diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 113c905d..bdc7b156 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -14,7 +14,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v4
+        uses: astral-sh/setup-uv@v5
 
       - name: Create Virtual Environment
         run: uv venv
@@ -26,17 +26,17 @@ jobs:
         run: uv run jupyter nbconvert --clear-output --inplace "main/COMO.ipynb"
 
       - name: Format Python Imports
-        uses: astral-sh/ruff-action@v2
+        uses: astral-sh/ruff-action@v3
         with:
           args: "check --fix --select I"
 
       - name: Format code
-        uses: astral-sh/ruff-action@v2
+        uses: astral-sh/ruff-action@v3
         with:
           args: "format"
 
       - name: Format Notebook
-        uses: astral-sh/ruff-action@v2
+        uses: astral-sh/ruff-action@v3
         with:
           args: "format main/COMO.ipynb"
 
@@ -54,7 +54,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Check Lint
-        uses: astral-sh/ruff-action@v2
+        uses: astral-sh/ruff-action@v3
         with:
           args: "check --no-fix --verbose"
 
@@ -68,7 +68,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v4
+        uses: astral-sh/setup-uv@v5
         with:
           enable-cache: "true"
           cache-suffix: "${{ matrix.python-version }}"
diff --git a/.github/workflows/pypackage_build.yml b/.github/workflows/pypackage_build.yml
new file mode 100644
index 00000000..3570f7de
--- /dev/null
+++ b/.github/workflows/pypackage_build.yml
@@ -0,0 +1,92 @@
+name: Build and publish python package
+
+on:
+  push:
+    tags:
+      - "*"
+
+# Define permissions needed for the workflow GITHUB_TOKEN
+permissions:
+  contents: read # Allow checkout
+  packages: write # Allow publishing to GitHub Packages
+
+
+jobs:
+  build:
+    strategy:
+      matrix:
+        operating-system: [macos-latest, ubuntu-latest, windows-latest]
+        python-version: [ 3.10, 3.11, 3.12, 3.13 ]
+
+    name: Build Python Package (${{ matrix.operating-system }}, Python ${{ matrix.python-version }})
+    runs-on: ${{ matrix.operating-system }}
+
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.ref }}
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Hatch
+        run: pip install hatch
+
+      - name: Build distributions
+        run: hatch build # Uses pyproject.toml to build sdist and wheel into dist/
+
+      - name: Upload distributions artifact
+        uses: actions/upload-artifact@v4 # Action to save artifacts between jobs
+        with:
+          name: como-distribution-package-${{ matrix.operating-system }}-${{ matrix.python-version }}  # Name for the artifact
+          path: dist/ # Path to the directory to upload
+
+  publish:
+    strategy:
+      matrix:
+        python-version: [ 3.10, 3.11, 3.12, 3.13 ]
+        operating-system: [macos-latest, windows-latest, ubuntu-latest]
+
+    name: Publish to GitHub Packages
+    runs-on: ubuntu-latest
+    needs: build # Depends on the build job succeeding
+
+    # IMPORTANT: Only run the publish job when a tag starting with 'v' is pushed
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
+
+
+    permissions: # Explicit permissions needed for this job
+      packages: write # Required to write to GitHub Packages registry
+      contents: read # Needed if accessing repo content (e.g., for download artifact)
+
+    steps:
+      - name: Download distributions artifact
+        uses: actions/download-artifact@v4 # Action to retrieve artifacts from previous job.
+        with:
+          name: como-distribution-package-${{ matrix.operating-system }}-${{ matrix.python-version }}
+          path: dist/
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Twine
+        run: pip install twine
+
+      - name: Publish package to GitHub Packages
+        env:
+          # Use __token__ as username and the automatically generated GITHUB_TOKEN as password
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          echo "Uploading to GitHub Packages for repository: ${{ github.repository }}"
+          # Construct the repository URL dynamically using the repository owner
+          TWINE_REPOSITORY_URL="https://pypi.pkg.github.com/${{ github.repository_owner }}"
+          python -m twine upload --verbose --repository-url ${TWINE_REPOSITORY_URL} dist/*
+
+        
+    
diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 3cbebec1..6b5b7f51 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -1,12 +1,8 @@
 {
  "cells": [
   {
+   "metadata": {},
    "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
    "source": [
     "# COMO: Constraint-based Optomization of Metabolic Objectives\n",
     "\n",
@@ -56,15 +52,12 @@
     "    2. Knock-out simulation\n",
     "    3. Compare results between perturbed and unperturbed models (i.e., knocked-out models vs non-knocked-out models)\n",
     "    4. Integrate with disease genes and create a score of drug targets"
-   ]
+   ],
+   "id": "ea1c9614518c30bc"
   },
   {
+   "metadata": {},
    "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
    "source": [
     "# Step 1: Data Preprocessing and Analysis\n",
     "\n",
@@ -229,7 +222,8 @@
     "        </tr>\n",
     "    </tbody>\n",
     "</table>"
-   ]
+   ],
+   "id": "28d59417491e63db"
   },
   {
    "metadata": {},
@@ -259,18 +253,20 @@
     "# If you are using single-cell data, adjust the following lines accordingly\n",
     "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n",
     "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
-   ]
+   ],
+   "id": "18061578964ca096"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "### RNA-seq Preprocessing Parameters\n",
     "- `context_names`: The tissue/cell types to use. This is a simple space-separated list of items, such as \"naiveB regulatoryTcell\"\n",
     "- `gene_format`: The format of input genes, accepts `\"Extrez\"`, `\"Emsembl\"` or `\"Symbol\"`\n",
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use\n",
     "- `preprocess_mode`: This should be set to `\"create-matrix\"` if you are **not** providing a matrix, otherwise set it to `\"provide-matrix\"`"
-   ]
+   ],
+   "id": "b475e41b000d0cb5"
   },
   {
    "metadata": {},
@@ -294,11 +290,12 @@
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
-   ]
+   ],
+   "id": "dbfb32f2a0f7b8ef"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "\n",
     "## Identification of Gene Activity in Transcriptomic and Proteomic Datasets\n",
@@ -326,11 +323,12 @@
     "This is a flat cutoff value of counts per million normalized values. Gene expression will be checked for consensus at the replicate and study level.\n",
     "\n",
     "This method is not recommended, as zFPKM is much more robust for a similar level of \"hands-off\" model building\n"
-   ]
+   ],
+   "id": "a820a3985a2601c7"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "### RNA Sequencing Analysis\n",
     "#### Bulk RNA Sequencing\n",
@@ -346,11 +344,12 @@
     "\n",
     "#### Single Cell RNA Sequencing\n",
     "While the Snakemake pipeline does not yet support single-cell alignment, and COMO does not yet support automated configuration file and counts matrix file creation for single-cell alignment output from STAR, it is possible to use single-cell data to create a model with COMO. Because normalization strategies can be applied to single-cell data in the same way it is applied to bulk RNA sequencing, `como/rnaseq_gen.py` can be used with a provided counts matrix and configuration file, from [Step 1](Step-1:-Initialize-and-Preprocess-RNA-seq-data), above. Just like `\"total\"` and `\"mRNA\"`, `como/rnaseq_gen.py` can be executed with `\"SC\"` as the \"`--library-prep`\" argument to help COMO differentiate it from any bulk RNA sequencing data if multiple strategies are being used."
-   ]
+   ],
+   "id": "a1d6608d045c7399"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "\n",
     "### Total RNA Sequencing Generation\n",
@@ -364,7 +363,8 @@
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering\n",
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
-   ]
+   ],
+   "id": "1fcf38e9cbc3de48"
   },
   {
    "metadata": {},
@@ -399,11 +399,12 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ]
+   ],
+   "id": "5a2e17b646d6de36"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## mRNA Sequencing Generation\n",
     "These parameters are identical to the ones listed for [total RNA sequencing](#Total-RNA-Sequencing-Generation), but they are listed again here for ease of reference\n",
@@ -418,7 +419,8 @@
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering\n",
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
-   ]
+   ],
+   "id": "87b5397301541650"
   },
   {
    "metadata": {},
@@ -453,11 +455,12 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ]
+   ],
+   "id": "3e73c07fa4a9e49a"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## Single-Cell RNA Sequencing Generation\n",
     "These parameters are identical to the ones listed for [total RNA sequencing](#Total-RNA-Sequencing-Generation), but they are listed again here for ease of reference\n",
@@ -472,7 +475,8 @@
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering\n",
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"scrna\"`,\n"
-   ]
+   ],
+   "id": "ae1278480199f415"
   },
   {
    "metadata": {},
@@ -507,11 +511,12 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ]
+   ],
+   "id": "567734d827571d6f"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## Proteomics Analysis\n",
     "The parameters here are mostly the same to total RNA and mRNA sequencing analysis, and are listed here for easier reference\n",
@@ -523,7 +528,8 @@
     "- `high_rep_ratio`: The ratio required before a gene is considered \"high-confidence\" in the replicate\n",
     "- `high_batch_ratio`: The ratio required before a gene is considered \"high-confidence\" in the study\n",
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering"
-   ]
+   ],
+   "id": "ea7004d23807dd1"
   },
   {
    "metadata": {},
@@ -549,7 +555,8 @@
     "        high_confidence_batch_ratio=0.7,\n",
     "        quantile=25,\n",
     "    )"
-   ]
+   ],
+   "id": "a7715d2cf9b0191a"
   },
   {
    "metadata": {},
@@ -574,7 +581,8 @@
     "- `n_neighbors_batch`: N nearest neighbors for batch clustering. The default is `\"default\"`, which is the total number of batches\n",
     "- `n_neighbors_context`: N nearest neighbors for context clustering. The default is `\"default\"`, which is the total number of contexts\n",
     "- `seed`: The random seed for clustering algorithm initialization. If not specified, `np.random.randint(0, 100000)` is used"
-   ]
+   ],
+   "id": "f4f395e25eeb1185"
   },
   {
    "metadata": {},
@@ -617,11 +625,12 @@
     "# fmt: on\n",
     "\n",
     "!{cmd}"
-   ]
+   ],
+   "id": "cc61326b8df01fc4"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## Merge Expression from Different Data Sources\n",
     "\n",
@@ -656,13 +665,14 @@
     "- `proteomics_weight`: Proteomic weight for merging zFPKM distribution\n",
     "\n",
     "Each of the \"weights\" (`total_rna_weight`, `mrna_weight`, etc.) are used to place a significance on each method. Becuase there are many steps in the Dogma from transcription to translation, the gene expression as seen by total RNA or mRNA sequencing may not be representative of the gene's protein expression, and this its metabolic impact. Because of this, you are able to weight each source more (or less) than another."
-   ]
+   ],
+   "id": "236fb36fa41f60b"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "expression_requirement = 3\n",
     "requirement_adjust = \"regressive\"\n",
@@ -692,11 +702,12 @@
     "# fmt: on\n",
     "\n",
     "!{cmd}"
-   ]
+   ],
+   "id": "3ce78fc277c55885"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Step 2: Create Tissue/Cell-Type Specific Models\n",
     "\n",
@@ -758,13 +769,14 @@
     "- `boundary_reactions_filename`: The filename of boundary reactions that should be used\n",
     "- `force_reactions_filename`: The filename of the force reactions to be used. Force reactions will (as the name implies) force the optimizer to use these reactions, **no matter their expression**\n",
     "- `exclude_reactions_filename`: The filename of reactions to exclude from the model, no matter their expression"
-   ]
+   ],
+   "id": "b3a182617ff3eb7a"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import json\n",
     "import os\n",
@@ -826,11 +838,12 @@
     "        )\n",
     "        # fmt: on\n",
     "        !{cmd}"
-   ]
+   ],
+   "id": "e94c36e65ba46e38"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Generate MEMOTE Reports\n",
     "> NOTE: This step is entirely optional\n",
@@ -850,13 +863,14 @@
     "- `mapName`: This is the name of the map file. In the `map_dict` dictionary below, this value would be `trypto`, `retinol`, etc.\n",
     "- `exampleTissue`: This is the name of the tissue context\n",
     "- `ALGORITHM`: This is the algorithm (`recon_algorithm`) used in the above model creation step\n"
-   ]
+   ],
+   "id": "47e3e576b43a040d"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import cobra\n",
     "from como.project import Config\n",
@@ -926,11 +940,12 @@
     "        )\n",
     "\n",
     "        !{cmd}"
-   ]
+   ],
+   "id": "4a064f3322a3991e"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Step 3: Disease-related Gene Identification\n",
     "This step can identify disease related genes by analyzing patient transcriptomics' data\n",
@@ -944,13 +959,14 @@
     "- `disease_names`: The diseases you are using. This should match the first section of the sheet name in the Excel file\n",
     "- `data_source`: The datasource you are using for disease analysis. This should be`\"rnaseq\"`\n",
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use for disease analysis"
-   ]
+   ],
+   "id": "3aa49b19ab4fc4b9"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "from como.utils import stringlist_to_list\n",
     "\n",
@@ -975,11 +991,12 @@
     "    # fmt: on\n",
     "\n",
     "    !{cmd}"
-   ]
+   ],
+   "id": "7f946d38cac3a18c"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Step 4: Drug Targets & Repurposable Drug Identification\n",
     "This step performs a series of tasks:\n",
@@ -1009,13 +1026,14 @@
     "Other than the `model_files` parameter (if required), the only other parameter for this section is the `solver` option\n",
     "\n",
     "- `solver`: The solver you would like to use. Available options are `\"gurobi\"` or `\"glpk\"`\n"
-   ]
+   ],
+   "id": "feb9da60acb9a3b3"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import json\n",
     "\n",
@@ -1080,30 +1098,11 @@
     "\n",
     "            cmd = \" \".join(cmd)\n",
     "            !{cmd}"
-   ]
+   ],
+   "id": "c999b3f953381f9f"
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": true
- },
+ "metadata": {},
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index f6ac8b91..4e483985 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -172,7 +172,7 @@ def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
         inner_string = inner_string.replace("[", "")
         inner_string = inner_string.replace("]", "")
 
-    expression_out = f"{gpr_expression[:loc_l]}{inner_string}{gpr_expression[loc_r + 1:]}"
+    expression_out = f"{gpr_expression[:loc_l]}{inner_string}{gpr_expression[loc_r + 1 :]}"
     expression_out = _gene_rule_logical(expression_out, level + 1)
 
     return expression_out
@@ -340,11 +340,11 @@ def _build_with_imat(
 def _build_with_tinit(cobra_model: cobra.Model, s_matrix, lb, ub, expr_vector, solver, idx_force) -> Model:
     properties = tINITProperties(
         reactions_scores=expr_vector,
-        solver=solver,
         essential_reactions=idx_force,
         production_weight=0.0,
         allow_excretion=False,
         no_reverse_loops=True,
+        solver=solver,
     )
     algorithm = tINIT(s_matrix, lb, ub, properties)
     algorithm.preprocessing()
@@ -384,7 +384,7 @@ def _map_expression_to_reaction(
             continue
         for gid in gene_ids:
             if gid in gene_expressions.index:
-                rep_val = f' {gene_expressions.at[gid, "active"]} '
+                rep_val = f" {gene_expressions.at[gid, 'active']} "
             else:
                 rep_val = f" {unknown_val!s} "
             gene_reaction_rule = f" {gene_reaction_rule} "  # pad white space to prevent gene matches inside floats
@@ -565,7 +565,6 @@ def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
     for column in df.columns:
         if column not in [
             "boundary",
-            "reaction",
             "abbreviation",
             "compartment",
             "minimum reaction rate",
@@ -577,7 +576,7 @@ def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
             )
 
     reactions: list[str] = [""] * len(df)
-    boundary_type: list[str] = df["reaction"].tolist()
+    boundary_type: list[str] = df["boundary"].tolist()
     reaction_abbreviation: list[str] = df["abbreviation"].tolist()
     reaction_compartment: list[str] = df["compartment"].tolist()
     lower_bound = df["minimum reaction rate"].tolist()
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 8dc2a6fd..77d2336a 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -194,7 +194,7 @@ def _merge_logical_table(df: pd.DataFrame):
         full_entrez_id_sets.add(new_entrez_id)
 
     entrez_dups_list.extend(i.split(" /// ") for i in full_entrez_id_sets)
-    entrez_dups_dict = dict(zip(full_entrez_id_sets, entrez_dups_list))
+    entrez_dups_dict = dict(zip(full_entrez_id_sets, entrez_dups_list, strict=True))
 
     for merged_entrez_id, entrez_dups_list in entrez_dups_dict.items():
         df["entrez_gene_id"].replace(to_replace=entrez_dups_list, value=merged_entrez_id, inplace=True)
diff --git a/main/como/proteomics/proteomics_preprocess.py b/main/como/proteomics/proteomics_preprocess.py
index 89264ca2..85d78ec2 100644
--- a/main/como/proteomics/proteomics_preprocess.py
+++ b/main/como/proteomics/proteomics_preprocess.py
@@ -158,7 +158,7 @@ def _gather_data(self):
             url_count = 0
 
             # Iterate through the URLs available
-            for url, study in zip(ftp_urls, studies):
+            for url, study in zip(ftp_urls, studies, strict=True):
                 ftp_data: FTPManager.Reader = FTPManager.Reader(
                     root_link=url, file_extensions=self._preferred_extensions
                 )
@@ -168,7 +168,7 @@ def _gather_data(self):
                 url_count += len(urls)
 
                 # Iterate through all files and sizes found for url_##
-                for file, size in zip(urls, sizes):
+                for file, size in zip(urls, sizes, strict=True):
                     self.file_information.append(
                         FileInformation(cell_type=cell_type, download_url=file, file_size=size, study=study)
                     )
diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index 1329406d..bced01b0 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -137,7 +137,7 @@ def protein_transform_main(
 ) -> None:
     """Transform protein abundance data."""
     abundance_df: pd.DataFrame = (
-        pd.read_csv(abundance_df) if isinstance(abundance_df, (str, Path)) else abundance_df.fillna(0)
+        pd.read_csv(abundance_df) if isinstance(abundance_df, str | Path) else abundance_df.fillna(0)
     )
     abundance_df = abundance_df[np.isfinite(abundance_df).all(axis=1)]  # Remove +/- infinity values
     z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0)
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 364bd4c3..b0079b8c 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -3,13 +3,13 @@
 import math
 import multiprocessing
 import time
-from collections import namedtuple
+from collections import Callable, namedtuple
 from dataclasses import dataclass, field
 from enum import Enum
 from functools import partial
 from multiprocessing.pool import Pool
 from pathlib import Path
-from typing import Callable, NamedTuple
+from typing import NamedTuple
 
 import numpy as np
 import numpy.typing as npt
@@ -349,8 +349,7 @@ def zfpkm_transform(
     """Perform zFPKM calculation/transformation."""
     if update_every_percent > 1:
         logger.warning(
-            f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - "
-            f"will convert to percentage"
+            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
 
@@ -359,8 +358,7 @@ def zfpkm_transform(
     cores = min(multiprocessing.cpu_count() - 2, total)
     logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores")
     logger.debug(
-        f"Will update every {update_per_step:,} steps as this is approximately "
-        f"{update_every_percent:.1%} of {total:,}"
+        f"Will update every {update_per_step:,} steps as this is approximately {update_every_percent:.1%} of {total:,}"
     )
 
     with Pool(processes=cores) as pool:
@@ -392,9 +390,7 @@ def zfpkm_transform(
                 total_time = current_time - start_time
                 formatted = f"{i:,}"
                 logger.debug(
-                    f"Processed {formatted:>{log_padding}} of {total:,} - "
-                    f"chunk took {chunk:.1f} seconds - "
-                    f"running for {total_time:.1f} seconds"
+                    f"Processed {formatted:>{log_padding}} of {total:,} - chunk took {chunk:.1f} seconds - running for {total_time:.1f} seconds"
                 )
                 chunk_time = current_time
     return results, zfpkm_df
@@ -546,13 +542,15 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
         top_genes: npt.NDArray[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) if keep]
-        metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes) if keep]
+        metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes, strict=True) if keep]
+        metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes, strict=True) if keep]
         metric.count_matrix = metric.count_matrix.iloc[min_genes, :]
         metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :]
 
-        keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes) if keep]
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes) if keep]
+        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
+        ]
 
     metrics = calculate_z_score(metrics)
 
@@ -583,13 +581,15 @@ def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions,
         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(metric.entrez_gene_ids, min_genes) if keep]
+        metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes, strict=True) if keep]
 
         # determine which genes are confidently expressed
         top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns))
         top_func = k_over_a(top_samples, cut_off)
         top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func)
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes) if keep]
+        metric.high_confidence_entrez_gene_ids = [
+            gene for gene, keep in zip(metric.entrez_gene_ids, top_genes, strict=True) if keep
+        ]
 
     return metrics
 
@@ -697,8 +697,7 @@ async def _save_rnaseq_tests(
 async def _create_metadata_df(path: Path) -> pd.DataFrame:
     if path.suffix not in {".xls", ".xlsx"}:
         raise ValueError(
-            f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. "
-            f"Attempted to process: {path}"
+            f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. Attempted to process: {path}"
         )
     return pd.read_excel(path)
 
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 83eb504f..4f53962c 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -137,7 +137,7 @@ 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):
+    for gene_dir, strand_dir in zip(gene_counts_directories, strandedness_directories, strict=True):
         count_files = list(gene_dir.glob("*.tab"))
         strand_files = list(strand_dir.glob("*.txt"))
         if len(count_files) == 0:
@@ -358,8 +358,7 @@ async def _create_config_df(  # noqa: C901
                 layout = file.read().strip()
         elif len(layout_files) > 1:
             raise ValueError(
-                f"Multiple matching layout files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+                f"Multiple matching layout files for {label}, make sure there is only one copy for each replicate in COMO_input"
             )
 
         strand = "UNKNOWN"
@@ -374,8 +373,7 @@ async def _create_config_df(  # noqa: C901
                 strand = file.read().strip()
         elif len(strand_files) > 1:
             raise ValueError(
-                f"Multiple matching strandedness files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+                f"Multiple matching strandedness files for {label}, make sure there is only one copy for each replicate in COMO_input"
             )
 
         prep = "total"
@@ -388,15 +386,13 @@ async def _create_config_df(  # noqa: C901
                     raise ValueError(f"Prep method must be either 'total' or 'mrna' for {label}")
         elif len(prep_files) > 1:
             raise ValueError(
-                f"Multiple matching prep files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+                f"Multiple matching prep files for {label}, make sure there is only one copy for each replicate in COMO_input"
             )
 
         mean_fragment_size = 100
         if len(frag_files) == 0 and prep != RNAPrepMethod.TOTAL.value:
             logger.warning(
-                f"No fragment file found for {label}, using '100'. "
-                "You should define this if you are going to use downstream zFPKM normalization"
+                f"No fragment file found for {label}, using '100'. You should define this if you are going to use downstream zFPKM normalization"
             )
         elif len(frag_files) == 1:
             if layout == "single-end":
@@ -420,8 +416,7 @@ async def _create_config_df(  # noqa: C901
                     mean_fragment_size = sum(mean_fragment_sizes * library_sizes) / sum(library_sizes)
         elif len(frag_files) > 1:
             raise ValueError(
-                f"Multiple matching fragment files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+                f"Multiple matching fragment files for {label}, make sure there is only one copy for each replicate in COMO_input"
             )
 
         sample_names.append(f"{context_name}_{study_number}{rep_number}")
diff --git a/pyproject.toml b/pyproject.toml
index aa9fff60..624ea4d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,30 +19,27 @@ dependencies = [
     "plotly>=5.24.1",
     "pydantic-settings",
     "pydantic>=2.10.3",
-    "scanpy>=1.9.8",
+    "scanpy>=1.11.4",
     "scikit-learn>=1.5.2",
     "scipy>=1.7.3",
     "setuptools<60.0",
+    "statsmodels>=0.14",
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
 ]
 
 [project.optional-dependencies]
-gurobi = [ "gurobipy>=10.0.0" ]
-interactive =   [
-    "ipython>=8.0.0",
-    "jupyterlab>=4.3.2"
+gurobi = ["gurobipy>=10.0.0"]
+interactive = [
+    "ipython>=7.0.0",
+    "jupyterlab>=4.0.0",
 ]
 dev = [
-    "commitlint>=1.3.0",
     "pytest-asyncio>=0.24.0",
-    "pytest>=8.3.3",
-    "ruff>=0.8.0",
-    "hypothesis>=6.122.1",
     "pytest-cov>=6.0.0",
-    "commitizen>=4.1.0",
+    "pytest>=8.0.0",
+    "ruff>=0.8.0",
 ]
 
-
 [tool.hatch.version]
 path = "main/como/__init__.py"
 
@@ -53,11 +50,4 @@ packages = ["main/como"]
 allow-direct-references = true
 
 [tool.pytest.ini_options]
-pythonpath = [ "main/src" ]
-
-[tool.commitizen]
-name = "cz_conventional_commits"
-tag_format = "$version"
-version_scheme = "semver2"
-version_provider = "pep621"
-update_changelog_on_bump = true
\ No newline at end of file
+pythonpath = ["main/src"]
diff --git a/uv.lock b/uv.lock
index 3a417c7a..0dcdbd6e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,141 +1,166 @@
 version = 1
+revision = 3
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*'",
     "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
 ]
 
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 [[package]]
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 [[package]]
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+resolution-markers = [
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 [[package]]
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-    { name = "scipy", version = "1.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
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     { name = "pytest-cov" },
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