
         output_zfpkm_plot_dirpath=output_zfpkm_plot_dirpath,
+        force_negative_to_zero=force_negative_to_zero,
     )
 
-    merged_zscore_df = pd.concat([m.z_score_matrix[m.z_score_matrix.index != "-"] for m in metrics.values()], axis="columns")
-    merged_zscore_df.fillna(-4, inplace=True)
-    expressed_genes: list[str] = list(itertools.chain.from_iterable(m.entrez_gene_ids for m in metrics.values()))
-    top_genes: list[str] = list(itertools.chain.from_iterable(m.high_confidence_entrez_gene_ids for m in metrics.values()))
+    if isinstance(rnaseq_matrix, pd.DataFrame):
+        merged_zscores = pd.concat(
+            [m.z_score_matrix[m.z_score_matrix.index != "-"] for m in metrics.values()], axis="columns"
+        )
 
-    # If any of the normalization metrics are not empty, write the normalized metrics to disk
-    if not all(metric.normalization_matrix.empty for metric in metrics.values()):
-        merged_zscore_df: pd.DataFrame = merged_zscore_df.reindex(columns=sorted(merged_zscore_df))
-        merged_zscore_df.to_csv(output_zscore_normalization_filepath, index=True)
-        logger.success(f"Wrote z-score normalization matrix to {output_zscore_normalization_filepath}")
-    else:
-        logger.warning(
-            "Not writing z-score normalization matrix because no normalization matrices exist. This is expected if you are using UMI filtering."
+        merged_zscores.index.name = (
+            "entrez_gene_id"
+            if merged_zscores.index.astype(str).str.isdigit().all()
+            else "ensembl_gene_id"
+            if merged_zscores.index.astype(str).str.startswith("ENS").all()
+            else "gene_symbol"
         )
 
+        merged_zscores = merged_zscores.reindex(columns=sorted(merged_zscores.columns))
+        merged_zscores = merged_zscores.groupby("entrez_gene_id").mean()
+        merged_zscores.to_csv(output_zscore_normalization_filepath, index=True)
+    elif isinstance(rnaseq_matrix, sc.AnnData):
+        merged_zscores = ad.concat([m.z_score_matrix for m in metrics.values()], axis="obs")
+        merged_zscores.var.index.name = "entrez_gene_id"
+        merged_zscores.obs = merged_zscores.obs.reindex(columns=sorted(merged_zscores.obs.columns))
+        merged_zscores.write_h5ad(output_zscore_normalization_filepath.with_suffix(".h5ad"))
+    expressed_genes: list[str] = list(itertools.chain.from_iterable(m.entrez_gene_ids for m in metrics.values()))
+    top_genes: list[str] = list(
+        itertools.chain.from_iterable(m.high_confidence_entrez_gene_ids for m in metrics.values())
+    )
+
+    logger.success(f"Wrote z-score normalization matrix to {output_zscore_normalization_filepath}")
+
     expression_frequency = pd.Series(expressed_genes).value_counts()
-    expression_df = pd.DataFrame({"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values})
+    expression_df = pd.DataFrame(
+        {"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values}
+    )
     expression_df["prop"] = expression_df["frequency"] / len(metrics)
     expression_df = expression_df[expression_df["prop"] >= filtering_options.batch_ratio]
 
@@ -722,10 +732,10 @@ async def _process(
     top_df["prop"] = top_df["frequency"] / len(metrics)
     top_df = top_df[top_df["prop"] >= filtering_options.high_batch_ratio]
 
-    entrez_id_series = pd.Series(read_counts_results.entrez_gene_ids)
+    entrez_id_series = pd.Series(entrez_gene_ids)
     boolean_matrix = pd.DataFrame(
         data={
-            "entrez_gene_id": read_counts_results.entrez_gene_ids,
+            "entrez_gene_id": entrez_gene_ids,
             "expressed": entrez_id_series.isin(expression_df["entrez_gene_id"]).astype(int),
             "high": entrez_id_series.isin(top_df["entrez_gene_id"]).astype(int),
         }
@@ -736,8 +746,13 @@ async def _process(
 
     # TODO: 2025-OCT-31: commented out dropping entrez ids, should this be kept?
     # boolean_matrix.dropna(subset="entrez_gene_id", inplace=True)
+    boolean_matrix = boolean_matrix.groupby("entrez_gene_id", as_index=False).mean()
+    boolean_matrix["expressed"] = boolean_matrix["expressed"].copy().astype(int)
+    boolean_matrix["high"] = boolean_matrix["high"].copy().astype(int)
     boolean_matrix.to_csv(output_boolean_activity_filepath, index=False)
-    logger.info(f"{context_name} - Found {expressed_count} expressed genes, {high_confidence_count} of which are confidently expressed")
+    logger.info(
+        f"{context_name} - Found {expressed_count} expressed genes, {high_confidence_count} of which are confidently expressed"
+    )
     logger.success(f"Wrote boolean matrix to {output_boolean_activity_filepath}")
 
 
@@ -757,11 +772,13 @@ async def rnaseq_gen(  # noqa: C901
     technique: FilteringTechnique | str = FilteringTechnique.ZFPKM,
     zfpkm_min_peak_height: float = 0.02,
     zfpkm_min_peak_distance: int = 1,
+    input_fragment_lengths: Path | None = None,
     cutoff: int | float | None = None,
     force_zfpkm_plot: bool = False,
     log_level: LogLevel = LogLevel.INFO,
     log_location: str | TextIO = sys.stderr,
     output_zfpkm_plot_dirpath: Path | None = None,
+    force_negative_counts_to_zero: bool = False,
 ) -> None:
     """Generate a list of active and high-confidence genes from a gene count matrix.
 
@@ -777,6 +794,7 @@ async def rnaseq_gen(  # noqa: C901
     :param prep: The preparation method
     :param taxon_id: The NCBI Taxon ID
     :param input_metadata_filepath_or_df: The filepath or dataframe containing metadata information
+    :param input_fragment_lengths: The filepath to the fragment lengths file, if applicable.
     :param replicate_ratio: The percentage of replicates that a gene must
         appear in for a gene to be marked as "active" in a batch/study
     :param batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active"
@@ -792,6 +810,9 @@ async def rnaseq_gen(  # noqa: C901
     :param log_level: The level of logging to output
     :param log_location: The location to write logs to
     :param output_zfpkm_plot_dirpath: Optional filepath to save zFPKM plots
+    :param force_negative_counts_to_zero: Should negative values be forcibly set to 0?
+        This could happen as a result of normalization producing negative near-zero values (e.g., -0.001)
+
     :return: None
     """
     _set_up_logging(level=log_level, location=log_location)
@@ -817,8 +838,10 @@ async def rnaseq_gen(  # noqa: C901
             elif cutoff:
                 cutoff = "default"
 
-        case FilteringTechnique.ZFPKM | FilteringTechnique.UMI:
+        case FilteringTechnique.ZFPKM:
             cutoff: int | float = cutoff or -3
+        case FilteringTechnique.UMI:
+            cutoff: int = cutoff or 1
         case _:
             _log_and_raise_error(
                 f"Technique must be one of {','.join(FilteringTechnique)}. Got: {technique.value}",
@@ -870,7 +893,8 @@ async def rnaseq_gen(  # noqa: C901
         context_name=context_name,
         rnaseq_matrix_filepath=input_rnaseq_filepath,
         metadata_df=metadata_df,
-        gene_info_df=await _read_file(input_gene_info_filepath),
+        gene_info_df=_read_file(input_gene_info_filepath),
+        fragment_df=_read_file(input_fragment_lengths),
         prep=prep,
         taxon=taxon_id,
         replicate_ratio=replicate_ratio,
@@ -885,4 +909,5 @@ async def rnaseq_gen(  # noqa: C901
         output_boolean_activity_filepath=output_boolean_activity_filepath,
         output_zscore_normalization_filepath=output_zscore_normalization_filepath,
         output_zfpkm_plot_dirpath=output_zfpkm_plot_dirpath,
+        force_negative_to_zero=force_negative_counts_to_zero,
     )

From 6af3990cd7236f3ebbe52f81ca1e96546cf4c2da Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:21:22 -0600
Subject: [PATCH 20/27] chore(ruff): reduce max line length

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py        | 2 ++
 main/como/rnaseq_preprocess.py | 2 --
 ruff.toml                      | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 104e68cb..4c522e3c 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -772,7 +772,9 @@ async def rnaseq_gen(  # noqa: C901
     technique: FilteringTechnique | str = FilteringTechnique.ZFPKM,
     zfpkm_min_peak_height: float = 0.02,
     zfpkm_min_peak_distance: int = 1,
+    umi_target_sum: int = 10_000,
     input_fragment_lengths: Path | None = None,
+    umi_perform_normalization: bool = False,
     cutoff: int | float | None = None,
     force_zfpkm_plot: bool = False,
     log_level: LogLevel = LogLevel.INFO,
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index f9f1b6ce..06f33922 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -16,8 +16,6 @@
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
-import pandera.pandas as pa
-import pandera.typing.pandas as pat
 from fast_bioservices.biothings.mygene import MyGene
 from fast_bioservices.pipeline import gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
diff --git a/ruff.toml b/ruff.toml
index a556c25f..b7ddbd88 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,4 +1,4 @@
-line-length = 150
+line-length = 120
 extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"]
 
 [format]

From 479fce2d064c044dc3ce4eb9eb74e51bf2d543e2 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:21:52 -0600
Subject: [PATCH 21/27] chore(ruff): mark unsorted imports as fixable

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 ruff.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ruff.toml b/ruff.toml
index b7ddbd88..691022d2 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -6,6 +6,7 @@ quote-style = "double"
 docstring-code-format = true
 
 [lint]
+extend-fixable = ["I001"]
 # Linting rules: https://docs.astral.sh/ruff/rules/
 unfixable = [
     "F401", # warn about, but do not remove, unused imports

From d83e974d597f57fd9ee8ffa004750a35fd0e3940 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:22:41 -0600
Subject: [PATCH 22/27] chore(uv): lock pyproject file

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 pyproject.toml |  17 +-
 uv.lock        | 413 ++++++++++---------------------------------------
 2 files changed, 86 insertions(+), 344 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 51f81319..488e14ef 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,11 +12,14 @@ dependencies = [
     "cobamp@git+https://github.com/JoshLoecker/cobamp@master",
     "cobra>=0.28.0",
     "fast-bioservices>=0.3.9",
+    "joypy>=0.2.6",
     "kaleido>=1.0.0",
     "loguru>=0.7.2",
+    "notebook>=7.4.7",
     "numpy>=2",
     "openpyxl>=3.1.5",
     "pandas>=1.3.5",
+    "python-louvain",
     "scanpy>=1.10.4",
     "scikit-learn>=1.5.2",
     "scipy>=1.13.0",
@@ -25,6 +28,7 @@ dependencies = [
     "statsmodels>=0.13.0; python_version < '3.12'",
     "statsmodels>=0.14.0; python_version >= '3.12'",
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
+    "zfpkm>=1.0.3",
 ]
 
 [project.optional-dependencies]
@@ -36,19 +40,11 @@ interactive = [
     "jupyterlab>=4.3.2"
 ]
 dev = [
-    "commitizen>=4.8.3",
-    "commitlint>=1.3.1",
     "como",
     "hatchling>=1.27.0",
-    "pandas-stubs>=2.3.2.250827",
-    "pre-commit>=4.2.0",
-    "pyright>=1.1.405",
-    "pytest>=8.4.1",
     "pytest-asyncio>=1.1.0",
     "pytest-cov>=6.2.1",
-    "ruff>=0.12.11",
-    "scipy-stubs>=1.16.1.1",
-    "types-aiofiles>=24.1.0.20250822",
+    "pytest>=8.4.1",
 ]
 
 [tool.hatch.version]
@@ -62,3 +58,6 @@ allow-direct-references = true
 
 [tool.pytest.ini_options]
 pythonpath = ["main/src"]
+
+[tool.uv.sources]
+python-louvain = { git = "https://github.com/taynaud/python-louvain" }
diff --git a/uv.lock b/uv.lock
index 4fcb4c61..a1a2c903 100644
--- a/uv.lock
+++ b/uv.lock
@@ -85,15 +85,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
 ]
 
-[[package]]
-name = "argcomplete"
-version = "3.6.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/16/0f/861e168fc813c56a78b35f3c30d91c6757d1fd185af1110f1aec784b35d0/argcomplete-3.6.2.tar.gz", hash = "sha256:d0519b1bc867f5f4f4713c41ad0aba73a4a5f007449716b16f385f2166dc6adf", size = 73403, upload-time = "2025-04-03T04:57:03.52Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/31/da/e42d7a9d8dd33fa775f467e4028a47936da2f01e4b0e561f9ba0d74cb0ca/argcomplete-3.6.2-py3-none-any.whl", hash = "sha256:65b3133a29ad53fb42c48cf5114752c7ab66c1c38544fdf6460f450c09b42591", size = 43708, upload-time = "2025-04-03T04:57:01.591Z" },
-]
-
 [[package]]
 name = "argon2-cffi"
 version = "25.1.0"
@@ -281,15 +272,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cb/0e/02ceeec9a7d6ee63bb596121c2c8e9b3a9e150936f4fbef6ca1943e6137c/cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91", size = 177780, upload-time = "2025-09-08T23:23:16.761Z" },
 ]
 
-[[package]]
-name = "cfgv"
-version = "3.4.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/11/74/539e56497d9bd1d484fd863dd69cbbfa653cd2aa27abfe35653494d85e94/cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560", size = 7114, upload-time = "2023-08-12T20:38:17.776Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c5/55/51844dd50c4fc7a33b653bfaba4c2456f06955289ca770a5dbd5fd267374/cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9", size = 7249, upload-time = "2023-08-12T20:38:16.269Z" },
-]
-
 [[package]]
 name = "charset-normalizer"
 version = "3.4.3"
@@ -403,38 +385,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" },
 ]
 
-[[package]]
-name = "commitizen"
-version = "4.9.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "argcomplete" },
-    { name = "charset-normalizer" },
-    { name = "colorama" },
-    { name = "decli" },
-    { name = "deprecated" },
-    { name = "jinja2" },
-    { name = "packaging" },
-    { name = "prompt-toolkit" },
-    { name = "pyyaml" },
-    { name = "questionary" },
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     { name = "cobra" },
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+    { name = "joypy" },
     { name = "kaleido" },
     { name = "loguru" },
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     { name = "openpyxl" },
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+    { name = "python-louvain" },
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@@ -456,22 +409,15 @@ dependencies = [
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+    { name = "zfpkm" },
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/f8/ef2baeaf2d15682d5d663c3f5165b63abad114fc0c4cd90b67b1ed0a6456/zfpkm-1.0.3-py3-none-any.whl", hash = "sha256:085007f97e75e50d686677ee28e3fceba5fc19958b35e9fbad3756ca2302a219", size = 17841, upload-time = "2025-11-10T16:47:44.805Z" },
+]

From 1df385c26f5985f41f96b43bfd83554914ec616d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:25:35 -0600
Subject: [PATCH 23/27] chore: remove zfpkm related code as it is now an
 external package

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/approx.py      | 333 ---------------------------------------
 main/como/density.py     | 328 --------------------------------------
 main/como/peak_finder.py | 191 ----------------------
 pyproject.toml           |   1 +
 4 files changed, 1 insertion(+), 852 deletions(-)
 delete mode 100644 main/como/approx.py
 delete mode 100644 main/como/density.py
 delete mode 100644 main/como/peak_finder.py

diff --git a/main/como/approx.py b/main/como/approx.py
deleted file mode 100644
index 441a489f..00000000
--- a/main/como/approx.py
+++ /dev/null
@@ -1,333 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Callable, Sequence
-from typing import Literal, NamedTuple
-
-import numpy as np
-import numpy.typing as npt
-
-
-class RegularizedArray(NamedTuple):
-    x: npt.NDArray[np.number]
-    y: npt.NDArray[np.number]
-    not_na: npt.NDArray[bool]
-    kept_na: bool
-
-
-class Approx(NamedTuple):
-    x: npt.NDArray[float]
-    y: npt.NDArray[float]
-
-
-def _coerce_to_float_array(a: Sequence[int | float | np.number]):
-    """Coerce input to a 1D float array.
-
-    Args:
-        a: the array to coerce
-
-    Returns:
-        A floating point 1D array.
-    """
-    arr = np.asarray(a, dtype=float)
-    if arr.ndim != 1:
-        arr = arr.ravel()
-    return arr
-
-
-def _regularize_values(
-    x: npt.NDArray[np.number],
-    y: npt.NDArray[np.number],
-    *,
-    na_rm: bool,
-    ties: Callable[[np.ndarray], float] | Literal["mean", "first", "last", "min", "max", "median", "sum"] = "mean",
-) -> RegularizedArray:
-    """Reimplementation of R's regularize.values() used by approx().
-
-      - Removes NA pairs if na_rm is True (else requires x to have no NA).
-      - Sorts by x (stable).
-      - Collapses duplicate x via `ties` aggregator.
-
-    Args:
-        x: the x values to regularize
-        y: ties: the corresponding y values
-        na_rm: should NA values be removed?
-        ties: how to handle duplicate x values; can be a string or a callable
-
-    Returns:
-        A NamedTuple with:
-          - x: sorted unique x
-          - y: aggregated y aligned with x
-          - not_na: boolean mask of y that are not NaN
-          - kept_na: True iff any NaN remained in y after regularization
-    """
-    if na_rm:
-        # Remove pairs where x or y is NA
-        keep = ~np.isnan(x) & ~np.isnan(y)
-        x = x[keep]
-        y = y[keep]
-        kept_na = False
-    else:
-        # R's C code errors if na_rm=False and any x is NA
-        if np.isnan(x).any():
-            raise ValueError("approx(x,y, .., na.rm=False): NA values in x are not allowed")
-        kept_na = np.isnan(y).any()
-
-    if x.size == 0:
-        return RegularizedArray(x=x, y=y, not_na=np.array([], dtype=bool), kept_na=kept_na)
-
-    # Use a stable sort (mergesort) to match R's order()
-    order = np.argsort(x, kind="mergesort")
-    x_sorted = x[order]
-    y_sorted = y[order]
-
-    # Handle the 'ties' argument
-    if callable(ties):
-        agg_fn = ties
-    else:
-        # fmt: off
-        if ties in ("mean", "avg", "average"):
-            agg_fn = np.mean
-        elif ties in ("first", "left"):
-            def agg_fn(a):
-                return a[0]
-        elif ties in ("last", "right"):
-            def agg_fn(a):
-                return a[-1]
-        elif ties == "min":
-            agg_fn = np.min
-        elif ties == "max":
-            agg_fn = np.max
-        elif ties == "median":
-            agg_fn = np.median
-        elif ties == "sum":
-            agg_fn = np.sum
-        else:
-            raise ValueError("Unsupported `ties`; use a callable or one of 'mean', 'first', 'last', 'min', 'max', 'median', 'sum'.")
-        # fmt: on
-
-    # Find unique x values and their indices/counts
-    unique_x, start_idx, counts = np.unique(x_sorted, return_index=True, return_counts=True)
-
-    # Aggregate y values for tied x values
-    y_agg = np.empty(unique_x.shape, dtype=float)
-    for k, (start, count) in enumerate(zip(start_idx, counts, strict=True)):
-        seg = y_sorted[start : start + count]
-        # Note: aggregators like np.mean will return NaN if `seg` contains NaN,
-        # matching R's default (mean(..., na.rm = FALSE)).
-        y_agg[k] = agg_fn(seg)
-
-    not_na = ~np.isnan(y_agg)
-    # Check if any NaNs remain in the *aggregated* y values
-    kept_na_final = bool(np.any(~not_na) if np.any(np.isnan(y_agg)) else False)
-    return RegularizedArray(x=unique_x, y=y_agg, not_na=not_na, kept_na=kept_na_final)
-
-
-def approx(
-    x: Sequence[float],
-    y: Sequence[float] | None = None,
-    xout: Sequence[float] | None = None,
-    method: str | int = "linear",
-    n: int = 50,
-    yleft: float | None = None,
-    yright: float | None = None,
-    rule: int | Sequence[int] | npt.NDArray[int] = 1,
-    f: float = 0.0,
-    ties: Callable[[np.ndarray], float] | Literal["mean", "first", "last", "min", "max", "median", "sum"] = "mean",
-    na_rm: bool = True,
-) -> Approx:
-    """Faithful Python port of R's `approx` function.
-
-    This implementation aims to replicate the behavior of R's `approx`
-    function, including its handling of `ties`, `rule`, `na_rm`, and
-    `method`.
-
-    Args:
-        x: Coordinates, or y-values if `y` is None.
-        y: y-coordinates. If None, `x` is treated as `y` and `x` becomes `1..n`.
-        xout: Points at which to interpolate.
-        method: Interpolation method. "linear" (1) or "constant" (2).
-        n: If `xout` is not provided, interpolation happens at `n` equally spaced points spanning the range of `x`.
-        yleft: Value to use for extrapolation to the left. Defaults to `NA` (np.nan) if `rule` is 1.
-        yright: Value to use for extrapolation to the right. Defaults to `NA` (np.nan) if `rule` is 1.
-        rule: Extrapolation rule.
-              - 1: Return `np.nan` for points outside the `x` range.
-              - 2: Use `yleft` and `yright` for points outside the range.
-        f: For `method="constant"`, determines the split point. `f=0` is left-step, `f=1` is right-step, `f=0.5` is midpoint.
-        ties: How to handle duplicate `x` values. Can be 'mean', 'first', 'last', 'min', 'max', 'median', 'sum', or a callable function.
-        na_rm: If True, `NA` pairs are removed before interpolation. If False, `NA`s in `x` cause an error, `NA`s in `y` are propagated.
-
-    Returns:
-        `Approx` class with:
-          - 'x': numpy array of xout used
-          - 'y': numpy array of interpolated values
-    """
-    # One-argument form: approx(y) -> x := 1..n, y := y
-    if y is None:
-        y_arr = _coerce_to_float_array(x)
-        x_arr = np.arange(1.0, y_arr.size + 1.0, dtype=float)
-    else:
-        x_arr = _coerce_to_float_array(x)
-        y_arr = _coerce_to_float_array(y)
-        if x_arr.size != y_arr.size:
-            raise ValueError("x and y must have same length")
-
-    # --- Method normalization ---
-    # (matches R's pmatch() result: 1=linear, 2=constant)
-    if isinstance(method, str):
-        m = method.strip().lower()
-        if m.startswith("lin"):
-            method_code = 1
-        elif m.startswith("const"):
-            method_code = 2
-        else:
-            raise ValueError("invalid interpolation method")
-    else:
-        if method in (1, 2):
-            method_code = int(method)
-        else:
-            raise ValueError("invalid interpolation method")
-
-    # --- Rule normalization ---
-    if isinstance(rule, Sequence | np.ndarray):
-        rule_list: list[int] = list(rule)  # type: ignore[invalid-argument-type]  # This is a valid argument type, not sure why ty is upset
-        if not (1 <= len(rule_list) <= 2):
-            raise ValueError("`rule` must have length 1 or 2")
-        if len(rule_list) == 1:
-            rule_list = [rule_list[0], rule_list[0]]
-    else:
-        rule_list = [rule, rule]
-
-    # Sort by x, collapse ties, and handle NAs like R's regularize.values()
-    r: RegularizedArray = _regularize_values(x_arr, y_arr, na_rm=na_rm, ties=ties)
-    x_reg: npt.NDArray[float] = r.x
-    y_reg: npt.NDArray[float] = r.y
-    not_na_mask: npt.NDArray[bool] = r.not_na
-    # no_na is True if we don't have to worry about NAs in y_reg
-    no_na: bool = na_rm or (not r.kept_na)
-    # nx is the number of *valid* (non-NA) points for interpolation
-    nx: int = x_reg.size if no_na else int(np.count_nonzero(not_na_mask))
-
-    if np.isnan(nx):
-        raise ValueError("invalid length(x)")
-
-    # R's C_ApproxTest logic
-    if nx <= 1:
-        if method_code == 1:
-            raise ValueError("need at least two non-NA values to interpolate")
-        if nx == 0:
-            raise ValueError("zero non-NA points")
-
-    # set extrapolation values (yleft/yright)
-    # This logic matches R's C code (R_approxtest)
-    if no_na:
-        y_first: float = y_reg[0]
-        y_last: float = y_reg[-1]
-    else:
-        # Find first and last non-NA y values
-        y_valid: npt.NDArray[float] = y_reg[not_na_mask]
-        y_first: float = y_valid[0]
-        y_last: float = y_valid[-1]
-
-    yleft_val: float = (np.nan if int(rule_list[0]) == 1 else y_first) if yleft is None else float(yleft)
-    yright_val: float = (np.nan if int(rule_list[1]) == 1 else y_last) if yright is None else float(yright)
-
-    # fabricate xout if it is missing
-    if xout is None:
-        if n <= 0:
-            raise ValueError("'approx' requires n >= 1")
-        if no_na:
-            x_first: float = x_reg[0]
-            x_last: float = x_reg[nx - 1]
-        else:
-            x_valid: npt.NDArray[float] = x_reg[not_na_mask]
-            x_first: float = x_valid[0]
-            x_last: float = x_valid[-1]
-        xout_arr: npt.NDArray[float] = np.linspace(x_first, x_last, num=int(n), dtype=float)
-    else:
-        xout_arr: npt.NDArray[float] = _coerce_to_float_array(xout)
-
-    # replicate R's C_ApproxTest checks
-    if method_code == 2 and (not np.isfinite(f) or f < 0.0 or f > 1.0):
-        raise ValueError("approx(): invalid f value; with `method=2`, `f` must be in the range [0,1] (inclusive) or NA")
-    if not no_na:
-        # R re-filters x and y here if NAs remained
-        x_reg: npt.NDArray[float] = x_reg[not_na_mask]
-        y_reg: npt.NDArray[float] = y_reg[not_na_mask]
-
-    # perform interpolation
-    # this section is a vectorized form of the logic from R's approx1 and R_approxfun
-    yout: npt.NDArray[float] = np.empty_like(xout_arr, dtype=float)
-    mask_nan_xout: npt.NDArray[bool] = np.isnan(xout_arr)
-    yout[mask_nan_xout] = np.nan
-    mask_valid: npt.NDArray[bool] = ~mask_nan_xout
-
-    if x_reg.size == 0:
-        # No valid points to interpolate against
-        yout[mask_valid] = np.nan
-        return Approx(x=xout_arr, y=yout)
-
-    xv: npt.NDArray[float] = xout_arr[mask_valid]
-    left_mask: npt.NDArray[bool] = xv < x_reg[0]
-    right_mask: npt.NDArray[bool] = xv > x_reg[-1]
-    mid_mask: npt.NDArray[bool] = ~(left_mask | right_mask)
-
-    res: npt.NDArray[float] = np.empty_like(xv, dtype=float)
-    res[left_mask] = yleft_val
-    res[right_mask] = yright_val
-
-    if np.any(mid_mask):
-        xm: npt.NDArray[float] = xv[mid_mask]
-
-        # Find indices
-        # j_right[k] = index of first x_reg > xm[k]
-        j_right: npt.NDArray[int] = np.searchsorted(x_reg, xm, side="right")
-        # j_left[k] = index of first x_reg >= xm[k]
-        j_left: npt.NDArray[int] = np.searchsorted(x_reg, xm, side="left")
-
-        # Points that exactly match an x_reg value
-        eq_mask: npt.NDArray[bool] = j_left != j_right
-        # Points that fall between x_reg values
-        in_interval_mask: npt.NDArray[bool] = ~eq_mask
-
-        res_mid: npt.NDArray[float] = np.empty_like(xm, dtype=float)
-
-        if np.any(eq_mask):
-            # For exact matches, use the corresponding y_reg value
-            # R's C code uses the 'j_left' index here
-            res_mid[eq_mask] = y_reg[j_left[eq_mask]]  # type: ignore[non-subscriptable]  # we know this is of type npt.NDArray[float], not sure why ty is upset
-
-        if np.any(in_interval_mask):
-            # R's C code sets i = j-1, where j is the 'right' index
-            j: npt.NDArray[float] = j_right[in_interval_mask]  # type: ignore[non-subscriptable]  # we know this is of type npt.NDArray[float], not sure why ty is upset
-            i: npt.NDArray[float] = j - 1
-
-            # Get the surrounding x and y values
-            xi: npt.NDArray[float] = x_reg[i]
-            xj: npt.NDArray[float] = x_reg[j]
-            yi: npt.NDArray[float] = y_reg[i]
-            yj: npt.NDArray[float] = y_reg[j]
-
-            if method_code == 1:  # linear
-                t: npt.NDArray[float] = (xm[in_interval_mask] - xi) / (xj - xi)
-                res_mid[in_interval_mask] = yi + (yj - yi) * t
-            else:  # constant
-                # This matches R_approxfun's constant logic
-                if f == 0.0:
-                    res_mid[in_interval_mask] = yi
-                elif f == 1.0:
-                    res_mid[in_interval_mask] = yj
-                else:
-                    # computes R's (1-f)*yi + f*yj
-                    f1: float = float(1.0 - f)
-                    f2: float = float(f)
-                    part: npt.NDArray[float] = np.zeros_like(yi, dtype=float)
-                    if f1 != 0.0:
-                        part: npt.NDArray[float] = part + yi * f1
-                    if f2 != 0.0:
-                        part: npt.NDArray[float] = part + yj * f2
-                    res_mid[in_interval_mask] = part
-
-        res[mid_mask] = res_mid
-
-    yout[mask_valid] = res
-    return Approx(x=xout_arr, y=yout)
diff --git a/main/como/density.py b/main/como/density.py
deleted file mode 100644
index 810ced26..00000000
--- a/main/como/density.py
+++ /dev/null
@@ -1,328 +0,0 @@
-from __future__ import annotations
-
-import sys
-from collections.abc import Sequence
-from typing import Literal, NamedTuple, cast
-
-import numpy as np
-import numpy.typing as npt
-
-from como.approx import Approx, approx
-
-
-class DensityResult(NamedTuple):
-    x: npt.NDArray[float]
-    y: npt.NDArray[float]
-    x_grid: npt.NDArray[float]
-    y_grid: npt.NDArray[float]
-    bw: float
-    n: int
-
-
-NUMBER = int | float | np.number
-
-
-def bin_distance(x: npt.NDArray[float], weights: npt.NDArray[float], lo: NUMBER, up: NUMBER, n: int) -> npt.NDArray[float]:
-    """Bin weighted distances."""
-    ixmin: int = 0
-    ixmax: int = n - 2
-    delta: float = (up - lo) / (n - 1)
-
-    y: npt.NDArray[float] = np.zeros((2 * n,), dtype=float)
-    for i in range(x.size):
-        i: int
-        xpos: float = (x[i] - lo) / delta
-        if xpos > sys.maxsize or xpos < -sys.maxsize:  # avoid integer overflows (taken from R's massdist.c)
-            continue
-        ix: int = np.floor(xpos).astype(int)
-        fx: float = xpos - ix
-        wi: float = weights[i]
-        if ixmin <= ix <= ixmax:
-            y[ix] += (1 - fx) * wi
-            y[ix + 1] += fx * wi
-        elif ix == -1:
-            y[0] += fx * wi
-        elif ix == ixmax + 1:
-            y[ix] += (1 - fx) * wi
-    return y
-
-
-def dnorm(x: float, mean: NUMBER = 0.0, sd: NUMBER = 1.0, log: bool = False, fast_dnorm: bool = False) -> float:
-    """Density function for the normal distribution.
-
-    This is a reproduction of R's `density` function.
-    Neither SciPy nor NumPy are capable of producing KDE values that align with R, and as a result,
-        a manual translation of R's KDE implementation was necessary.
-
-    References:
-        1) Documentation: https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/Normal
-        2) Source code (2025-OCT-30): https://github.com/wch/r-source/blob/3f7e2528990351bc4b0d1f1b237714668ab4c0c5/src/nmath/dnorm.c
-
-    Args:
-        x: Value at which to evaluate the density.
-        mean: Mean of the normal distribution.
-        sd: Standard deviation of the normal distribution.
-        log: If True, return the log density.
-        fast_dnorm: If True, use a faster but less accurate calculation for small `x`.
-
-
-    """
-    # Constants
-    m_ln2: float = 0.693147180559945309417232121458  # ln(2)
-    m_1_sqrt_2pi: float = 0.398942280401432677939946059934  # 1/sqrt(2pi)
-    m_ln_sqrt_2pi: float = 0.918938533204672741780329736406  # log(sqrt(2*pi))
-    r_d__0 = float(-np.inf) if log else 0.0  # R's R_D__0: (log_p ? ML_NEGINF : 0.)
-
-    # 11 bits exponent, where one bit is used to indicate special numbers (e.g. NaN and Infinity),
-    #   so the maximum exponent is 10 bits wide (2^10 == 1024).
-    # dbl_min_exp = -1022
-    dbl_min_exp = -1074
-
-    # The mantissa is 52 bits wide, but because numbers are normalized the initial binary 1 is represented
-    #   implicitly (the so-called "hidden bit"), which leaves us with the ability to represent 53 bits wide mantissa.
-    dbl_mant_dig = 53
-
-    mean: float = float(mean)
-    sd: float = float(sd)
-
-    if np.isnan(x) or np.isnan(mean) or np.isnan(sd):
-        return x + mean + sd
-    if sd < 0.0:
-        return float(np.nan)
-    if not np.isfinite(sd):
-        return r_d__0
-    if not np.isfinite(x) and x == mean:
-        return float(np.nan)
-    if sd == 0.0:
-        return float(np.inf) if x == mean else r_d__0
-
-    # From this point on, dividing by `sd` is safe because we know it is not 0
-    z = (x - mean) / sd
-    if (not np.isfinite(z)) and (x == mean):
-        return float(np.nan)
-
-    if not np.isfinite(z):
-        return r_d__0
-
-    a = np.fabs(z)
-    if a >= 2 * np.sqrt(np.finfo(float).max):
-        return r_d__0
-    if log:
-        return -(m_ln_sqrt_2pi + 0.5 * a * a + np.log(sd))
-    if a < 5 or fast_dnorm:  # for `x < 5`, this is more accurate but less fast
-        return m_1_sqrt_2pi * np.exp(-0.5 * a * a) / sd
-
-    # underflow boundary
-    boundary = np.sqrt(-2.0 * m_ln2 * (dbl_min_exp + 1 - dbl_mant_dig))
-    if a > boundary:
-        return 0.0
-
-    # Now, to get full accuracy, split x into two parts,
-    #   x = x1+x2, such that |x2| <= 2^-16.
-    #   Assuming that we are using IEEE doubles, that means that
-    #   x1*x1 is error free for x<1024 (but we have x < 38.6 anyway).
-    #   If we do not have IEEE this is still an improvement over the naive formula.
-    a1 = np.ldexp(np.rint(np.ldexp(a, 16)), -16)
-    a2 = a - a1
-    return (m_1_sqrt_2pi / sd) * (np.exp(-0.5 * a1 * a1) * np.exp((-a1 * a2) - (0.5 * a2 * a2)))
-
-
-def nrd0(x: npt.NDArray[float]) -> float:
-    """Calculate nrd0 from R source.
-
-    This bandwidth calculation matches R's
-
-    References:
-        1) Documentation: https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/bandwidth
-        2) Source code (as of 2025-OCT-30, copied below): https://github.com/wch/r-source/blob/trunk/src/library/stats/R/bandwidths.R
-            ```R
-            bw.nrd0 <- function (x)
-            {
-                if(length(x) < 2L) stop("need at least 2 data points")
-                hi <- sd(x)
-                if(!(lo <- min(hi, IQR(x)/1.34)))# qnorm(.75) - qnorm(.25) = 1.34898
-                    (lo <- hi) || (lo <- abs(x[1L])) || (lo <- 1.)
-                0.9 * lo * length(x)^(-0.2)
-            }
-            ```
-    """
-    if x.size < 2:
-        raise ValueError("need at least 2 data points")
-
-    hi = np.std(x, ddof=1)
-    q75, q25 = cast(tuple[float, float], cast(object, np.percentile(x, [75, 25])))
-    iqr_over_134 = (q75 - q25) / 1.34
-
-    # We are using a cascading series of checks on `lo` to  make sure it is a non-zero value
-    lo = min(hi, iqr_over_134)
-    if lo == 0:
-        if hi != 0:
-            lo = hi
-        elif abs(x[0]) != 0:
-            lo = abs(x[0])
-        else:
-            lo = 1.0
-
-    return float(0.9 * lo * x.size ** (-1 / 5))
-
-
-def density(
-    x: Sequence[NUMBER] | npt.NDArray[NUMBER],
-    bw: NUMBER | Literal["nrd0"] = "nrd0",
-    adjust: NUMBER = 1,
-    kernel: Literal["gaussian", "epanechnikov", "rectangular", "triangular", "biweight", "cosine", "optcosine"] = "gaussian",
-    weights: Sequence[NUMBER] | None = None,
-    n: int = 512,
-    from_: NUMBER | None = None,
-    to_: NUMBER | None = None,
-    cut: int = 3,
-    ext: int = 4,
-    remove_na: bool = False,
-    kernel_only: bool = False,
-) -> DensityResult:
-    """Compute kernel density estimates (KDE) using FFT method.
-
-    This is a reproduction of R's `density` function.
-    Neither SciPy nor NumPy are capable of producing KDE values that align with R, and as a result,
-        a manual translation of R's KDE implementation was necessary.
-
-    Args:
-        x: Input data points.
-        bw: Bandwidth for the kernel. If "nrd0", uses R's nrd0 method.
-        adjust: Adjustment factor for the bandwidth.
-        kernel: Kernel type to use.
-        weights: Optional weights for each data point.
-        n: Number of points in the output grid.
-        from_: Start of the grid (calculated automatically if not provided).
-        to_: End of the grid (calculated automatically if not provided).
-        cut: Number of bandwidths to extend the grid on each side.
-        ext: Number of bandwidths to extend the grid for FFT calculation.
-        remove_na: Whether to remove NA values from `x`.
-        kernel_only: If True, returns only the integral of the kernel function.
-
-    Returns:
-        DensityResult: A named tuple containing:
-            x: The x-coordinates of the density estimate.
-            y: The estimated density values at the x-coordinates.
-            x_grid: The grid of x-coordinates used for FFT calculation.
-            y_grid: The density values on the FFT grid.
-            bw: The bandwidth used.
-            n: The number of points in the output grid.
-    """
-    if not isinstance(x, Sequence | np.ndarray) or (len(x) < 2 and bw == "nrd0"):
-        raise ValueError("Need at at least two points to select a bandwidth automatically using 'nrd0'")
-    if kernel_only:
-        if kernel == "gaussian":
-            return 1 / (2 * np.sqrt(np.pi))
-        elif kernel == "epanechnikov":
-            return 3 / (5 * np.sqrt(5))
-        elif kernel == "rectangular":
-            return np.sqrt(3) / 6
-        elif kernel == "triangular":
-            return np.sqrt(6) / 9
-        elif kernel == "biweight":
-            return 5 * np.sqrt(7) / 49
-        elif kernel == "cosine":
-            return 3 / 4 * np.sqrt(1 / 3 - 2 / np.pi**2)
-        elif kernel == "optcosine":
-            return np.sqrt(1 - 8 / np.pi**2) * np.pi**2 / 16
-
-    if kernel != "gaussian":
-        raise NotImplementedError(f"Only 'gaussian' kernel is implemented; got '{kernel}'")
-
-    x: npt.NDArray[float] = np.asarray(x, dtype=float)
-
-    has_weights = weights is not None
-    weights: npt.NDArray[float] | None = np.asarray(weights, float) if weights is not None else None
-    if has_weights and (weights is not None and weights.size != x.size):
-        raise ValueError(f"The length of provided weights does not match the length of x: {weights.size} != {x.size}")
-
-    x_na: npt.NDArray[np.bool_] = np.isnan(x)
-    if np.any(x_na):
-        if remove_na:
-            x: npt.NDArray[float] = x[~x_na]
-            if has_weights and weights is not None:
-                true_d = weights.sum().astype(float) == 1
-                weights = weights[~x_na]
-                if true_d:
-                    weights = weights / weights.sum()
-        else:
-            raise ValueError("NA values found in 'x'. Set 'remove_na=True' to ignore them.")
-
-    nx = n
-    x_finite = np.isfinite(x)
-    if np.any(~x_finite):
-        x = x[x_finite]
-        nx = x.size
-    if not has_weights:
-        weights: npt.NDArray[float] = np.full(shape=nx, fill_value=1 / nx, dtype=float)
-        total_mass = nx / n
-    else:
-        weights = np.asarray(weights)
-        if not np.all(np.isfinite(weights)):
-            raise ValueError("Non-finite values found in 'weights'.")
-        if np.any(weights < 0):
-            raise ValueError("Negative values found in 'weights'.")
-        wsum: float = weights.sum()
-        if np.any(~x_finite):
-            weights = weights[x_finite]
-            total_mass = float(weights.sum() / wsum)
-        else:
-            total_mass = float(1)
-
-    n_user: int = n
-    n = max(n, 512)
-    if n > 512:  # round n up to the next power of 2 (i.e., 2^8=512, 2^9=1024)
-        n: int = int(2 ** np.ceil(np.log2(n)))
-
-    if isinstance(bw, str) and bw != "nrd0":
-        raise TypeError("Bandwidth 'bw' must be a number or 'nrd0'.")
-    elif isinstance(bw, str) and bw == "nrd0":
-        bw_calc = nrd0(x)
-    else:
-        bw_calc = float(bw)
-    if not np.isfinite(bw_calc):
-        raise ValueError("Calculated bandwidth 'bw' is not finite.")
-    bw_calc *= adjust
-
-    if bw_calc <= 0:
-        raise ValueError("Bandwidth 'bw' must be positive.")
-
-    # have to use `... if ... else` because `0` is falsey, resulting in the right-half being used instead of the user-provided value
-    from_ = float(from_ if from_ is not None else min(x) - cut * bw_calc)
-    to_ = float(to_ if to_ is not None else max(x) + cut * bw_calc)
-
-    if not np.isfinite(from_):
-        raise ValueError("'from_' is not finite.")
-    if not np.isfinite(to_):
-        raise ValueError("'to_' is not finite.")
-
-    lo = float(from_ - ext * bw_calc)
-    up = float(to_ + ext * bw_calc)
-
-    y: npt.NDArray[float] = bin_distance(x, weights, lo, up, n) * total_mass
-    kords: npt.NDArray[float] = np.linspace(start=0, stop=((2 * n - 1) / (n - 1) * (up - lo)), num=2 * n, dtype=float)
-    kords[n + 1 : 2 * n] = -kords[n:1:-1]  # mirror/negate tail: R's kords[n:2] will index from the reverse if `n`>2
-
-    # Initial diverge here (inside dnorm calculation)
-    kords: npt.NDArray[float] = np.asarray([dnorm(i, sd=bw_calc) for i in kords], dtype=float)
-
-    fft_y: npt.NDArray[np.complex128] = np.fft.fft(y)
-    conj_fft_kords: npt.NDArray[np.complex128] = np.conjugate(np.fft.fft(kords))
-    # Must multiply by `kords.size` because R does not produce a normalize inverse FFT, but NumPy normalizes by `1/size`
-    kords: npt.NDArray[np.complex128] = np.fft.ifft(fft_y * conj_fft_kords) * kords.size
-    kords: npt.NDArray[float] = (np.maximum(0, kords.real)[0:n]) / y.size  # for values of kords, get 0 or kords[i], whichever is larger
-    xords: npt.NDArray[float] = np.linspace(lo, up, num=n, dtype=float)
-
-    # xp=known x-coords, fp=known y-cords, x=unknown x-coords; returns interpolated (e.g., unknown) y-coords
-    interp_x: npt.NDArray[float] = np.linspace(from_, to_, num=n_user)
-    interp_y: Approx = approx(xords, kords, interp_x)
-
-    return DensityResult(
-        x=interp_x,
-        y=interp_y.y,
-        x_grid=xords,
-        y_grid=kords,
-        bw=bw_calc,
-        n=n,
-    )
diff --git a/main/como/peak_finder.py b/main/como/peak_finder.py
deleted file mode 100644
index 230db014..00000000
--- a/main/como/peak_finder.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import re
-from collections.abc import Sequence
-from typing import Literal, cast, overload
-
-import numpy as np
-import numpy.typing as npt
-import pandas as pd
-
-from como.utils import _num_rows
-
-
-def _validate_args(
-    x: npt.NDArray[np.number],
-    nups: int,
-    ndowns: int,
-    zero: Literal["0", "+", "-"],
-    min_peak_height: float,
-    min_peak_distance: int,
-    threshold: float,
-):
-    """Validate input arguments to `find_peaks` function.
-
-    Function created to reduce the complexity of `find_peaks` (ruff linting rule C901)
-    """
-    if x.ndim != 1:
-        raise ValueError(f"Expected a 1D array, got {x.ndim}D array instead.")
-    if np.any(np.isnan(x)):
-        raise ValueError("Input x contains NaNs")
-    if nups < 0:
-        raise ValueError("Argument 'nups' must be non-negative")
-    if ndowns < 0:
-        raise ValueError("Argument 'ndowns' must be non-negative")
-    if zero not in {"0", "+", "-"}:
-        raise ValueError("Argument 'zero' must be '0', '+', or '-'")
-    if min_peak_height < 0:
-        raise ValueError("Argument 'min_peak_height' must be non-negative")
-    if min_peak_distance < 0:
-        raise ValueError("Argument 'minpeakdistance' must be non-negative")
-    if threshold < 0:
-        raise ValueError("Argument 'threshold' must be non-negative")
-
-
-def _encode_signs(x: npt.NDArray[np.number], zero: str) -> str:
-    """Encode the signs of the differences in `x` into a string.
-
-    Function created to reduce the complexity of `find_peaks` (ruff linting rule C901)
-    """
-    diff_signs = np.sign(np.diff(x))
-    chars = ["+" if d > 0 else "-" if d < 0 else "0" for d in diff_signs]
-    x_chars = "".join(chars)
-    if zero != "0":
-        x_chars = x_chars.replace("0", zero)
-    return x_chars
-
-
-@overload
-def _enforce_minimum_peak_distance(df: pd.DataFrame, min_peak_distance: int, inplace: Literal[True] = True) -> None: ...
-
-
-@overload
-def _enforce_minimum_peak_distance(df: pd.DataFrame, min_peak_distance: int, inplace: Literal[False] = False) -> pd.DataFrame: ...
-
-
-def _enforce_minimum_peak_distance(df: pd.DataFrame, min_peak_distance: int, inplace: bool = True) -> None | pd.DataFrame:
-    """Enforces a minimum peak distance between identified peaks.
-
-    Function created to reduce the complexity of `find_peaks` (ruff linting rule C901)
-
-    Modifies `df` inplace
-    """
-    good_peaks = np.ones(_num_rows(df), dtype=bool)
-    for i in range(_num_rows(df)):
-        if not good_peaks[i]:
-            continue
-        dpos = np.abs(df.at[i, "peak_idx"] - df["peak_idx"].values)
-        close = (dpos > 0) & (dpos < min_peak_distance)
-        good_peaks[close] = False
-
-    if inplace:
-        df.drop(df.index[~good_peaks], inplace=True)
-        df.reset_index(drop=True, inplace=True)
-        return None
-    return cast(pd.DataFrame, df.iloc[good_peaks, :].reset_index(drop=True))
-
-
-def find_peaks(
-    x: Sequence[float] | Sequence[int] | npt.NDArray[np.number],
-    nups: int = 1,
-    ndowns: int | None = None,
-    zero: Literal["0", "+", "-"] = "0",
-    peak_pattern: str | None = None,
-    min_peak_height: float = 0.02,  # default for R's zFPKM `peak_parameters`
-    min_peak_distance: int = 1,  # default for R's zFPKM `peak_parameters`
-    threshold: float = 0.0,
-    npeaks: int = 0,
-    sortstr: bool = False,
-) -> pd.DataFrame:
-    """Identify peaks in a given time series.
-
-    This function is modelled after R's `pracma::findpeaks` function.
-    SciPy's `scipy.signal.find_peaks` provides different results than `pracma::findpeaks`,
-        resulting in the requirement for this translation.
-
-    References:
-        1) pracma::findpeaks: https://rdrr.io/cran/pracma/man/findpeaks.html
-
-    Args:
-        x: numerical vector taken as a time series (no NA values allowed)
-        nups: minimum number of increasing steps before a peak is reached
-        ndowns: minimum number of decreasing steps after the peak (defaults to the same value as `nups`)
-        zero: can be '+', '-', or '0'; how to interprete succeeding steps of the same value: increasing, decreasing, or special
-        peak_pattern: define a peak as a regular pattern, such as the default pattern `[+]{1,}[-]{1,}`
-            If a pattern is provided, parameters `nups` and `ndowns` are not taken into account
-        min_peak_height: the minimum (absolute) height a peak has to have before being recognized
-        min_peak_distance: the minimum distance (in indices) between peaks before they are counted
-        threshold: the minimum difference in height between a peak and its surrounding values
-        npeaks: the number of peaks to return (<=0 returns all)
-        sortstr: should the peaks be returned in decreasing order of their peak height?
-
-    Returns:
-        A dataframe with the columns:
-            height: the height of the peak
-            peak_idx: the index (from `x`) of the identified peak
-            start_idx: the starting index (from `x`) of the identified peak
-            end_idx: the ending index (from `x`) of the identified peak
-        If the dataframe is empty, no peaks could be identified
-    """
-    x = np.asarray(x, dtype=float) if not isinstance(x, np.ndarray) else x
-    npeaks: int = max(npeaks, 0)
-    ndowns: int = ndowns or nups
-    peak_pattern: str = peak_pattern or rf"[+]{{{nups},}}[-]{{{ndowns},}}"
-    _validate_args(
-        x=x,
-        nups=nups,
-        ndowns=ndowns,
-        zero=zero,
-        min_peak_height=min_peak_height,
-        min_peak_distance=min_peak_distance,
-        threshold=threshold,
-    )
-
-    # find peaks by regex matching the sign pattern
-    derivative_chars = _encode_signs(x=x, zero=zero)
-    matches: list[re.Match[str]] = list(re.finditer(peak_pattern, derivative_chars))
-    if not matches:
-        return pd.DataFrame(columns=["height", "peak_idx", "start_idx", "end_idx"])
-
-    pattern_start_index: npt.NDArray[int] = np.asarray([m.start() for m in matches], dtype=int)
-    pattern_end_index: npt.NDArray[int] = np.asarray([m.end() for m in matches], dtype=int)
-
-    num_matches: int = len(pattern_start_index)
-    peak_index: npt.NDArray[int] = np.zeros(num_matches, dtype=int)
-    peak_height: npt.NDArray[float] = np.zeros(num_matches, dtype=float)
-
-    # for each match region, find the local max
-    for i in range(num_matches):
-        segment: npt.NDArray[np.uint64] = x[pattern_start_index[i] : pattern_end_index[i] + 1]
-        segment_max_idx: np.uint64 = np.argmax(segment)
-        peak_index[i] = pattern_start_index[i] + segment_max_idx
-        peak_height[i] = segment[segment_max_idx]
-
-    # filter for values that are too low or below the threshold difference
-    x_left: float = x[pattern_start_index]
-    x_right: float = x[np.minimum(pattern_end_index, x.size - 1)]
-    valid_peaks: list[int] = list(np.where((peak_height >= min_peak_height) & ((peak_height - np.maximum(x_left, x_right)) >= threshold))[0].astype(int))  # noqa: E501  # fmt: skip
-
-    if len(valid_peaks) == 0:
-        return pd.DataFrame(columns=["height", "peak_idx", "start_idx", "end_idx"])
-
-    out_df: pd.DataFrame = pd.DataFrame(
-        data={
-            "height": peak_height[valid_peaks],
-            "peak_idx": peak_index[valid_peaks],
-            "start_idx": pattern_start_index[valid_peaks],
-            "end_idx": pattern_end_index[valid_peaks],
-        }
-    )
-
-    # sort by peak height in descending order (largest to smallest)
-    if sortstr or min_peak_distance > 1:
-        out_df.sort_values(by=["height"], ascending=False, ignore_index=True, inplace=True)
-
-    # if provided, enforce a minimum distance between the peaks (removes smaller peaks next to larger ones)
-    if min_peak_distance > 1 and len(out_df) > 1:
-        _enforce_minimum_peak_distance(df=out_df, min_peak_distance=min_peak_distance)
-
-    # if provided, limit the number of peaks returned
-    if 0 < npeaks < _num_rows(out_df):
-        out_df = cast(pd.DataFrame, out_df.iloc[:npeaks, :].reset_index(drop=True))
-
-    return out_df
diff --git a/pyproject.toml b/pyproject.toml
index 488e14ef..f3a38e18 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -61,3 +61,4 @@ pythonpath = ["main/src"]
 
 [tool.uv.sources]
 python-louvain = { git = "https://github.com/taynaud/python-louvain" }
+fast-bioservices = { path = "../fast_bioservices", editable = true }

From eed2c933243965e9bdc51b61280d2da403b477ac Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:26:55 -0600
Subject: [PATCH 24/27] revert: remove local pyproject requirement

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 pyproject.toml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index f3a38e18..488e14ef 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -61,4 +61,3 @@ pythonpath = ["main/src"]
 
 [tool.uv.sources]
 python-louvain = { git = "https://github.com/taynaud/python-louvain" }
-fast-bioservices = { path = "../fast_bioservices", editable = true }

From fa54bc794c1b8480bb2baa186543dc893afa430e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Feb 2026 16:29:33 -0600
Subject: [PATCH 25/27] chore: remove zfpkm testing files

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 tests/unit/test_approx.py      | 237 ---------------------------------
 tests/unit/test_density.py     | 226 -------------------------------
 tests/unit/test_peak_finder.py | 204 ----------------------------
 3 files changed, 667 deletions(-)
 delete mode 100644 tests/unit/test_approx.py
 delete mode 100644 tests/unit/test_density.py
 delete mode 100644 tests/unit/test_peak_finder.py

diff --git a/tests/unit/test_approx.py b/tests/unit/test_approx.py
deleted file mode 100644
index 772dbc77..00000000
--- a/tests/unit/test_approx.py
+++ /dev/null
@@ -1,237 +0,0 @@
-import numpy as np
-import pytest
-
-from como.approx import _coerce_to_float_array, _regularize_values, approx
-
-
-class TestCoerceToFloatArray:
-    """Tests for the _coerce_to_float_array helper function."""
-
-    def test_converts_int_list_to_array(self):
-        result = _coerce_to_float_array([1, 2, 3])
-        assert isinstance(result, np.ndarray)
-        assert result.dtype == float
-        np.testing.assert_array_equal(result, [1.0, 2.0, 3.0])
-
-    def test_converts_float_list_to_array(self):
-        result = _coerce_to_float_array([1.0, 2.0, 3.0])
-        assert isinstance(result, np.ndarray)
-        assert result.dtype == float
-        np.testing.assert_array_equal(result, [1.0, 2.0, 3.0])
-
-
-class TestRegularizeValues:
-    """Tests for the _regularize_values function."""
-
-    def test_removes_na_pairs_when_na_rm_true(self):
-        x: list[float] = np.array([1.0, 2.0, np.nan, 4.0])
-        y: list[float] = np.array([10.0, np.nan, 30.0, 40.0])
-        result = _regularize_values(x, y, na_rm=True, ties="mean")
-        np.testing.assert_array_equal(result.x, [1.0, 4.0])
-        np.testing.assert_array_equal(result.y, [10.0, 40.0])
-
-    def test_raises_error_with_na_in_x_when_na_rm_false(self):
-        x: list[float] = np.array([1.0, np.nan, 3.0])
-        y: list[float] = np.array([10.0, 20.0, 30.0])
-        with pytest.raises(ValueError, match="NA values in x are not allowed"):
-            _regularize_values(x, y, na_rm=False, ties="mean")
-
-    def test_sorts_by_x(self):
-        x: list[float] = np.array([3.0, 1.0, 2.0])
-        y: list[float] = np.array([30.0, 10.0, 20.0])
-        result = _regularize_values(x, y, na_rm=True, ties="mean")
-        np.testing.assert_array_equal(result.x, [1.0, 2.0, 3.0])
-        np.testing.assert_array_equal(result.y, [10.0, 20.0, 30.0])
-
-    def test_aggregates_duplicates_with_mean(self):
-        x: list[float] = np.array([1.0, 1.0, 2.0])
-        y: list[float] = np.array([10.0, 20.0, 30.0])
-        result = _regularize_values(x, y, na_rm=True, ties="mean")
-        np.testing.assert_array_equal(result.x, [1.0, 2.0])
-        np.testing.assert_array_equal(result.y, [15.0, 30.0])
-
-    def test_aggregates_duplicates_with_first(self):
-        x: list[float] = np.array([1.0, 1.0, 2.0])
-        y: list[float] = np.array([10.0, 20.0, 30.0])
-        result = _regularize_values(x, y, na_rm=True, ties="first")
-        np.testing.assert_array_equal(result.x, [1.0, 2.0])
-        np.testing.assert_array_equal(result.y, [10.0, 30.0])
-
-    def test_aggregates_duplicates_with_last(self):
-        x: list[float] = np.array([1.0, 1.0, 2.0])
-        y: list[float] = np.array([10.0, 20.0, 30.0])
-        result = _regularize_values(x, y, na_rm=True, ties="last")
-        np.testing.assert_array_equal(result.x, [1.0, 2.0])
-        np.testing.assert_array_equal(result.y, [20.0, 30.0])
-
-    def test_handles_empty_arrays(self):
-        x: list[float] = np.array([])
-        y: list[float] = np.array([])
-        result = _regularize_values(x, y, na_rm=True, ties="mean")
-        assert result.x.size == 0
-        assert result.y.size == 0
-        assert result.not_na.size == 0
-
-    def test_callable_ties_function(self):
-        x: list[float] = np.array([1.0, 1.0, 2.0])
-        y: list[float] = np.array([10.0, 20.0, 30.0])
-        result = _regularize_values(x, y, na_rm=True, ties=np.sum)
-        np.testing.assert_array_equal(result.x, [1.0, 2.0])
-        np.testing.assert_array_equal(result.y, [30.0, 30.0])
-
-
-class TestApprox:
-    """Tests for the main approx function."""
-
-    def test_basic_linear_interpolation(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [1.5, 2.5]
-        result = approx(x, y, xout=xout)
-        np.testing.assert_array_almost_equal(result.y, [15.0, 25.0])
-
-    def test_one_argument_form(self):
-        y: list[float] = [10.0, 20.0, 30.0]
-        result = approx(y, xout=[1.5, 2.5])
-        np.testing.assert_array_almost_equal(result.y, [15.0, 25.0])
-
-    def test_default_n_points(self):
-        x: list[float] = [1.0, 5.0]
-        y: list[float] = [10.0, 50.0]
-        result = approx(x, y)
-        assert len(result.x) == 50
-        assert len(result.y) == 50
-
-    def test_custom_n_points(self):
-        x: list[float] = [1.0, 5.0]
-        y: list[float] = [10.0, 50.0]
-        result = approx(x, y, n=10)
-        assert len(result.x) == 10
-
-    def test_extrapolation_rule_1(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [0.5, 3.5]
-        result = approx(x, y, xout=xout, rule=1)
-        assert np.isnan(result.y[0])
-        assert np.isnan(result.y[1])
-
-    def test_extrapolation_rule_2(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [0.5, 3.5]
-        result = approx(x, y, xout=xout, rule=2)
-        assert result.y[0] == 10.0
-        assert result.y[1] == 30.0
-
-    def test_yleft_yright_parameters(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [0.5, 3.5]
-        result = approx(x, y, xout=xout, yleft=5.0, yright=35.0)
-        assert result.y[0] == 5.0
-        assert result.y[1] == 35.0
-
-    def test_constant_method_f_0(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [1.5]
-        result = approx(x, y, xout=xout, method="constant", f=0.0)
-        assert result.y[0] == 10.0
-
-    def test_constant_method_f_1(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [1.5]
-        result = approx(x, y, xout=xout, method="constant", f=1.0)
-        assert result.y[0] == 20.0
-
-    def test_constant_method_f_05(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [1.5]
-        result = approx(x, y, xout=xout, method="constant", f=0.5)
-        assert result.y[0] == 15.0
-
-    def test_exact_match_returns_exact_value(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [2.0]
-        result = approx(x, y, xout=xout)
-        assert result.y[0] == 20.0
-
-    def test_na_in_xout_returns_nan(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [np.nan, 2.0]
-        result = approx(x, y, xout=xout)
-        assert np.isnan(result.y[0])
-        assert result.y[1] == 20.0
-
-    def test_method_numeric_codes(self):
-        x: list[float] = [1.0, 2.0]
-        y: list[float] = [10.0, 20.0]
-        xout: list[float] = [1.5]
-        result1 = approx(x, y, xout=xout, method=1)
-        result2 = approx(x, y, xout=xout, method=2, f=0.0)
-        assert result1.y[0] == 15.0
-        assert result2.y[0] == 10.0
-
-    def test_raises_error_different_length_xy(self):
-        x: list[float] = [1.0, 2.0]
-        y: list[float] = [10.0]
-        with pytest.raises(ValueError, match="x and y must have same length"):
-            approx(x, y)
-
-    def test_raises_error_invalid_method(self):
-        x: list[float] = [1.0, 2.0]
-        y: list[float] = [10.0, 20.0]
-        with pytest.raises(ValueError, match="invalid interpolation method"):
-            approx(x, y, method="invalid")
-
-    def test_raises_error_invalid_method_code(self):
-        x: list[float] = [1.0, 2.0]
-        y: list[float] = [10.0, 20.0]
-        with pytest.raises(ValueError, match="invalid interpolation method"):
-            approx(x, y, method=3)
-
-    def test_raises_error_invalid_f(self):
-        x: list[float] = [1.0, 2.0]
-        y: list[float] = [10.0, 20.0]
-        with pytest.raises(ValueError, match="invalid f value"):
-            approx(x, y, method="constant", f=2.0)
-
-    def test_raises_error_need_two_points_for_linear(self):
-        x: list[float] = [1.0]
-        y: list[float] = [10.0]
-        with pytest.raises(ValueError, match="need at least two non-NA values"):
-            approx(x, y, method="linear")
-
-    def test_raises_error_zero_points(self):
-        x: list[float] = []
-        y: list[float] = []
-        with pytest.raises(ValueError, match="zero non-NA points"):
-            approx(x, y, method="constant")
-
-    def test_handles_ties_mean(self):
-        x: list[float] = [1.0, 1.0, 2.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [1.5]
-        result = approx(x, y, xout=xout, ties="mean")
-        assert result.y[0] == 22.5
-
-    def test_rule_as_list(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, 20.0, 30.0]
-        xout: list[float] = [0.5, 3.5]
-        result = approx(x, y, xout=xout, rule=[1, 2])
-        assert np.isnan(result.y[0])
-        assert result.y[1] == 30.0
-
-    def test_na_rm_false_with_na_in_y(self):
-        x: list[float] = [1.0, 2.0, 3.0]
-        y: list[float] = [10.0, np.nan, 30.0]
-        xout: list[float] = [2.5]
-        result = approx(x, y, xout=xout, na_rm=False)
-        # After filtering out NA, should interpolate between 1.0->10.0 and 3.0->30.0
-        assert result.y[0] == 25.0
diff --git a/tests/unit/test_density.py b/tests/unit/test_density.py
deleted file mode 100644
index c4d88721..00000000
--- a/tests/unit/test_density.py
+++ /dev/null
@@ -1,226 +0,0 @@
-from typing import Literal, cast
-
-import numpy as np
-import numpy.typing as npt
-import pytest
-from numpy.testing import assert_allclose, assert_array_equal
-
-from como.density import DensityResult, bin_distance, density, dnorm, nrd0
-
-KERNEL_TYPE = Literal["gaussian", "epanechnikov", "rectangular", "triangular", "biweight", "cosine", "optcosine"]
-
-
-class TestBinDistance:
-    def test_basic_binning(self):
-        x: npt.NDArray[float] = np.array([0.5, 1.5, 2.5])
-        weights: npt.NDArray[float] = np.array([1.0, 1.0, 1.0])
-        result: npt.NDArray[float] = bin_distance(x, weights, lo=0, up=3, n=4)
-
-        assert result.shape == (8,)
-        assert np.all(result >= 0)
-
-    def test_weighted_binning(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0])
-        weights: npt.NDArray[float] = np.array([2.0, 1.0])
-        result: npt.NDArray[float] = bin_distance(x, weights, lo=0, up=3, n=4)
-
-        assert result.shape == (8,)
-        assert result.sum() > 0
-
-    def test_empty_array(self):
-        x: npt.NDArray[float] = np.array([])
-        weights: npt.NDArray[float] = np.array([])
-        result: npt.NDArray[float] = bin_distance(x, weights, lo=0, up=1, n=2)
-
-        assert result.shape == (4,)
-        assert_array_equal(result, np.zeros(4))
-
-    def test_out_of_bounds_handling(self):
-        x: npt.NDArray[float] = np.array([10.0])
-        weights: npt.NDArray[float] = np.array([1.0])
-        result: npt.NDArray[float] = bin_distance(x, weights, lo=0, up=1, n=2)
-
-        assert result.shape == (4,)
-
-
-class TestDnorm:
-    def test_standard_normal(self):
-        # dnorm(0) for standard normal should be ~0.3989
-        result: float = dnorm(0.0, mean=0.0, sd=1.0)
-        expected: float = 1.0 / np.sqrt(2 * np.pi)
-        assert_allclose(result, expected, rtol=1e-10)
-
-    def test_with_mean_and_sd(self):
-        result: float = dnorm(5.0, mean=5.0, sd=2.0)
-        expected: float = 1.0 / (2.0 * np.sqrt(2 * np.pi))
-        assert_allclose(result, expected, rtol=1e-10)
-
-    def test_log_density(self):
-        result: float = dnorm(0.0, mean=0.0, sd=1.0, log=True)
-        expected: float = np.log(1.0 / np.sqrt(2 * np.pi))
-        assert_allclose(result, expected, rtol=1e-10)
-
-    def test_nan_inputs(self):
-        assert np.isnan(dnorm(np.nan))
-        assert np.isnan(dnorm(0.0, mean=np.nan))
-        assert np.isnan(dnorm(0.0, sd=np.nan))
-
-    def test_negative_sd(self):
-        result: float = dnorm(0.0, sd=-1.0)
-        assert np.isnan(result)
-
-    def test_infinite_sd(self):
-        result: float = dnorm(0.0, sd=np.inf)
-        assert result == 0.0 or result == -np.inf
-
-    def test_zero_sd_at_mean(self):
-        result: float = dnorm(5.0, mean=5.0, sd=0.0)
-        assert np.isinf(result)
-
-    def test_zero_sd_away_from_mean(self):
-        result: float = dnorm(5.0, mean=3.0, sd=0.0, log=False)
-        assert result == 0.0
-
-    def test_fast_dnorm_flag(self):
-        result_fast: float = dnorm(1.0, fast_dnorm=True)
-        result_slow: float = dnorm(1.0, fast_dnorm=False)
-        assert_allclose(result_fast, result_slow, rtol=1e-5)
-
-    def test_large_values(self):
-        result: float = dnorm(100.0, mean=0.0, sd=1.0)
-        assert result >= 0.0
-        assert result < 1e-10
-
-
-class TestNrd0:
-    def test_basic_calculation(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
-        result: float = nrd0(x)
-        assert result > 0
-        assert np.isfinite(result)
-
-    def test_with_constant_values(self):
-        x: npt.NDArray[float] = np.array([5.0, 5.0, 5.0, 5.0])
-        result: float = nrd0(x)
-        assert result > 0
-        assert np.isfinite(result)
-
-    def test_single_nonzero_value(self):
-        x: npt.NDArray[float] = np.array([0.0, 7.0])
-        result: float = nrd0(x)
-        assert result > 0
-
-    def test_all_zeros(self):
-        # if the input array is changed from a shape of `(3,)`, the result will change
-        # this is because `nrd0` takes the length of the input into account when calculating bandwidth
-        x: npt.NDArray[float] = np.array([0.0, 0.0, 0.0], dtype=float)
-        result: float = nrd0(x)
-        assert result == 0.7224674055842076
-
-    def test_insufficient_data(self):
-        with pytest.raises(ValueError, match="need at least 2 data points"):
-            nrd0(np.array([1.0]))
-
-    def test_empty_array(self):
-        with pytest.raises(ValueError, match="need at least 2 data points"):
-            nrd0(np.array([]))
-
-
-class TestDensity:
-    def test_basic_density(self):
-        x: npt.NDArray[float] = np.random.normal(0, 1, 100)
-        result: DensityResult = density(x)
-
-        assert isinstance(result, DensityResult)
-        assert len(result.x) == 512
-        assert len(result.y) == 512
-        assert result.bw > 0
-        assert np.all(result.y >= 0)
-
-    def test_custom_bandwidth(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
-        result: DensityResult = density(x, bw=0.5)
-
-        assert_allclose(result.bw, 0.5, rtol=1e-10)
-
-    def test_with_weights(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
-        weights: npt.NDArray[float] = np.array([0.1, 0.2, 0.4, 0.2, 0.1])
-        result: DensityResult = density(x, weights=weights, n=5)
-
-        assert len(result.x) == 5
-        assert np.all(result.y >= 0)
-
-    def test_custom_grid_range(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
-        result: DensityResult = density(x, from_=0, to_=6, n=100)
-        assert result.x[0] == 0
-        assert result.x[-1] == 6
-        assert len(result.x) == 100
-
-    def test_different_kernels(self):
-        x: npt.NDArray[float] = np.random.normal(0, 1, 50)
-
-        for kernel in ["epanechnikov", "rectangular", "triangular", "biweight", "cosine", "optcosine"]:
-            with pytest.raises(NotImplementedError, match=f"Only 'gaussian' kernel is implemented; got '{kernel}'"):
-                density(x, kernel=cast(KERNEL_TYPE, kernel))
-
-    def test_kernel_only_mode(self):
-        result: npt.NDArray[float] = density([1, 2, 3], kernel="gaussian", kernel_only=True)
-        expected: float = 1 / (2 * np.sqrt(np.pi))
-        assert isinstance(result, float)
-        assert_allclose(result, expected, rtol=1e-10)
-
-    def test_na_removal(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
-        result: DensityResult = density(x, remove_na=True)
-
-        assert len(result.x) == 512
-        assert np.all(np.isfinite(result.y))
-
-    def test_na_without_removal(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
-
-        with pytest.raises(ValueError, match="NA values found"):
-            density(x, remove_na=False)
-
-    def test_insufficient_data_for_nrd0(self):
-        with pytest.raises(ValueError, match="at least two points"):
-            density([1.0], bw="nrd0")
-
-    def test_adjust_parameter(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
-        result1: DensityResult = density(x, bw=1.0, adjust=1.0)
-        result2: DensityResult = density(x, bw=1.0, adjust=2.0)
-
-        assert_allclose(result2.bw, 2.0 * result1.bw, rtol=1e-10)
-
-    def test_invalid_bandwidth_string(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-
-        with pytest.raises(TypeError, match="must be a number or 'nrd0'"):
-            density(x, bw="invalid")
-
-    def test_negative_weights(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-        weights: npt.NDArray[float] = np.array([1.0, -1.0, 1.0])
-
-        with pytest.raises(ValueError, match="Negative values found"):
-            density(x, weights=weights)
-
-    def test_infinite_values_in_x(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, np.inf, 4.0, 5.0])
-        result: DensityResult = density(x)
-
-        assert np.all(np.isfinite(result.y))
-
-    def test_result_named_tuple_fields(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
-        result: DensityResult = density(x, n=100)
-
-        assert hasattr(result, "x")
-        assert hasattr(result, "y")
-        assert hasattr(result, "x_grid")
-        assert hasattr(result, "y_grid")
-        assert hasattr(result, "bw")
-        assert hasattr(result, "n")
diff --git a/tests/unit/test_peak_finder.py b/tests/unit/test_peak_finder.py
deleted file mode 100644
index 7d2d730d..00000000
--- a/tests/unit/test_peak_finder.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import numpy as np
-import numpy.typing as npt
-import pandas as pd
-import pytest
-
-from como.peak_finder import (
-    _encode_signs,
-    _enforce_minimum_peak_distance,
-    _validate_args,
-    find_peaks,
-)
-
-
-class TestValidateArgs:
-    def test_multidimensional_array_raises_error(self):
-        x: npt.NDArray[float] = np.array([[1, 2], [3, 4]])
-        with pytest.raises(ValueError, match="Expected a 1D array, got 2D array instead"):
-            _validate_args(x, 1, 1, "0", 0.0, 1, 0.0)
-
-    def test_nan_values_raise_error(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, np.nan, 4.0])
-        with pytest.raises(ValueError, match="Input x contains NaNs"):
-            _validate_args(x, 1, 1, "0", 0.0, 1, 0.0)
-
-    def test_negative_nups_raises_error(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-        with pytest.raises(ValueError, match="Argument 'nups' must be non-negative"):
-            _validate_args(x, -1, 1, "0", 0.0, 1, 0.0)
-
-    def test_negative_ndowns_raises_error(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-        with pytest.raises(ValueError, match="Argument 'ndowns' must be non-negative"):
-            _validate_args(x, 1, -1, "0", 0.0, 1, 0.0)
-
-    def test_invalid_zero_raises_error(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-        with pytest.raises(ValueError, match="Argument 'zero' must be '0', '\\+', or '-'"):
-            _validate_args(x, 1, 1, "x", 0.0, 1, 0.0)  # type: ignore[invalid-type-argument]
-
-    def test_negative_min_peak_height_raises_error(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-        with pytest.raises(ValueError, match="Argument 'min_peak_height' must be non-negative"):
-            _validate_args(x, 1, 1, "0", -1.0, 1, 0.0)
-
-    def test_negative_min_peak_distance_raises_error(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-        with pytest.raises(ValueError, match="Argument 'minpeakdistance' must be non-negative"):
-            _validate_args(x, 1, 1, "0", 0.0, -1, 0.0)
-
-    def test_negative_threshold_raises_error(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-        with pytest.raises(ValueError, match="Argument 'threshold' must be non-negative"):
-            _validate_args(x, 1, 1, "0", 0.0, 1, -1.0)
-
-    def test_valid_args_does_not_raise(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0])
-        _validate_args(x, 1, 1, "0", 0.0, 1, 0.0)
-
-
-class TestEncodeSigns:
-    def test_increasing_sequence(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 3.0, 4.0])
-        result: str = _encode_signs(x, "0")
-        assert result == "+++"
-
-    def test_decreasing_sequence(self):
-        x: npt.NDArray[float] = np.array([4.0, 3.0, 2.0, 1.0])
-        result: str = _encode_signs(x, "0")
-        assert result == "---"
-
-    def test_flat_sequence_with_zero(self):
-        x: npt.NDArray[float] = np.array([1.0, 1.0, 1.0])
-        result: str = _encode_signs(x, "0")
-        assert result == "00"
-
-    def test_flat_sequence_with_plus(self):
-        x: npt.NDArray[float] = np.array([1.0, 1.0, 1.0])
-        result: str = _encode_signs(x, "+")
-        assert result == "++"
-
-    def test_flat_sequence_with_minus(self):
-        x: npt.NDArray[float] = np.array([1.0, 1.0, 1.0])
-        result: str = _encode_signs(x, "-")
-        assert result == "--"
-
-    def test_flat_sequence_with_dollarsign(self):
-        x: npt.NDArray[float] = np.array([1.0, 1.0, 1.0])
-        result: str = _encode_signs(x, "$")
-        assert result == "$$"
-
-    def test_mixed_sequence(self):
-        x: npt.NDArray[float] = np.array([1.0, 2.0, 2.0, 3.0, 2.0])
-        result: str = _encode_signs(x, "0")
-        assert result == "+0+-"
-
-
-class TestEnforceMinimumPeakDistance:
-    def test_inplace_removes_close_peaks(self):
-        df: pd.DataFrame = pd.DataFrame(
-            {
-                "height": [10.0, 8.0, 5.0],
-                "peak_idx": [0, 2, 10],
-                "start_idx": [0, 1, 9],
-                "end_idx": [1, 3, 11],
-            }
-        )
-        _enforce_minimum_peak_distance(df, min_peak_distance=5, inplace=True)
-        assert len(df) == 2
-        assert df["peak_idx"].tolist() == [0, 10]
-
-    def test_not_inplace_returns_new_dataframe(self):
-        df: pd.DataFrame = pd.DataFrame(
-            {
-                "height": [10.0, 8.0, 5.0],
-                "peak_idx": [0, 2, 10],
-                "start_idx": [0, 1, 9],
-                "end_idx": [1, 3, 11],
-            }
-        )
-        result = _enforce_minimum_peak_distance(df, min_peak_distance=5, inplace=False)
-        assert len(result) == 2
-        assert len(df) == 3  # original unchanged
-        assert result["peak_idx"].tolist() == [0, 10]
-
-    def test_all_peaks_sufficiently_spaced(self):
-        df: pd.DataFrame = pd.DataFrame(
-            {
-                "height": [10.0, 8.0],
-                "peak_idx": [0, 10],
-                "start_idx": [0, 9],
-                "end_idx": [1, 11],
-            }
-        )
-        _enforce_minimum_peak_distance(df, min_peak_distance=5, inplace=True)
-        assert len(df) == 2
-
-
-class TestFindPeaks:
-    def test_simple_peak(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 1.0, 2.0, 3.0, 2.0, 1.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, min_peak_height=0.0)
-        assert len(result) == 1
-        assert result.iloc[0]["peak_idx"] == 3
-        assert result.iloc[0]["height"] == 3.0
-
-    def test_multiple_peaks(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 1.0, 0.0, 2.0, 0.0, 3.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, min_peak_height=0.0)
-        assert len(result) == 3
-
-    def test_no_peaks(self):
-        x: npt.NDArray[float] = np.asarray([1.0, 2.0, 3.0, 4.0, 5.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x)
-        assert len(result) == 0
-        assert list(result.columns) == ["height", "peak_idx", "start_idx", "end_idx"]
-
-    def test_min_peak_height_filter(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 1.0, 0.0, 5.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, min_peak_height=2.0)
-        assert len(result) == 1
-        assert result.iloc[0]["height"] == 5.0
-
-    def test_nups_and_ndowns(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 1.0, 2.0, 3.0, 2.0, 1.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, nups=2, ndowns=2, min_peak_height=0.0)
-        assert len(result) == 1
-
-    def test_npeaks_limits_output(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 5.0, 0.0, 3.0, 0.0, 1.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, npeaks=2, min_peak_height=0.0)
-        assert len(result) == 2
-
-    def test_sortstr_orders_by_height(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 1.0, 0.0, 5.0, 0.0, 3.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, sortstr=True, min_peak_height=0.0)
-        heights = result["height"].tolist()
-        assert heights == sorted(heights, reverse=True)
-
-    def test_min_peak_distance(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 10.0, 0.0, 8.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, min_peak_distance=3, min_peak_height=0.0)
-        assert len(result) == 1
-        assert result.iloc[0]["height"] == 10.0
-
-    def test_threshold_filter(self):
-        x: npt.NDArray[float] = np.asarray([1.0, 2.0, 1.5, 5.0, 1.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, threshold=2.0, min_peak_height=0.0)
-        assert len(result) == 1
-        assert result.iloc[0]["height"] == 5.0
-
-    def test_accepts_list_input(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 1.0, 2.0, 1.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, min_peak_height=0.0)
-        assert len(result) == 1
-
-    def test_accepts_numpy_array(self):
-        x: npt.NDArray[float] = np.array([0.0, 1.0, 2.0, 1.0, 0.0])
-        result: pd.DataFrame = find_peaks(x, min_peak_height=0.0)
-        assert len(result) == 1
-
-    def test_custom_peak_pattern(self):
-        x: npt.NDArray[float] = np.asarray([0.0, 1.0, 2.0, 3.0, 2.0, 1.0, 0.0], dtype=float)
-        result: pd.DataFrame = find_peaks(x, peak_pattern=r"[+]{3,}[-]{3,}", min_peak_height=0.0)
-        assert len(result) == 1

From eb9a55121f71c07c5f42ac558a7495812504b700 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Feb 2026 14:26:42 -0600
Subject: [PATCH 26/27] chore: force sync lock

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 uv.lock | 2306 ++++++++++++++++++++++++++++---------------------------
 1 file changed, 1155 insertions(+), 1151 deletions(-)

diff --git a/uv.lock b/uv.lock
index c4bac4ae..eb47f1c6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -8,11 +8,11 @@ resolution-markers = [
 
 [[package]]
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+version = "25.1.0"
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+sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
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+    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
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 [[package]]
@@ -26,7 +26,7 @@ wheels = [
 
 [[package]]
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-version = "0.12.2"
+version = "0.12.10"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -39,9 +39,9 @@ dependencies = [
     { name = "scipy" },
     { name = "zarr" },
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-sdist = { url = "https://files.pythonhosted.org/packages/b1/42/398550c2099e9017c0dbcead889910f09992367628fee8191446fdea3471/anndata-0.12.2.tar.gz", hash = "sha256:ecb3e0613585f5b464d0d3cfb0043a981b2eb92efaa90aae9042dd3e289c4a0a", size = 586312, upload-time = "2025-08-11T07:20:09.549Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/84/81/29809c5710123bb37ea9d9de9da0e83dda5be9d8419cce256e4406b37c44/anndata-0.12.10.tar.gz", hash = "sha256:73a73c99ca50400eb9dc7f2fdd400cf677ea4bb9ef1f7c04691c0fc557e43d7f", size = 2254675, upload-time = "2026-02-06T14:02:24.716Z" }
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+    { url = "https://files.pythonhosted.org/packages/4c/f4/4d0193dc5bab3af74e9560a8b45830d88ac707467d15ceff7e3df17adc41/anndata-0.12.10-py3-none-any.whl", hash = "sha256:e3d940d8e34373dc250f998c1011c1da52721f980de9d83a0599daa2baa286e5", size = 176574, upload-time = "2026-02-06T14:02:23.097Z" },
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 [[package]]
@@ -55,16 +55,15 @@ wheels = [
 
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-version = "4.11.0"
+version = "4.12.1"
 source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
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 [[package]]
@@ -120,42 +119,42 @@ wheels = [
 
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+version = "1.13.0"
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+    { url = "https://files.pythonhosted.org/packages/df/5d/493b1b5528ab5072feae30821ff3a07b7a0474213d548efb1fdf135f85c1/array_api_compat-1.13.0-py3-none-any.whl", hash = "sha256:c15026a0ddec42815383f07da285472e1b1ff2e632eb7afbcfe9b08fcbad9bf1", size = 58585, upload-time = "2025-12-28T11:26:56.081Z" },
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 [[package]]
 name = "arrow"
-version = "1.3.0"
+version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "python-dateutil" },
-    { name = "types-python-dateutil" },
+    { name = "tzdata" },
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-sdist = { url = "https://files.pythonhosted.org/packages/2e/00/0f6e8fcdb23ea632c866620cc872729ff43ed91d284c866b515c6342b173/arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85", size = 131960, upload-time = "2023-09-30T22:11:18.25Z" }
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 [[package]]
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From e7c5c118573afd58f2b9e5d5b5f0a8872dfb4056 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Feb 2026 17:07:53 -0600
Subject: [PATCH 27/27] Purge `fast_bioservices` (#246)

* feat: use bioservice's `MyGeneInfo`

Signed-off-by: Josh Loecker <joshloecker@icloud.com>

* refactor: convert from fast_bioservices to COMO's built-in pipeline

Signed-off-by: Josh Loecker <joshloecker@icloud.com>

* refactor: remove fast-bioservices from pyproject

Signed-off-by: Josh Loecker <joshloecker@icloud.com>

* chore: use bioservices over fast_bioservices

Signed-off-by: Josh Loecker <joshloecker@icloud.com>

* feat(test): added unit tests for identifier conversion pipeline

Signed-off-by: Josh Loecker <joshloecker@icloud.com>

---------

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/combine_distributions.py    |  43 ++-
 main/como/merge_xomics.py             |  23 +-
 main/como/pipelines/identifier.py     | 100 ++++++
 main/como/proteomics/Crux.py          |  30 +-
 main/como/proteomics_gen.py           |  19 +-
 main/como/rnaseq_gen.py               |   9 +-
 main/como/rnaseq_preprocess.py        |   8 +-
 main/como/utils.py                    |  84 ++---
 pyproject.toml                        |   2 +-
 tests/test_proteomics.py              |   2 +-
 tests/unit/test_identifier_convert.py | 274 +++++++++++++++
 uv.lock                               | 468 +++++++++++++++++++++-----
 12 files changed, 882 insertions(+), 180 deletions(-)
 create mode 100644 main/como/pipelines/identifier.py
 create mode 100644 tests/unit/test_identifier_convert.py

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 2fb38462..4b5406a8 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -18,7 +18,8 @@
     _OutputCombinedSourceFilepath,
     _SourceWeights,
 )
-from como.utils import LogLevel, log_and_raise_error, _num_columns, get_missing_gene_data
+from como.pipelines.identifier import convert
+from como.utils import LogLevel, get_missing_gene_data, log_and_raise_error, num_columns
 
 
 def _combine_z_distribution_for_batch(
@@ -55,9 +56,13 @@ def _combine_z_distribution_for_batch(
         f"source: '{source.value}', "
         f"context: '{context_name}'"
     )
-    if _num_columns(matrix) < 2:
-        logger.trace(f"A single sample exists for batch '{batch.batch_num}'. Returning as-is because no additional combining can be done")
-        return matrix
+    if num_columns(matrix) < 2:
+        logger.trace(
+            f"A single sample exists for batch '{batch.batch_num}'. Returning as-is because no additional combining can be done"
+        )
+        with_batch_num = matrix.copy()
+        with_batch_num.columns = [batch.batch_num]
+        return with_batch_num
 
     values = matrix.values
     weighted_matrix = np.clip(
@@ -116,7 +121,7 @@ def _combine_z_distribution_for_source(
           A pandas dataframe of the weighted z-distributions
     """
     # If only one batch column exists, return as-is (rename like R path-through).
-    if _num_columns(merged_source_data) <= 1:
+    if num_columns(merged_source_data) <= 1:
         logger.warning("A single batch exists for this source; returning matrix as-is.")
         out_df = merged_source_data.copy()
         out_df.columns = ["combine_z"]
@@ -203,8 +208,10 @@ def _combine_z_distribution_for_context(
         z_matrices.append(matrix)
 
     z_matrix = pd.concat(z_matrices, axis="columns", join="outer", ignore_index=False)
-    if _num_columns(z_matrix) <= 1:
-        logger.trace(f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine")
+    if num_columns(z_matrix) <= 1:
+        logger.trace(
+            f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine"
+        )
         z_matrix.columns = ["combine_z"]
         return z_matrix
 
@@ -275,8 +282,26 @@ async def _begin_combining_distributions(
         batch_results: list[pd.DataFrame] = []
         for batch in batch_names[source.value]:
             batch: _BatchEntry
-            matrix_subset = cast(pd.DataFrame, matrix[[GeneIdentifier.ensembl_gene_id.value, *batch.sample_names]])
-            matrix_subset = matrix_subset.set_index(keys=[GeneIdentifier.ensembl_gene_id.value], drop=True)
+            if isinstance(matrix, pd.DataFrame):
+                matrix_subset = matrix[[GeneIdentifier.entrez_gene_id.value, *batch.sample_names]]
+                matrix_subset = matrix_subset.set_index(keys=[GeneIdentifier.entrez_gene_id.value], drop=True)
+                matrix_subset = matrix_subset.drop(columns=["gene_symbol", "ensembl_gene_id"], errors="ignore")
+            elif isinstance(matrix, sc.AnnData):
+                conversion = convert(ids=matrix.var_names.tolist(), taxon=taxon)
+                conversion.reset_index(drop=False, inplace=True)
+                matrix: pd.DataFrame = matrix.to_df().T
+                matrix.reset_index(inplace=True, drop=False, names=["gene_symbol"])
+                matrix: pd.DataFrame = matrix.merge(
+                    conversion, left_on="gene_symbol", right_on="gene_symbol", how="left"
+                )
+                matrix_subset: pd.DataFrame = matrix[[GeneIdentifier.entrez_gene_id.value, *batch.sample_names]]
+                matrix_subset: pd.DataFrame = matrix_subset.set_index(
+                    keys=[GeneIdentifier.entrez_gene_id.value], drop=True
+                )
+            else:
+                raise TypeError(
+                    f"Unexpected matrix type for source '{source.value}'; expected 'pandas.DataFrame' or 'scanpy.AnnData': {type(matrix)}"
+                )
 
             output_fp = output_filepaths[source.value].parent / f"{source.value}_batch{batch.batch_num}_combined_z_distrobution_{context_name}.csv"
             batch_results.append(
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index f1e04a81..96d6a3a9 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -1,14 +1,12 @@
 from __future__ import annotations
 
-import asyncio
 import re
 import sys
 from pathlib import Path
-from typing import TextIO, cast
+from typing import TextIO
 
 import numpy as np
 import pandas as pd
-from fast_bioservices.biothings.mygene import MyGene
 from loguru import logger
 
 from como.combine_distributions import _begin_combining_distributions
@@ -24,7 +22,7 @@
     _SourceWeights,
 )
 from como.project import Config
-from como.utils import log_and_raise_error, read_file, set_up_logging, get_missing_gene_data, return_placeholder_data
+from como.utils import get_missing_gene_data, log_and_raise_error, read_file, return_placeholder_data, set_up_logging
 
 
 class _MergedHeaderNames:
@@ -348,17 +346,16 @@ async def _update_missing_data(input_matrices: _InputMatrices, taxon_id: int) ->
         "proteomics": [input_matrices.proteomics],
     }
     logger.trace(f"Gathering missing data for data sources: {','.join(key for key in matrix_keys if key is not None)}")
+    # ruff: disable[E501]
     # fmt: off
-    results = await asyncio.gather(
-        *[
-            # Using 'is not None' is required because the truth value of a Dataframe is ambiguous
-            get_missing_gene_data(values=input_matrices.trna, taxon_id=taxon_id) if input_matrices.trna is not None else asyncio.sleep(0),
-            get_missing_gene_data(values=input_matrices.mrna, taxon_id=taxon_id) if input_matrices.mrna is not None else asyncio.sleep(0),
-            get_missing_gene_data(values=input_matrices.scrna, taxon_id=taxon_id) if input_matrices.scrna is not None else asyncio.sleep(0),
-            get_missing_gene_data(values=input_matrices.proteomics, taxon_id=taxon_id) if input_matrices.proteomics is not None else asyncio.sleep(0),
-        ]
+    results: tuple[pd.DataFrame | None, ...] = (
+        get_missing_gene_data(values=input_matrices.trna, taxon_id=taxon_id) if input_matrices.trna is not None else None,
+        get_missing_gene_data(values=input_matrices.mrna, taxon_id=taxon_id) if input_matrices.mrna is not None else None,
+        get_missing_gene_data(values=input_matrices.scrna, taxon_id=taxon_id) if input_matrices.scrna is not None else None,
+        get_missing_gene_data(values=input_matrices.proteomics, taxon_id=taxon_id) if input_matrices.proteomics is not None else None,
     )
     # fmt: on
+    # ruff: enable[E501]
     for i, key in enumerate(matrix_keys):
         matrix_keys[key].append(results[i])
 
@@ -591,7 +588,7 @@ async def merge_xomics(  # noqa: C901
     log_location: str | TextIO = sys.stderr,
 ):
     """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
-    _set_up_logging(level=log_level, location=log_location)
+    set_up_logging(level=log_level, location=log_location)
     logger.info(f"Starting to merge all omics data for context: '{context_name}'")
 
     # fmt: off
diff --git a/main/como/pipelines/identifier.py b/main/como/pipelines/identifier.py
new file mode 100644
index 00000000..59fe7444
--- /dev/null
+++ b/main/como/pipelines/identifier.py
@@ -0,0 +1,100 @@
+from collections.abc import Sequence
+from typing import Literal
+
+import pandas as pd
+from bioservices.mygeneinfo import MyGeneInfo
+
+__all__ = [
+    "convert",
+    "determine_gene_type",
+]
+
+T_MG_SCOPE = Literal["entrezgene", "ensembl.gene", "symbol"]
+T_MG_TRANSLATE = Literal["entrez_gene_id", "ensembl_gene_id", "gene_symbol"]
+T_MG_RETURN = list[dict[T_MG_TRANSLATE, str]]
+
+
+def _get_conversion(info: MyGeneInfo, values: list[str], scope: T_MG_SCOPE, fields: str, taxon: str) -> T_MG_RETURN:
+    value_str = ",".join(map(str, values))
+    results = info.get_queries(query=value_str, dotfield=True, scopes=scope, fields=fields, species=taxon)
+    if not isinstance(results, list):
+        raise TypeError(f"Expected results to be a list, but got {type(results)}")
+    if not isinstance(results[0], dict):
+        raise TypeError(f"Expected each result to be a dict, but got {type(results[0])}")
+
+    data: T_MG_RETURN = []
+    for result in results:
+        ensembl = result.get("query" if scope == "ensembl.gene" else "ensembl.gene")
+        entrez = result.get("query" if scope == "entrezgene" else "entrezgene")
+        symbol = result.get("query" if scope == "symbol" else "symbol")
+        data.append({"ensembl_gene_id": ensembl, "entrez_gene_id": entrez, "gene_symbol": symbol})
+    return data
+
+
+def convert(ids: int | str | Sequence[int] | Sequence[str] | Sequence[int | str], taxon: int | str, cache: bool = True):
+    """Convert between genomic identifiers.
+
+    This function will convert between the following components:
+        - Entrez Gene ID
+        - Ensembl Gene ID
+        - Gene Symbol
+
+    :param ids: IDs to be converted
+    :param taxon: Taxonomic identifier
+    :param: scope: The type of identifier provided in `ids`
+    :param cache: Should local caching be used for queries
+    :return: DataFrame with columns "entrez_gene_id", "ensembl_gene_id", and "gene_symbol"
+    """
+    my_geneinfo = MyGeneInfo(cache=cache)
+    chunk_size = 1000
+    id_list = list(map(str, [ids] if isinstance(ids, (int, str)) else ids))
+    chunks = list(range(0, len(id_list), chunk_size))
+
+    data_type = determine_gene_type(id_list)
+    if not all(v == data_type[id_list[0]] for v in data_type.values()):
+        raise ValueError("All items in ids must be of the same type (Entrez, Ensembl, or symbols).")
+
+    scope = next(iter(data_type.values()))
+    fields = ",".join({"ensembl.gene", "entrezgene", "symbol"} - {scope})
+    taxon_str = str(taxon)
+    return pd.DataFrame(
+        [
+            row
+            for i in chunks
+            for row in _get_conversion(
+                info=my_geneinfo,
+                values=id_list[i : i + chunk_size],
+                scope=scope,
+                fields=fields,
+                taxon=taxon_str,
+            )
+        ]
+    )
+
+
+def determine_gene_type(items: str | list[str], /) -> dict[str, T_MG_SCOPE]:
+    """Determine the genomic data type.
+
+    :param items: A string or list of strings representing gene identifiers.
+        The function will determine whether each identifier is an Entrez Gene ID,
+        Ensembl Gene ID, or a gene symbol based on its format.
+
+    :return: A dictionary mapping each input item to its determined type, which can be one of:
+        - "entrez_gene_id": If the item consists solely of digits.
+        - "ensembl_gene_id": If the item starts with "ENS" and is
+            followed by a specific format (length greater than 11 and the last 11 characters are digits).
+        - "gene_symbol": If the item does not match the above criteria, it is assumed to be a gene symbol.
+    """
+    items = [items] if isinstance(items, str) else items
+
+    determine: dict[str, Literal["entrezgene", "ensembl.gene", "symbol"]] = {}
+    for i in items:
+        i_str = str(i).split(".")[0] if isinstance(i, float) else str(i)
+        if i_str.isdigit():
+            determine[i_str] = "entrezgene"
+        elif i_str.startswith("ENS") and (len(i_str) > 11 and all(i.isdigit() for i in i_str[-11:])):
+            determine[i_str] = "ensembl.gene"
+        else:
+            determine[i_str] = "symbol"
+
+    return determine
diff --git a/main/como/proteomics/Crux.py b/main/como/proteomics/Crux.py
index 9351cf4e..1703a857 100644
--- a/main/como/proteomics/Crux.py
+++ b/main/como/proteomics/Crux.py
@@ -1,5 +1,6 @@
 # ruff: noqa
 
+from typing import Type
 import asyncio
 import multiprocessing
 import os
@@ -8,10 +9,11 @@
 from multiprocessing.sharedctypes import Synchronized
 from pathlib import Path
 
+from bioservices.biodbnet import BioDBNet
 import numpy as np
 import pandas as pd
 import tqdm
-from fast_bioservices import BioDBNet
+
 
 from como.proteomics.FileInformation import FileInformation, clear_print
 
@@ -128,7 +130,9 @@ def mzml_to_sqt(self) -> None:
             )
 
             # Replace all "comet.*" in output directory with the name of the file being processed
-            comet_files = [str(file) for file in os.listdir(file_information.sqt_base_path) if str(file).startswith("comet.")]
+            comet_files = [
+                str(file) for file in os.listdir(file_information.sqt_base_path) if str(file).startswith("comet.")
+            ]
             for file_name in comet_files:
                 # Determine the old file path
                 old_file_path: Path = Path(file_information.sqt_base_path, file_name)
@@ -240,7 +244,9 @@ def collect_uniprot_ids_and_ion_intensity(self) -> None:
 
             # Assign the file_information intensity dataframe to the gathered values
             self._file_information[i].intensity_df = pd.DataFrame(average_intensities_dict)
-            self._file_information[i].intensity_df = self._file_information[i].intensity_df.groupby("uniprot", as_index=False).mean()
+            self._file_information[i].intensity_df = (
+                self._file_information[i].intensity_df.groupby("uniprot", as_index=False).mean()
+            )
 
     async def _convert_uniprot_wrapper(self) -> None:
         """This function is a multiprocessing wrapper around the convert_ids function"""
@@ -248,7 +254,9 @@ async def _convert_uniprot_wrapper(self) -> None:
 
         # Create a progress bar of results
         # From: https://stackoverflow.com/a/61041328/
-        progress_bar = tqdm.tqdm(desc="Starting UniProt to Gene Symbol conversion... ", total=len(self._file_information))
+        progress_bar = tqdm.tqdm(
+            desc="Starting UniProt to Gene Symbol conversion... ", total=len(self._file_information)
+        )
         for i, result in enumerate(asyncio.as_completed(values)):
             await result  # Get result from asyncio.as_completed
             progress_bar.set_description(f"Working on {i + 1} of {len(self._file_information)}")
@@ -271,13 +279,11 @@ async def async_convert_uniprot(self, file_information: FileInformation) -> None
             loop = asyncio.get_event_loop()
             # async with self._semaphore:
             #     gene_symbols: pd.DataFrame = await loop.run_in_executor(None, self._biodbnet.db2db, "UniProt Accession", "Gene Symbol", input_values)
-            gene_symbols: pd.DataFrame = await loop.run_in_executor(
-                None,
-                self._biodbnet.db2db,
-                input_values,
-                "UniProt Accession",
-                "Gene Symbol",
+            gene_symbols = self._biodbnet.db2db(
+                input_db="UniProt Accession", output_db="Gene Symbol", input_values=input_values
             )
+            if not isinstance(gene_symbols, pd.DataFrame):
+                raise TypeError(f"Expected gene_symbols to be a DataFrame, but got {type(gene_symbols)}")
 
             # The index is UniProt IDs. Create a new column of these values
             gene_symbols["uniprot"] = gene_symbols.index
@@ -372,7 +378,9 @@ def split_abundance_values(self) -> None:
                 # Create a new dataframe to split the S# columns from
                 split_frame: pd.DataFrame = dataframe.copy()
                 # Get the current S{i} columns in
-                abundance_columns: list[str] = [column for column in split_frame.columns if re.match(rf"{cell_type}_S{i}R\d+", column)]
+                abundance_columns: list[str] = [
+                    column for column in split_frame.columns if re.match(rf"{cell_type}_S{i}R\d+", column)
+                ]
                 take_columns: list[str] = ["symbol"] + abundance_columns
                 average_intensity_name: str = f"{cell_type}_S{i}"
 
diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index fa76c618..a2714e69 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -71,15 +71,18 @@ async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None
         biodbnet.services.settings.TIMEOUT = 60
         for i in range(0, len(gene_symbols), step_size):
             # Operations: Goes from gene_symbols=["A", "B;C", "D"] -> gene.split=[["A"], ["B", "C"], ["D"]] -> chain.from_iterable["A", "B", "C", "D"]
-            chunk: list[str] = list(itertools.chain.from_iterable(gene.split(";") for gene in gene_symbols[i : i + step_size]))
-            dataframes.append(
-                biodbnet.db2db(
-                    input_values=chunk,
-                    input_db=Input.GENE_SYMBOL.value,
-                    output_db=[Output.GENE_ID.value, Output.ENSEMBL_GENE_ID.value],
-                    taxon=9606,
-                )
+            chunk: list[str] = list(
+                itertools.chain.from_iterable(gene.split(";") for gene in gene_symbols[i : i + step_size])
             )
+            result = biodbnet.db2db(
+                input_db="Gene Symbol",
+                output_db=["Gene ID", "Ensembl Gene ID"],
+                input_values=chunk,
+                taxon=9606,
+            )
+            if not isinstance(result, pd.DataFrame):
+                raise TypeError(f"Got {type(result)}, expected pd.DataFrame")
+            dataframes.append(result)
         df = pd.concat(dataframes, axis="columns")
         print(df)
         df.loc[df["gene_id"].isna(), ["gene_id"]] = np.nan
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 817b76d6..ba1d3df7 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -10,7 +10,6 @@
 from typing import NamedTuple, TextIO, cast
 
 import anndata as ad
-import boolean
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
@@ -19,13 +18,13 @@
 import sklearn.neighbors
 from anndata.compat import XDataArray
 from anndata.experimental.backed import Dataset2D
-from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 from scipy import sparse
 from zfpkm import zFPKM, zfpkm_plot
 
 from como.data_types import FilteringTechnique, LogLevel, RNAType
 from como.migrations import gene_info_migrations
+from como.pipelines.identifier import convert
 from como.project import Config
 from como.utils import log_and_raise_error, read_file, set_up_logging
 
@@ -173,7 +172,7 @@ async def _build_matrix_results(
             raise TypeError("AnnData.var is expected to be a pandas.DataFrame")
 
         matrix.var = matrix.var.reset_index(drop=False, names=["gene_symbol"])
-        conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=matrix.var["gene_symbol"].tolist(), taxon=taxon)
+        conversion = convert(ids=matrix.var["gene_symbol"].tolist(), taxon=taxon)
     else:
         if "ensembl_gene_id" not in matrix.columns:
             log_and_raise_error(
@@ -181,9 +180,7 @@ async def _build_matrix_results(
                 error=ValueError,
                 level=LogLevel.CRITICAL,
             )
-        conversion: pd.DataFrame = await ensembl_to_gene_id_and_symbol(
-            ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon
-        )
+        conversion: pd.DataFrame = convert(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
     # If the entrez gene id column is empty, it is indicative that the incorrect taxon id was provided
     if conversion["entrez_gene_id"].eq("-").all():
         logger.critical(
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index d2e9a8dc..6faf4862 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -15,11 +15,10 @@
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
-from fast_bioservices.biothings.mygene import MyGene
-from fast_bioservices.pipeline import gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 
 from como.data_types import LogLevel, RNAType
+from como.pipelines.identifier import convert
 from como.utils import log_and_raise_error, read_file, set_up_logging
 
 
@@ -183,6 +182,7 @@ def to_dataframe(cls, samples: list[SampleConfiguration]) -> tuple[pd.DataFrame,
 
         return config, lengths
 
+
 def _sample_name_from_filepath(file: Path) -> str:
     group = re.search(r".+_S\d+R\d+(r\d+)?", file.stem)
     if not group:
@@ -196,9 +196,11 @@ def _sample_name_from_filepath(file: Path) -> str:
         )
     return group.group()
 
+
 def _sample_name_from_filepath(file: Path) -> str:
     return re.search(r".+_S\d+R\d+(r\d+)?", file.stem).group()
 
+
 def _require_one(
     paths: list[Path | None],
     kind: Literal["layout", "strand", "preparation", "fragment"],
@@ -536,7 +538,7 @@ async def read_ensembl_gene_ids(file: Path) -> list[str]:
         if isinstance(data_, pd.DataFrame):
             return data_["ensembl_gene_id"].tolist()
         try:
-            conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=data_.var_names.tolist(), taxon=taxon)
+            conversion = convert(ids=data_.var_names.tolist(), taxon=taxon)
         except json.JSONDecodeError as e:
             log_and_raise_error(
                 f"Got a JSON decode error for file '{counts_matrix_filepaths}' ({e})",
diff --git a/main/como/utils.py b/main/como/utils.py
index 38db7d72..996adef1 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -3,27 +3,24 @@
 import contextlib
 import io
 import sys
-from collections.abc import Iterator
+from collections.abc import Iterator, Sequence
 from pathlib import Path
 from typing import Any, Literal, NoReturn, TextIO, TypeVar, overload
 
 import numpy.typing as npt
 import pandas as pd
 import scanpy as sc
-from fast_bioservices import BioDBNet, Output, Taxon
-from fast_bioservices.pipeline import (
-    determine_gene_type,
-    ensembl_to_gene_id_and_symbol,
-    gene_id_to_ensembl_and_gene_symbol,
-    gene_symbol_to_ensembl_and_gene_id,
-)
 from loguru import logger
 
 from como.data_types import LOG_FORMAT, Algorithm, LogLevel
+from como.pipelines.identifier import convert
 
 T = TypeVar("T")
 __all__ = [
+    "get_missing_gene_data",
     "log_and_raise_error",
+    "num_columns",
+    "num_rows",
     "read_file",
     "set_up_logging",
     "split_gene_expression_data",
@@ -124,48 +121,21 @@ def suppress_stdout() -> Iterator[None]:
             sys.stdout = sys.__stdout__
 
 
-async def _format_determination(
-    biodbnet: BioDBNet, *, requested_output: Output | list[Output], input_values: list[str], taxon: Taxon
-) -> pd.DataFrame:
-    """Determine the data type of the given input values (i.e., Entrez Gene ID, Gene Symbol, etc.).
+def get_missing_gene_data(values: Sequence[str] | pd.DataFrame | sc.AnnData, taxon_id: int | str) -> pd.DataFrame:
+    """Get missing gene data from a given set of values.
 
-    Args:
-        biodbnet: The BioDBNet to use for determination
-        requested_output: The data type to generate (of type `Output`)
-        input_values: The input values to determine
-        taxon: The Taxon ID
-
-    Returns:
-        A pandas DataFrame
+    This function will attempt to find gene identifiers in the provided values and convert them to a DataFrame
+    containing "entrez_gene_id", "ensembl_gene_id", and "gene_symbol"
 
+    :param values: The values to extract gene identifiers from.
+        This can be a list of strings, a pandas DataFrame, or a scanpy AnnData object.
+    :param taxon_id: The taxonomic identifier to use for gene consversion
+    :return: A DataFrame containing "entrez_gene_id", "ensembl_gene_id", and "gene_symbol" for the provided values
     """
-    requested_output = [requested_output] if isinstance(requested_output, Output) else requested_output
-    coercion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(
-        columns=["Input Type"]
-    )
-    coercion.columns = pd.Index(["input_value", *[o.value.replace(" ", "_").lower() for o in requested_output]])
-    return coercion
-
-
-async def get_missing_gene_data(  # noqa: C901
-    values: list[str] | pd.DataFrame | sc.AnnData, taxon_id: int | str | Taxon
-) -> pd.DataFrame:
-    if isinstance(values, list) and not isinstance(
-        values, pd.DataFrame
-    ):  # second isinstance required for static type check to be happy
-        gene_type = await determine_gene_type(values)
-        if all(v == "gene_symbol" for v in gene_type.values()):
-            return await gene_symbol_to_ensembl_and_gene_id(values, taxon=taxon_id)
-        elif all(v == "ensembl_gene_id" for v in gene_type.values()):
-            return await ensembl_to_gene_id_and_symbol(ids=values, taxon=taxon_id)
-        elif all(v == "entrez_gene_id" for v in gene_type.values()):
-            return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id)
-        else:
-            log_and_raise_error(
-                message="Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)",
-                error=ValueError,
-                level=LogLevel.CRITICAL,
-            )
+    # second isinstance required for static type check to be happy
+    # if isinstance(values, list) and not isinstance(values, pd.DataFrame):
+    if isinstance(values, list):
+        return convert(ids=values, taxon=taxon_id)
     elif isinstance(values, pd.DataFrame):
         # raise error if duplicate column names exist
         if any(values.columns.duplicated(keep=False)):
@@ -183,17 +153,17 @@ async def get_missing_gene_data(  # noqa: C901
         if values.index.name is not None:
             names.append(str(values.index.name))
         if "gene_symbol" in names:
-            return await get_missing_gene_data(
+            return get_missing_gene_data(
                 values["gene_symbol"].tolist() if "gene_symbol" in values.columns else values.index.tolist(),
                 taxon_id=taxon_id,
             )
         elif "entrez_gene_id" in names:
-            return await get_missing_gene_data(
+            return get_missing_gene_data(
                 values["entrez_gene_id"].tolist() if "entrez_gene_id" in values.columns else values.index.tolist(),
                 taxon_id=taxon_id,
             )
         elif "ensembl_gene_id" in names:
-            return await get_missing_gene_data(
+            return get_missing_gene_data(
                 values["ensembl_gene_id"].tolist() if "ensembl_gene_id" in values.columns else values.index.tolist(),
                 taxon_id=taxon_id,
             )
@@ -315,11 +285,21 @@ def _listify(value: T | list[T]) -> list[T]:
     return value if isinstance(value, list) else [value]
 
 
-def _num_rows(item: pd.DataFrame | npt.NDArray) -> int:
+def num_rows(item: pd.DataFrame | npt.NDArray) -> int:
+    """Return the number of rows in an object.
+
+    :param item: The object to check the number of rows of
+    :return: The number of rows in the provided object
+    """
     return item.shape[0]
 
 
-def _num_columns(item: pd.DataFrame | npt.NDArray) -> int:
+def num_columns(item: pd.DataFrame | npt.NDArray) -> int:
+    """Return the number of columns in an object.
+
+    :param item: The object to check the number of columns of
+    :return: The number of columns in the provided object
+    """
     return item.shape[1]
 
 
diff --git a/pyproject.toml b/pyproject.toml
index 070be7c5..ede17cb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,9 +9,9 @@ requires-python = ">=3.11,<3.14"
 dependencies = [
     "aioftp>=0.23.1",
     "anndata>=0.12.0",
+    "bioservices>=1.12.1",
     "cobamp@git+https://github.com/JoshLoecker/cobamp@master",
     "cobra>=0.28.0",
-    "fast-bioservices>=0.3.9",
     "joypy>=0.2.6",
     "kaleido>=1.0.0",
     "loguru>=0.7.2",
diff --git a/tests/test_proteomics.py b/tests/test_proteomics.py
index defb0fef..0f38be27 100644
--- a/tests/test_proteomics.py
+++ b/tests/test_proteomics.py
@@ -2,11 +2,11 @@
 
 import aioftp
 import pytest
-
 from como.proteomics.Crux import MZMLtoSQT, RAWtoMZML, SQTtoCSV
 from como.proteomics.FileInformation import FileInformation
 from como.proteomics.FTPManager import Download, Reader, aioftp_client
 from como.proteomics.proteomics_preprocess import ParseCSVInput, PopulateInformation
+
 from tests.fixtures.fixture_ftp_server import fixture_ftp_server, ftp_file_names
 
 
diff --git a/tests/unit/test_identifier_convert.py b/tests/unit/test_identifier_convert.py
new file mode 100644
index 00000000..72118877
--- /dev/null
+++ b/tests/unit/test_identifier_convert.py
@@ -0,0 +1,274 @@
+# tests/test_gene_conversion.py
+from __future__ import annotations
+
+import importlib
+import sys
+import types
+from dataclasses import dataclass
+
+import pandas as pd
+import pytest
+
+MODULE_NAME = "como.pipelines.identifier"
+
+
+@pytest.fixture
+def mod(monkeypatch):
+    """Import como in a way that does NOT require bioservices to be installed (we stub it before import).
+
+    If bioservices *is* installed, this still keeps tests hermetic and avoids
+    any accidental network calls.
+    """
+    # Stub bioservices.mygeneinfo.MyGeneInfo so importing the module works
+    bioservices = types.ModuleType("bioservices")
+    mygeneinfo = types.ModuleType("bioservices.mygeneinfo")
+
+    class DummyMyGeneInfo:
+        def __init__(self, cache: bool = True):
+            self.cache = cache
+
+        def get_queries(self, **kwargs):
+            raise AssertionError(
+                "DummyMyGeneInfo.get_queries was called unexpectedly. "
+                "Patch mod.MyGeneInfo or use _get_conversion tests with a fake info object."
+            )
+
+    mygeneinfo.MyGeneInfo = DummyMyGeneInfo
+    bioservices.mygeneinfo = mygeneinfo
+
+    monkeypatch.setitem(sys.modules, "bioservices", bioservices)
+    monkeypatch.setitem(sys.modules, "bioservices.mygeneinfo", mygeneinfo)
+
+    # Ensure a clean import each test
+    if MODULE_NAME in sys.modules:
+        del sys.modules[MODULE_NAME]
+
+    return importlib.import_module(MODULE_NAME)
+
+
+def test_determine_gene_type_accepts_single_string(mod):
+    assert mod.determine_gene_type("1017") == {"1017": "entrezgene"}
+
+
+def test_determine_gene_type_classifies_common_formats_and_float(mod):
+    # Includes a float to exercise the float-specific split(".")[0] branch.
+    out = mod.determine_gene_type(["1017", "ENSG00000141510", "TP53", 123.0])
+
+    assert out["1017"] == "entrezgene"
+    assert out["ENSG00000141510"] == "ensembl.gene"
+    assert out["TP53"] == "symbol"
+    # float 123.0 -> "123"
+    assert out["123"] == "entrezgene"
+
+
+def test_determine_gene_type_ensembl_format_requires_ens_prefix_and_11_trailing_digits(mod):
+    # Should be Ensembl-like: starts with ENS, length > 11, last 11 chars are digits
+    assert mod.determine_gene_type(["ENSX00000123456"])["ENSX00000123456"] == "ensembl.gene"
+    # Not enough trailing digits / too short => symbol
+    assert mod.determine_gene_type(["ENS123"])["ENS123"] == "symbol"
+    # ENS but last 11 not all digits => symbol
+    assert mod.determine_gene_type(["ENSGABCDEFGHIJK"])["ENSGABCDEFGHIJK"] == "symbol"
+
+
+# ---------------------------------------------------------------------------
+# _get_conversion tests
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class FakeInfo:
+    return_value: object
+    calls: list[dict] = None
+
+    def __post_init__(self):  # noqa: D105
+        self.calls = []
+
+    def get_queries(self, **kwargs):
+        self.calls.append(kwargs)
+        return self.return_value
+
+
+def test_get_conversion_raises_typeerror_when_results_not_list(mod):
+    info = FakeInfo(return_value={"not": "a list"})
+    with pytest.raises(TypeError, match="Expected results to be a list"):
+        mod._get_conversion(info=info, values=["1017"], scope="entrezgene", fields="ensembl.gene,symbol", taxon="9606")
+
+
+def test_get_conversion_raises_typeerror_when_first_item_not_dict(mod):
+    info = FakeInfo(return_value=["not a dict"])
+    with pytest.raises(TypeError, match="Expected each result to be a dict"):
+        mod._get_conversion(info=info, values=["1017"], scope="entrezgene", fields="ensembl.gene,symbol", taxon="9606")
+
+
+def test_get_conversion_calls_get_queries_with_expected_arguments(mod):
+    info = FakeInfo(
+        return_value=[
+            {"query": "1017", "ensembl.gene": "ENSG00000123374", "symbol": "CDK2"},
+        ]
+    )
+
+    out = mod._get_conversion(
+        info=info, values=["1017"], scope="entrezgene", fields="ensembl.gene,symbol", taxon="9606"
+    )
+
+    assert out == [
+        {"ensembl_gene_id": "ENSG00000123374", "entrez_gene_id": "1017", "gene_symbol": "CDK2"},
+    ]
+
+    assert len(info.calls) == 1
+    call = info.calls[0]
+    assert call["query"] == "1017"
+    assert call["dotfield"] is True
+    assert call["scopes"] == "entrezgene"
+    assert call["fields"] == "ensembl.gene,symbol"
+    assert call["species"] == "9606"
+
+
+@pytest.mark.parametrize(
+    ("scope", "results", "expected"),
+    [
+        (
+            "entrezgene",
+            [
+                {"query": "1017", "ensembl.gene": "ENSG00000123374", "symbol": "CDK2"},
+                {"query": "1018", "ensembl.gene": "ENSG00000123400", "symbol": "CDK3"},
+            ],
+            [
+                {"ensembl_gene_id": "ENSG00000123374", "entrez_gene_id": "1017", "gene_symbol": "CDK2"},
+                {"ensembl_gene_id": "ENSG00000123400", "entrez_gene_id": "1018", "gene_symbol": "CDK3"},
+            ],
+        ),
+        (
+            "ensembl.gene",
+            [
+                {"query": "ENSG00000141510", "entrezgene": "7157", "symbol": "TP53"},
+            ],
+            [
+                {"ensembl_gene_id": "ENSG00000141510", "entrez_gene_id": "7157", "gene_symbol": "TP53"},
+            ],
+        ),
+        (
+            "symbol",
+            [
+                {"query": "TP53", "ensembl.gene": "ENSG00000141510", "entrezgene": "7157"},
+            ],
+            [
+                {"ensembl_gene_id": "ENSG00000141510", "entrez_gene_id": "7157", "gene_symbol": "TP53"},
+            ],
+        ),
+    ],
+)
+def test_get_conversion_scope_specific_mapping(mod, scope, results, expected):
+    info = FakeInfo(return_value=results)
+    out = mod._get_conversion(info=info, values=[r["query"] for r in results], scope=scope, fields="x", taxon="9606")
+    assert out == expected
+
+
+# ---------------------------------------------------------------------------
+# convert tests
+# ---------------------------------------------------------------------------
+
+
+def test_convert_rejects_mixed_id_types(mod):
+    # Mixed: Entrez-like ("1017") + symbol ("TP53") => ValueError
+    with pytest.raises(ValueError, match="All items in ids must be of the same type"):
+        mod.convert(ids=["1017", "TP53"], taxon=9606)
+
+
+def test_convert_single_id_passes_expected_scope_fields_taxon_and_cache(monkeypatch, mod):
+    calls = []
+
+    class CapturingMyGeneInfo:
+        def __init__(self, cache: bool = True):
+            self.cache = cache
+
+    def fake_get_conversion(*, info, values, scope, fields, taxon):
+        calls.append({"info": info, "values": values, "scope": scope, "fields": fields, "taxon": taxon})
+        # Return one row per value
+        return [{"ensembl_gene_id": "ENSG_TEST", "entrez_gene_id": v, "gene_symbol": "SYM_TEST"} for v in values]
+
+    monkeypatch.setattr(mod, "MyGeneInfo", CapturingMyGeneInfo)
+    monkeypatch.setattr(mod, "_get_conversion", fake_get_conversion)
+
+    df = mod.convert(ids=1017, taxon=9606, cache=False)
+
+    assert isinstance(df, pd.DataFrame)
+    assert set(df.columns) == {"ensembl_gene_id", "entrez_gene_id", "gene_symbol"}
+    assert len(df) == 1
+    assert df.loc[0, "entrez_gene_id"] == "1017"
+    assert df.loc[0, "ensembl_gene_id"] == "ENSG_TEST"
+    assert df.loc[0, "gene_symbol"] == "SYM_TEST"
+
+    assert len(calls) == 1
+    call = calls[0]
+    assert isinstance(call["info"], CapturingMyGeneInfo)
+    assert call["info"].cache is False
+    assert call["values"] == ["1017"]
+    assert call["scope"] == "entrezgene"
+    assert call["taxon"] == "9606"
+
+    # Fields are computed via set arithmetic; order is not guaranteed.
+    assert set(call["fields"].split(",")) == {"ensembl.gene", "symbol"}
+
+
+def test_convert_chunks_inputs_over_1000(monkeypatch, mod):
+    calls = []
+
+    class CapturingMyGeneInfo:
+        def __init__(self, cache: bool = True):
+            self.cache = cache
+
+    def fake_get_conversion(*, info, values, scope, fields, taxon):
+        calls.append({"values_len": len(values), "scope": scope, "fields": fields, "taxon": taxon})
+        return [
+            {"ensembl_gene_id": f"ENSG{v.zfill(11)}", "entrez_gene_id": v, "gene_symbol": f"SYM{v}"} for v in values
+        ]
+
+    monkeypatch.setattr(mod, "MyGeneInfo", CapturingMyGeneInfo)
+    monkeypatch.setattr(mod, "_get_conversion", fake_get_conversion)
+
+    ids = list(range(1, 1002))  # 1001 ids => should create 2 chunks: 1000 and 1
+    df = mod.convert(ids=ids, taxon="9606")
+
+    assert len(df) == 1001
+    assert set(df.columns) == {"ensembl_gene_id", "entrez_gene_id", "gene_symbol"}
+
+    # Ensure chunking happened as expected
+    assert [c["values_len"] for c in calls] == [1000, 1]
+    for c in calls:
+        assert c["scope"] == "entrezgene"
+        assert c["taxon"] == "9606"
+        assert set(c["fields"].split(",")) == {"ensembl.gene", "symbol"}
+
+    # Spot-check first and last rows keep ordering
+    assert df.loc[0, "entrez_gene_id"] == "1"
+    assert df.loc[0, "gene_symbol"] == "SYM1"
+    assert df.loc[1000, "entrez_gene_id"] == "1001"
+    assert df.loc[1000, "gene_symbol"] == "SYM1001"
+
+
+def test_convert_symbol_scope_fields_exclude_symbol(monkeypatch, mod):
+    calls = []
+
+    class CapturingMyGeneInfo:
+        def __init__(self, cache: bool = True):
+            self.cache = cache
+
+    def fake_get_conversion(*, info, values, scope, fields, taxon):
+        calls.append({"scope": scope, "fields": fields, "taxon": taxon, "values": values})
+        # Return minimal row(s)
+        return [{"ensembl_gene_id": None, "entrez_gene_id": None, "gene_symbol": v} for v in values]
+
+    monkeypatch.setattr(mod, "MyGeneInfo", CapturingMyGeneInfo)
+    monkeypatch.setattr(mod, "_get_conversion", fake_get_conversion)
+
+    df = mod.convert(ids=["TP53", "BRCA1"], taxon=9606)
+
+    assert len(df) == 2
+    assert set(df.columns) == {"ensembl_gene_id", "entrez_gene_id", "gene_symbol"}
+
+    assert len(calls) == 1
+    assert calls[0]["scope"] == "symbol"
+    assert calls[0]["taxon"] == "9606"
+    # When scope is "symbol", requested fields should be the other two
+    assert set(calls[0]["fields"].split(",")) == {"ensembl.gene", "entrezgene"}
diff --git a/uv.lock b/uv.lock
index eb47f1c6..9139aea2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -6,15 +6,6 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
-[[package]]
-name = "aiofiles"
-version = "25.1.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
-]
-
 [[package]]
 name = "aioftp"
 version = "0.27.2"
@@ -188,6 +179,33 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" },
 ]
 
+[[package]]
+name = "bioservices"
+version = "1.12.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "appdirs" },
+    { name = "beautifulsoup4" },
+    { name = "click" },
+    { name = "colorlog" },
+    { name = "easydev" },
+    { name = "grequests" },
+    { name = "lxml" },
+    { name = "matplotlib" },
+    { name = "pandas" },
+    { name = "requests" },
+    { name = "requests-cache" },
+    { name = "rich-click" },
+    { name = "suds-community" },
+    { name = "tqdm" },
+    { name = "wrapt" },
+    { name = "xmltodict" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/51/1a8dc87ffc6c27e0a896b982da3ff1b483f5ef85a47ae1ffafbc6a479bb4/bioservices-1.12.1.tar.gz", hash = "sha256:0f31782ae50930d4ab82b43f98d1ca2cc9befaa699f3a7a6cba512a8f9e2cab0", size = 218752, upload-time = "2025-02-27T22:38:58.628Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b8/c9/656854139abbdc0a09d544bacc6fa3132245ebf9847dd45da9a8e416c5a5/bioservices-1.12.1-py3-none-any.whl", hash = "sha256:898033fe0158a0e31ea5ad93ff02f5000aa67ff9d06e9dc6477583d3f60ace80", size = 258032, upload-time = "2025-02-27T22:38:56.406Z" },
+]
+
 [[package]]
 name = "bleach"
 version = "6.3.0"
@@ -214,6 +232,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e5/ca/78d423b324b8d77900030fa59c4aa9054261ef0925631cd2501dd015b7b7/boolean_py-5.0-py3-none-any.whl", hash = "sha256:ef28a70bd43115208441b53a045d1549e2f0ec6e3d08a9d142cbc41c1938e8d9", size = 26577, upload-time = "2025-04-03T10:39:48.449Z" },
 ]
 
+[[package]]
+name = "cattrs"
+version = "25.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6e/00/2432bb2d445b39b5407f0a90e01b9a271475eea7caf913d7a86bcb956385/cattrs-25.3.0.tar.gz", hash = "sha256:1ac88d9e5eda10436c4517e390a4142d88638fe682c436c93db7ce4a277b884a", size = 509321, upload-time = "2025-10-07T12:26:08.737Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d8/2b/a40e1488fdfa02d3f9a653a61a5935ea08b3c2225ee818db6a76c7ba9695/cattrs-25.3.0-py3-none-any.whl", hash = "sha256:9896e84e0a5bf723bc7b4b68f4481785367ce07a8a02e7e9ee6eb2819bc306ff", size = 70738, upload-time = "2025-10-07T12:26:06.603Z" },
+]
+
 [[package]]
 name = "certifi"
 version = "2026.1.4"
@@ -341,6 +372,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/9f/d73dfb85d7a5b1a56a99adc50f2074029468168c970ff5daeade4ad819e4/choreographer-1.2.1-py3-none-any.whl", hash = "sha256:9af5385effa3c204dbc337abf7ac74fd8908ced326a15645dc31dde75718c77e", size = 49338, upload-time = "2025-11-09T23:04:43.154Z" },
 ]
 
+[[package]]
+name = "click"
+version = "8.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
+]
+
 [[package]]
 name = "cobamp"
 version = "0.2.2"
@@ -389,6 +432,18 @@ wheels = [
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