diff --git a/.flake8 b/.flake8
deleted file mode 100644
index 9326c76..0000000
--- a/.flake8
+++ /dev/null
@@ -1,6 +0,0 @@
-[flake8]
-ignore = N802, N803, N806, E731, W503, N815, E402, E203, F841, F811, C901, F401
-max-complexity = 7
-max-line-length = 120
-docstring-convention=pep257
-exclude = .git,__pycache__,build,docs/source/conf.py,example,*.ipynb,.venv
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index dd80685..1564d28 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,6 +1,7 @@
 name: Test & lint package & deploy documentation
 
 on:
+  workflow_dispatch:
   schedule:
     # run every Monday at 15:00 UTC
     - cron: "0 15 * * 1"
@@ -11,54 +12,49 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: true
       matrix:
         include:
-          - os: ubuntu-24.04
-            python: "3.12"
+          - os: ubuntu-latest
+            python: "3.13"
+
+    name: Python ${{ matrix.python }}
+
+    env:
+      OS: ${{ matrix.os }}
+      PYTHON: ${{ matrix.python }}
 
     steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v3
+      - uses: actions/checkout@v5
+      - name: Install the latest version of uv and setup Python
+        uses: astral-sh/setup-uv@v6
         with:
+          version: "latest"
           python-version: ${{ matrix.python-version }}
-      - uses: actions/cache@v3
-        with:
-          path: ${{ env.pythonLocation }}
-          key: ${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}
+          enable-cache: true
       - name: Install dependencies
         run: |
-          export FLIT_ROOT_INSTALL=1
-          python -m pip install --upgrade pip
-          python -m pip install flit
-          python -m flit install --deps production
-          python -m pip install biocframe dolomite dolomite-se summarizedexperiment
-          python -m pip install bandit mypy==1.15.0 ruff==0.11.2
-          python -m pip install coverage pytest sphinx sphinx-autoapi \
-            myst-parser pygments sphinx-autodoc-typehints sphinx-rtd-theme furo sphinx_design sphinx-copybutton \
-            sphinxcontrib-bibtex sphinxcontrib-mermaid nbsphinx pandoc
-          sudo apt-get update
-          sudo apt-get install -y pandoc
+          uv sync --all-extras
       - name: Lint with ruff
         run: |
-          ruff check --fix --exit-non-zero-on-fix pytximport
+          uv run ruff check --fix --exit-non-zero-on-fix pytximport
       - name: Format with ruff
         run: |
-          ruff format --check pytximport
+          uv run ruff format --check pytximport
       - name: Type check with mypy
         run: |
-          python -m mypy -p pytximport
+          uv run mypy -p pytximport
       - name: Check security with bandit
         run: |
-          python -m bandit --verbose --ini .bandit.ini -ll --recursive pytximport
+          uv run bandit --verbose --ini .bandit.ini -ll --recursive pytximport
       - name: Test with pytest
         run: |
-          coverage run -m pytest --maxfail=10
-          coverage xml
-      - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v4.0.1
+          uv run coverage run -m pytest --maxfail=10 -m "not gpu" -m "not no_github_ci"
+          uv run coverage xml
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v5
         with:
+          fail_ci_if_error: true
           token: ${{ secrets.CODECOV_TOKEN }}
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 785d219..57bde74 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -1,4 +1,4 @@
-name: Upload Python Package
+name: Publish Package
 
 on:
   release:
@@ -8,25 +8,25 @@ permissions:
   contents: read
 
 jobs:
-  deploy:
-
+  pypi-publish:
+    name: Upload release to PyPI
     runs-on: ubuntu-latest
+    environment: pypi
+    permissions:
+      contents: read
+      id-token: write
 
     steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python
-      uses: actions/setup-python@v3
-      with:
-        python-version: '3.11'
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install flit
-    - name: Build package
-      run: flit build
-    - name: Publish package
-      run: flit publish
-      env:
-        FLIT_USERNAME: '__token__'
-        FLIT_PASSWORD: ${{ secrets.FLIT_PASSWORD }}
-        FLIT_INDEX_URL: 'https://upload.pypi.org/legacy/'
+      - uses: actions/checkout@v5
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version-file: "pyproject.toml"
+      - name: Install the latest version of uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "latest"
+      - name: Build package
+        run: uv build
+      - name: Publish package
+        run: uv publish --trusted-publishing always
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3ffe464..93948ef 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,7 +2,7 @@ default_stages: [pre-commit, pre-push]
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v6.0.0
     hooks:
       - id: check-yaml
         stages: [pre-commit]
@@ -21,14 +21,16 @@ repos:
         stages: [pre-commit]
       - id: mixed-line-ending
         stages: [pre-commit]
+      - id: no-commit-to-branch
+        args: [--branch=main]
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.2
+    rev: v0.13.2
     hooks:
-      - id: ruff
-        args: [--select, I, --fix]
+      - id: ruff-check
+        args: [--select, "I,RUF022", --fix, --exit-non-zero-on-fix]
       - id: ruff-format
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.15.0
+    rev: "v1.18.2"
     hooks:
       - id: mypy
         stages: [pre-commit]
@@ -36,18 +38,17 @@ repos:
         pass_filenames: false
         args: [-p, pytximport]
   - repo: https://github.com/PyCQA/bandit
-    rev: 1.7.5
+    rev: 1.8.6
     hooks:
       - id: bandit
         stages: [pre-commit]
         args: [--verbose, --ini, .bandit.ini, -ll, --recursive, pytximport]
-  # run the unittests
   - repo: local
     hooks:
       - id: unittests
         stages: [pre-commit]
         name: unittests
-        entry: python -m coverage run -m pytest --maxfail=10
+        entry: uv run coverage run -m pytest --maxfail=10
         language: system
         types: [python]
         pass_filenames: false
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index e7852bd..78559c6 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -1,16 +1,18 @@
 version: 2
 build:
-  os: ubuntu-22.04
+  os: ubuntu-24.04
   tools:
-    python: "3.11"
+    python: "3.12"
+  jobs:
+    create_environment:
+      - asdf plugin add uv
+      - asdf install uv latest
+      - asdf global uv latest
+      - uv venv $READTHEDOCS_VIRTUALENV_PATH
+      - UV_PROJECT_ENVIRONMENT=$READTHEDOCS_VIRTUALENV_PATH uv sync --all-extras --link-mode=copy
+    install:
+      - "true"
 
 sphinx:
-   configuration: docs/source/conf.py
-   fail_on_warning: false
-
-python:
-  install:
-  - method: pip
-    path: .
-    extra_requirements:
-    - dev
+  configuration: docs/source/conf.py
+  fail_on_warning: false
diff --git a/Makefile b/Makefile
index 82105b2..abdfeb7 100755
--- a/Makefile
+++ b/Makefile
@@ -1,48 +1,33 @@
 .DEFAULT_GOAL := test
 
-#
-# Virtual environment setup
-# You may want to replace python3 with the path to your python3 executable,
-# e.g. the output of `pyenv which python3`when using pyenv
-#
-create-venv:
-	python3 -m venv '.venv'
-	echo "Don't forget to activate with 'source .venv/bin/activate'"
-
 #
 # Install requirements
 #
 install:
-	python3 -m pip install --upgrade pip
-	python3 -m pip install flit poetry
-	python3 -m flit install --deps production
+	uv sync
 
-install-dev: install
-	python3 -m pip install '.[dev]'
-	python3 -m certifi
+install-dev:
+	uv sync --all-extras
 	pre-commit install --hook-type pre-commit --hook-type pre-push
 	echo "Please also install pandoc to create the documentation."
 
 #
-# Checks & package upload
+# Checks
 #
 check: install-dev
-	ruff format --check pytximport
-	ruff check pytximport
-	mypy -p pytximport
-	bandit -ll --recursive pytximport
-
-upload: check
-	flit publish
+	uv run ruff format --check pytximport
+	uv run ruff check pytximport
+	uv run mypy -p pytximport
+	uv run bandit -ll --recursive pytximport
 
 #
 # Testing
 #
 unittest:
-	coverage run -m pytest --maxfail=10
+	uv run coverage run -m pytest --maxfail=10
 
 coverage-report: unittest
-	coverage report
-	coverage html
+	uv run coverage report
+	uv run coverage html
 
 test: check unittest
diff --git a/README.md b/README.md
index 7b0a284..e0b8b26 100755
--- a/README.md
+++ b/README.md
@@ -29,12 +29,16 @@ The recommended way to install `pytximport` is through Bioconda:
 mamba install -c bioconda pytximport
 ```
 
-`pytximport` can also be installed via pip:
+`pytximport` can also be installed via `pip` or added to your project via `uv`:
 
 ```bash
 pip install pytximport
 ```
 
+```bash
+uv add pytximport
+```
+
 While not required, we recommend users also install `pyarrow` for faster import of tab-separated value-based quantification files:
 
 ```bash
@@ -120,6 +124,7 @@ Generally, outputs from `pytximport` correspond to the outputs from `tximport` w
 While the outputs are identical within floating point tolerance for the same configuration, there remain some differences between the packages:
 
 Features unique to `pytximport`:
+
 - Generating transcript-to-gene maps, either from a BioMart server or an `annotation.gtf` file. Use `create_transcript_gene_map` or `create_transcript_gene_map_from_annotation` from `pytximport.utils`.
 - Command line interface. Type `pytximport --help` into your terminal to explore all options.
 - `AnnData`-support, enabling seamless integration with the `scverse`.
@@ -129,6 +134,7 @@ Features unique to `pytximport`:
 - Post-hoc biotype-filtering using `pytximport.utils.filter_by_biotype`.
 
 Features unique to `tximport`:
+
 - Alevin single-cell RNA-seq data support
 
 Argument order and argument defaults may differ between the implementations.
@@ -142,8 +148,7 @@ To set up `pytximport` for development on your machine, we recommend to git clon
 ```bash
 git clone --depth 1 -b dev https://github.com/complextissue/pytximport.git
 cd pytximport
-pyenv local 3.12
-make create-venv
+uv venv --python 3.13
 source .venv/source/activate
 make install-dev
 ```
diff --git a/docs/source/example.ipynb b/docs/source/example.ipynb
index 625c08d..0492ef8 100644
--- a/docs/source/example.ipynb
+++ b/docs/source/example.ipynb
@@ -4,21 +4,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Vignette"
+    "# Vignette\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This vignette extends the [vignette for the R-version of tximport](https://bioconductor.org/packages/release/bioc/vignettes/tximport/inst/doc/tximport.html). If you are unfamiliar with `tximport` or curious about the motivation behind it, please check it out."
+    "This vignette extends the [vignette for the R-version of tximport](https://bioconductor.org/packages/release/bioc/vignettes/tximport/inst/doc/tximport.html). If you are unfamiliar with `tximport` or curious about the motivation behind it, please check it out.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you are looking for a full-featured end-to-end workflow for Pythonic bulk RNA-sequencing analysis, check out our [Snakemake workflow](https://github.com/complextissue/snakemake-bulk-rna-seq-workflow/) based on pytximport."
+    "If you are looking for a full-featured end-to-end workflow for Pythonic bulk RNA-sequencing analysis, check out our [Snakemake workflow](https://github.com/complextissue/snakemake-bulk-rna-seq-workflow/) based on pytximport.\n"
    ]
   },
   {
@@ -27,21 +27,21 @@
    "source": [
     "## Creating your transcript to gene map\n",
     "\n",
-    "Here, we will show you how to generate a transcript-to-gene mapping based on the Ensembl reference or a gene transfer format file."
+    "Here, we will show you how to generate a transcript-to-gene mapping based on the Ensembl reference or a gene transfer format file.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Build it from Ensembl"
+    "### Build it from Ensembl\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This example requires `pybiomart` which is installed together with `pytximport`. Providing a host is optional, for a list of available archives that correspond to Ensembl releases, please consult [https://www.ensembl.org/info/website/archives/index.html](https://www.ensembl.org/info/website/archives/index.html). By default, the transcript ids will be mapped to the `ensembl_gene_id` field. If you prefer to use gene names, choose `external_gene_name`. Be aware that not all proposed transcripts have been assigned a name yet and thus will not be included if you use gene names. The first time you run this function, it may take a few seconds to download the data."
+    "This example requires `pybiomart` which is installed together with `pytximport`. Providing a host is optional, for a list of available archives that correspond to Ensembl releases, please consult [https://www.ensembl.org/info/website/archives/index.html](https://www.ensembl.org/info/website/archives/index.html). By default, the transcript ids will be mapped to the `ensembl_gene_id` field. If you prefer to use gene names, choose `external_gene_name`. Be aware that not all proposed transcripts have been assigned a name yet and thus will not be included if you use gene names. The first time you run this function, it may take a few seconds to download the data.\n"
    ]
   },
   {
@@ -211,7 +211,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also provide a list of mapping targets as the target_field argument. For example, to map transcripts to both gene names and gene biotypes, you can use the following code:"
+    "You can also provide a list of mapping targets as the target_field argument. For example, to map transcripts to both gene names and gene biotypes, you can use the following code:\n"
    ]
   },
   {
@@ -247,33 +247,33 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>ENST00000673100</td>\n",
-       "      <td>LINC03015</td>\n",
+       "      <th>94</th>\n",
+       "      <td>ENST00000632454</td>\n",
+       "      <td>LINC01784</td>\n",
        "      <td>lncRNA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>ENST00000673009</td>\n",
-       "      <td>LINC03015</td>\n",
+       "      <th>118</th>\n",
+       "      <td>ENST00000707308</td>\n",
+       "      <td>LINC02688</td>\n",
        "      <td>lncRNA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>ENST00000671859</td>\n",
-       "      <td>LINC03015</td>\n",
+       "      <th>119</th>\n",
+       "      <td>ENST00000707309</td>\n",
+       "      <td>LINC02688</td>\n",
        "      <td>lncRNA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>61</th>\n",
-       "      <td>ENST00000673474</td>\n",
-       "      <td>LINC03015</td>\n",
+       "      <th>164</th>\n",
+       "      <td>ENST00000631340</td>\n",
+       "      <td>LINC02735</td>\n",
        "      <td>lncRNA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>ENST00000671974</td>\n",
-       "      <td>LINC03015</td>\n",
+       "      <th>181</th>\n",
+       "      <td>ENST00000672718</td>\n",
+       "      <td>AGAP12P</td>\n",
        "      <td>lncRNA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -281,12 +281,12 @@
        "</div>"
       ],
       "text/plain": [
-       "      transcript_id    gene_id gene_biotype\n",
-       "58  ENST00000673100  LINC03015       lncRNA\n",
-       "59  ENST00000673009  LINC03015       lncRNA\n",
-       "60  ENST00000671859  LINC03015       lncRNA\n",
-       "61  ENST00000673474  LINC03015       lncRNA\n",
-       "62  ENST00000671974  LINC03015       lncRNA"
+       "       transcript_id    gene_id gene_biotype\n",
+       "94   ENST00000632454  LINC01784       lncRNA\n",
+       "118  ENST00000707308  LINC02688       lncRNA\n",
+       "119  ENST00000707309  LINC02688       lncRNA\n",
+       "164  ENST00000631340  LINC02735       lncRNA\n",
+       "181  ENST00000672718    AGAP12P       lncRNA"
       ]
      },
      "execution_count": 3,
@@ -309,7 +309,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Use a gene transfer format file"
+    "### Use a gene transfer format file\n"
    ]
   },
   {
@@ -318,7 +318,7 @@
    "source": [
     "If you already have an annotation file in .gtf format (e.g. from the GENCODE or Ensembl references), you can use the `create_transcript_gene_map_from_annotation` function contained in `pytximport.utils` to generate a transcript to gene map. Using an annotation file is considered best practice, as it ensures that your annotation and alignment reference are the same if you download them from the same source, e.g., an Ensembl release.\n",
     "\n",
-    "Set `target_field` to `gene_name` to map transcript ids to gene names instead of gene ids. If the transcript does not have a corresponding gene name in the annotation file, it will be mapped to the gene id."
+    "Set `target_field` to `gene_name` to map transcript ids to gene names instead of gene ids. If the transcript does not have a corresponding gene name in the annotation file, it will be mapped to the gene id.\n"
    ]
   },
   {
@@ -409,14 +409,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Map transcript names to gene names"
+    "### Map transcript names to gene names\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also optionally map transcript names to either gene names or gene ids if your data requires it."
+    "You can also optionally map transcript names to either gene names or gene ids if your data requires it.\n"
    ]
   },
   {
@@ -508,12 +508,12 @@
    "source": [
     "## Importing transcript quantification files\n",
     "\n",
-    "You can easily import quantification files from tools like `salmon` with `pytximport`."
+    "You can easily import quantification files from tools like `salmon` with `pytximport`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -532,7 +532,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Reading quantification files: 2it [00:00, 180.15it/s]\n"
+      "Reading quantification files: 2it [00:00, 101.58it/s]\n"
      ]
     },
     {
@@ -553,39 +553,86 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "html[data-theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -608,11 +655,18 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
        ".xr-obj-type {\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
@@ -621,6 +675,8 @@
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -630,10 +686,13 @@
        ".xr-section-item input {\n",
        "  display: inline-block;\n",
        "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -642,7 +701,7 @@
        "}\n",
        "\n",
        ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0);\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label:hover {\n",
@@ -666,7 +725,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -677,7 +736,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -687,7 +746,6 @@
        ".xr-section-summary,\n",
        ".xr-section-inline-details {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -697,6 +755,7 @@
        ".xr-section-details {\n",
        "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
@@ -704,6 +763,32 @@
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
        ".xr-array-wrap {\n",
        "  grid-column: 1 / -1;\n",
        "  display: grid;\n",
@@ -749,15 +834,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -774,7 +859,9 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -785,6 +872,7 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -834,8 +922,15 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -848,6 +943,12 @@
        "  float: right;\n",
        "}\n",
        "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -907,6 +1008,14 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 28kB\n",
        "Dimensions:    (gene_id: 496, file: 2, file_path: 2)\n",
        "Coordinates:\n",
@@ -916,10 +1025,10 @@
        "Data variables:\n",
        "    abundance  (gene_id, file) float64 8kB 0.08291 0.0 0.09854 ... 0.4618 0.0\n",
        "    counts     (gene_id, file) float64 8kB 1.005 0.0 1.086 ... 1.957 6.208 0.0\n",
-       "    length     (gene_id, file) float64 8kB 509.1 509.1 445.8 ... 564.6 564.6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-89af187f-cc08-404c-a45f-52f666c47f4f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-89af187f-cc08-404c-a45f-52f666c47f4f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>gene_id</span>: 496</li><li><span>file</span>: 2</li><li><span class='xr-has-index'>file_path</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9c4f6ba9-01e7-4434-8909-4817b833f16a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c4f6ba9-01e7-4434-8909-4817b833f16a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gene_id</span></div><div class='xr-var-dims'>(gene_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;ENSMUSG00000083355&#x27; ... &#x27;ENSMUS...</div><input id='attrs-1778d46d-ded3-4f01-98ae-e49bc9b7b4fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1778d46d-ded3-4f01-98ae-e49bc9b7b4fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f4b27f1-f37e-4e67-9c34-bec568a01d7d' class='xr-var-data-in' type='checkbox'><label for='data-2f4b27f1-f37e-4e67-9c34-bec568a01d7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ENSMUSG00000083355&#x27;, &#x27;ENSMUSG00000029863&#x27;, &#x27;ENSMUSG00000049960&#x27;, ...,\n",
+       "    length     (gene_id, file) float64 8kB 509.1 509.1 445.8 ... 564.6 564.6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dfe7db9b-b21b-4e23-88f1-f254f8a83bf7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dfe7db9b-b21b-4e23-88f1-f254f8a83bf7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>gene_id</span>: 496</li><li><span>file</span>: 2</li><li><span class='xr-has-index'>file_path</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c08af45-941c-4c96-bd07-610a550b5cf4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c08af45-941c-4c96-bd07-610a550b5cf4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gene_id</span></div><div class='xr-var-dims'>(gene_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;ENSMUSG00000083355&#x27; ... &#x27;ENSMUS...</div><input id='attrs-9a528865-2110-40e7-a821-d69144221354' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a528865-2110-40e7-a821-d69144221354' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d227d2b-cf8d-47d3-b415-36661f9e62fd' class='xr-var-data-in' type='checkbox'><label for='data-8d227d2b-cf8d-47d3-b415-36661f9e62fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ENSMUSG00000083355&#x27;, &#x27;ENSMUSG00000029863&#x27;, &#x27;ENSMUSG00000049960&#x27;, ...,\n",
        "       &#x27;ENSMUSG00000026083&#x27;, &#x27;ENSMUSG00000014496&#x27;, &#x27;ENSMUSG00000067713&#x27;],\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>file_path</span></div><div class='xr-var-dims'>(file_path)</div><div class='xr-var-dtype'>&lt;U43</div><div class='xr-var-preview xr-preview'>&#x27;../../test/data/salmon/multiple...</div><input id='attrs-d77552a1-19f6-438f-88ba-6bd2131fdead' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d77552a1-19f6-438f-88ba-6bd2131fdead' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef49901e-3a0c-41c6-87f7-d9ac38a24b77' class='xr-var-data-in' type='checkbox'><label for='data-ef49901e-3a0c-41c6-87f7-d9ac38a24b77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;../../test/data/salmon/multiple/Sample_1.sf&#x27;,\n",
-       "       &#x27;../../test/data/salmon/multiple/Sample_2.sf&#x27;], dtype=&#x27;&lt;U43&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-159a518e-8945-4f51-b326-b4d50f9204ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-159a518e-8945-4f51-b326-b4d50f9204ce' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abundance</span></div><div class='xr-var-dims'>(gene_id, file)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08291 0.0 0.09854 ... 0.4618 0.0</div><input id='attrs-d201ef28-b9aa-4b06-8ddd-f2e3b53290a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d201ef28-b9aa-4b06-8ddd-f2e3b53290a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8239a87e-39c7-4e57-b7fb-cd4da1e9e1df' class='xr-var-data-in' type='checkbox'><label for='data-8239a87e-39c7-4e57-b7fb-cd4da1e9e1df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[8.29050000e-02, 0.00000000e+00],\n",
+       "      shape=(496,), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>file_path</span></div><div class='xr-var-dims'>(file_path)</div><div class='xr-var-dtype'>&lt;U43</div><div class='xr-var-preview xr-preview'>&#x27;../../test/data/salmon/multiple...</div><input id='attrs-c418aa52-37ba-4f9c-9225-afb196a2f569' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c418aa52-37ba-4f9c-9225-afb196a2f569' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ee4bab-ca05-4558-a489-a4df29cfdd27' class='xr-var-data-in' type='checkbox'><label for='data-92ee4bab-ca05-4558-a489-a4df29cfdd27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;../../test/data/salmon/multiple/Sample_1.sf&#x27;,\n",
+       "       &#x27;../../test/data/salmon/multiple/Sample_2.sf&#x27;], dtype=&#x27;&lt;U43&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5de7584b-5e8f-4c84-b401-6a4563a5e3a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5de7584b-5e8f-4c84-b401-6a4563a5e3a0' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abundance</span></div><div class='xr-var-dims'>(gene_id, file)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08291 0.0 0.09854 ... 0.4618 0.0</div><input id='attrs-9b72bda5-326f-4c67-95e6-fcb7f7d3c77b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b72bda5-326f-4c67-95e6-fcb7f7d3c77b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-249aaa1c-8a52-4c6f-a6a0-c3e8bc595d53' class='xr-var-data-in' type='checkbox'><label for='data-249aaa1c-8a52-4c6f-a6a0-c3e8bc595d53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[8.29050000e-02, 0.00000000e+00],\n",
        "       [9.85390000e-02, 9.29614000e-01],\n",
        "       [3.43083300e+00, 2.15451600e+00],\n",
        "       [0.00000000e+00, 1.89568000e-01],\n",
@@ -959,7 +1068,7 @@
        "       [0.00000000e+00, 0.00000000e+00],\n",
        "       [0.00000000e+00, 1.35071000e-01],\n",
        "       [1.35126000e-01, 3.30816000e-01],\n",
-       "       [4.61785000e-01, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>counts</span></div><div class='xr-var-dims'>(gene_id, file)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.005 0.0 1.086 ... 1.957 6.208 0.0</div><input id='attrs-70fa0c56-9226-45ca-8eaf-997b016f5a4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70fa0c56-9226-45ca-8eaf-997b016f5a4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68bf1a46-cfa2-4f3f-a149-4c738611dca0' class='xr-var-data-in' type='checkbox'><label for='data-68bf1a46-cfa2-4f3f-a149-4c738611dca0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.00505049e+00, 0.00000000e+00],\n",
+       "       [4.61785000e-01, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>counts</span></div><div class='xr-var-dims'>(gene_id, file)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.005 0.0 1.086 ... 1.957 6.208 0.0</div><input id='attrs-6b729763-8d6a-4279-b129-a8378addb33e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b729763-8d6a-4279-b129-a8378addb33e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3024f63-7658-4c6b-b637-b7e2793fa53a' class='xr-var-data-in' type='checkbox'><label for='data-b3024f63-7658-4c6b-b637-b7e2793fa53a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.00505049e+00, 0.00000000e+00],\n",
        "       [1.08620160e+00, 8.01851033e+00],\n",
        "       [1.93498734e+02, 9.50860978e+01],\n",
        "       [0.00000000e+00, 1.22938529e+01],\n",
@@ -999,7 +1108,7 @@
        "       [0.00000000e+00, 0.00000000e+00],\n",
        "       [0.00000000e+00, 1.12011948e+00],\n",
        "       [1.02177262e+00, 1.95744965e+00],\n",
-       "       [6.20806094e+00, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length</span></div><div class='xr-var-dims'>(gene_id, file)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>509.1 509.1 445.8 ... 564.6 564.6</div><input id='attrs-64089eab-352f-4363-8f8b-11d6aa107524' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64089eab-352f-4363-8f8b-11d6aa107524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e81a5c6a-7d92-4917-b111-4287e2fb3203' class='xr-var-data-in' type='checkbox'><label for='data-e81a5c6a-7d92-4917-b111-4287e2fb3203' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.09113000e+02, 5.09113000e+02],\n",
+       "       [6.20806094e+00, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length</span></div><div class='xr-var-dims'>(gene_id, file)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>509.1 509.1 445.8 ... 564.6 564.6</div><input id='attrs-c446619b-110d-46f9-9e2c-f3bd007d6127' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c446619b-110d-46f9-9e2c-f3bd007d6127' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bb92f3c-1def-4e12-8b74-f26a446c9a15' class='xr-var-data-in' type='checkbox'><label for='data-1bb92f3c-1def-4e12-8b74-f26a446c9a15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.09113000e+02, 5.09113000e+02],\n",
        "       [4.45830000e+02, 4.80017194e+02],\n",
        "       [2.36868400e+03, 2.36844900e+03],\n",
        "       [3.48049700e+03, 3.48049700e+03],\n",
@@ -1039,16 +1148,7 @@
        "       [1.46500000e+02, 1.46500000e+02],\n",
        "       [4.45062000e+02, 4.45062000e+02],\n",
        "       [3.11983000e+02, 3.23132000e+02],\n",
-       "       [5.64577000e+02, 5.64577000e+02]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e8a19fc-fc76-4f3f-a540-a06dffad9a1d' class='xr-section-summary-in' type='checkbox'  ><label for='section-3e8a19fc-fc76-4f3f-a540-a06dffad9a1d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>gene_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7cf804c-90d2-4797-a23d-22f4ddbf7ccb' class='xr-index-data-in' type='checkbox'/><label for='index-b7cf804c-90d2-4797-a23d-22f4ddbf7ccb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ENSMUSG00000083355&#x27;, &#x27;ENSMUSG00000029863&#x27;, &#x27;ENSMUSG00000049960&#x27;,\n",
-       "       &#x27;ENSMUSG00000031644&#x27;, &#x27;ENSMUSG00000021451&#x27;, &#x27;ENSMUSG00000088668&#x27;,\n",
-       "       &#x27;ENSMUSG00000028970&#x27;, &#x27;ENSMUSG00000067377&#x27;, &#x27;ENSMUSG00000068748&#x27;,\n",
-       "       &#x27;ENSMUSG00000022878&#x27;,\n",
-       "       ...\n",
-       "       &#x27;ENSMUSG00000001520&#x27;, &#x27;ENSMUSG00000085227&#x27;, &#x27;ENSMUSG00000109089&#x27;,\n",
-       "       &#x27;ENSMUSG00000033530&#x27;, &#x27;ENSMUSG00000022538&#x27;, &#x27;ENSMUSG00000050379&#x27;,\n",
-       "       &#x27;ENSMUSG00000074139&#x27;, &#x27;ENSMUSG00000026083&#x27;, &#x27;ENSMUSG00000014496&#x27;,\n",
-       "       &#x27;ENSMUSG00000067713&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;gene_id&#x27;, length=496))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>file_path</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d32a96f7-71a3-43dc-bb65-e8d9eef08886' class='xr-index-data-in' type='checkbox'/><label for='index-d32a96f7-71a3-43dc-bb65-e8d9eef08886' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;../../test/data/salmon/multiple/Sample_1.sf&#x27;, &#x27;../../test/data/salmon/multiple/Sample_2.sf&#x27;], dtype=&#x27;object&#x27;, name=&#x27;file_path&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7505bdda-5c06-4490-86df-d08098b54531' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7505bdda-5c06-4490-86df-d08098b54531' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       [5.64577000e+02, 5.64577000e+02]])</pre></div></li></ul></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 28kB\n",
@@ -1086,14 +1186,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exporting transcript-level count estimates"
+    "## Exporting transcript-level count estimates\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also export the transformed transcript counts directly for transcript-level analysis."
+    "You can also export the transformed transcript counts directly for transcript-level analysis.\n"
    ]
   },
   {
@@ -1105,7 +1205,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Reading quantification files: 1it [00:00, 293.78it/s]\n"
+      "Reading quantification files: 1it [00:00, 207.47it/s]\n"
      ]
     },
     {
@@ -1136,7 +1236,7 @@
    "metadata": {},
    "source": [
     "Note that the example above works without a transcript to gene map. If you want to use the transcript names instead of the transcript ids, you can optionally use the `replace_transcript_ids_with_names` function together with a transcript id to transcript name map.\n",
-    "We can use the `create_transcript_gene_map` function to create a map between transcript ids and transcript names, too."
+    "We can use the `create_transcript_gene_map` function to create a map between transcript ids and transcript names, too.\n"
    ]
   },
   {
@@ -1307,7 +1407,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The same data summarized to genes:"
+    "The same data summarized to genes:\n"
    ]
   },
   {
@@ -1319,7 +1419,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Reading quantification files: 1it [00:00, 105.05it/s]\n"
+      "Reading quantification files: 1it [00:00, 231.92it/s]\n"
      ]
     },
     {
@@ -1404,21 +1504,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that the top transcript corresponds to the top expressed gene in this case."
+    "Note that the top transcript corresponds to the top expressed gene in this case.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exporting AnnData files"
+    "## Exporting AnnData files\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`pytximport` integrates well with other packages from the `scverse` through its `AnnData` export option."
+    "`pytximport` integrates well with other packages from the `scverse` through its `AnnData` export option.\n"
    ]
   },
   {
@@ -1430,7 +1530,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Reading quantification files: 1it [00:00, 366.57it/s]\n"
+      "Reading quantification files: 1it [00:00, 309.11it/s]\n"
      ]
     },
     {
@@ -1463,14 +1563,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exporting SummarizedExperiment files"
+    "## Exporting SummarizedExperiment files\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Experiment support for SummarizedExperiment files is available through the [BiocPy](https://github.com/biocpy) ecosystem (unaffiliated). While not part of the core functionality of `pytximport`, this output type/format may be useful for interacting with other R software packages. The optional dependencies necessary to support SummarizedExperiment files can be installed via `pip install pytximport[biocpy]`."
+    "Experiment support for SummarizedExperiment files is available through the [BiocPy](https://github.com/biocpy) ecosystem (unaffiliated). While not part of the core functionality of `pytximport`, this output type/format may be useful for interacting with other R software packages. The optional dependencies necessary to support SummarizedExperiment files can be installed via `pip install pytximport[biocpy]`.\n"
    ]
   },
   {
@@ -1482,7 +1582,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Reading quantification files: 1it [00:00, 357.27it/s]\n",
+      "Reading quantification files: 1it [00:00, 291.19it/s]\n",
       "WARNING:root:Support for the SummarizedExperiment output type is experimental.\n"
      ]
     },
@@ -1518,7 +1618,7 @@
    "source": [
     "## Use from the command line\n",
     "\n",
-    "You can run `pytximport` from the command line, too. Available options can be viewed via the `pytximport --help` command."
+    "You can run `pytximport` from the command line, too. Available options can be viewed via the `pytximport --help` command.\n"
    ]
   },
   {
@@ -1530,17 +1630,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2024-11-26 15:53:23,385: Starting the import.\n",
-      "Reading quantification files: 1it [00:00, 94.06it/s]\n",
-      "2024-11-26 15:53:23,666: Converting transcript-level expression to gene-level expression.\n",
-      "2024-11-26 15:53:23,722: Matching gene_ids.\n",
-      "2024-11-26 15:53:23,747: Creating gene abundance.\n",
-      "2024-11-26 15:53:23,927: Creating gene counts.\n",
-      "2024-11-26 15:53:23,927: Creating lengths.\n",
-      "2024-11-26 15:53:23,927: Replacing missing lengths.\n",
-      "2024-11-26 15:53:23,932: Creating gene expression dataset.\n",
-      "2024-11-26 15:53:23,934: Saving the gene-level expression to: ../../test/data/salmon/quant.h5ad.\n",
-      "2024-11-26 15:53:23,939: Finished the import in 0.55 seconds.\n"
+      "2025-10-23 23:53:31,869: Starting the import.\n",
+      "Reading quantification files: 1it [00:00, 65.95it/s]\n",
+      "2025-10-23 23:53:32,208: Converting transcript-level expression to gene-level expression.\n",
+      "2025-10-23 23:53:32,269: Matching gene_ids.\n",
+      "2025-10-23 23:53:32,298: Creating gene abundance.\n",
+      "2025-10-23 23:53:32,520: Creating gene counts.\n",
+      "2025-10-23 23:53:32,520: Creating lengths.\n",
+      "2025-10-23 23:53:32,521: Replacing missing lengths.\n",
+      "2025-10-23 23:53:32,528: Creating gene expression dataset.\n",
+      "2025-10-23 23:53:32,530: Saving the gene-level expression to: ../../test/data/salmon/quant.h5ad.\n",
+      "2025-10-23 23:53:32,539: Finished the import in 0.67 seconds.\n"
      ]
     }
    ],
@@ -1552,7 +1652,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also create a transcript-to-gene mapping via the `pytximport create-map` command."
+    "You can also create a transcript-to-gene mapping via the `pytximport create-map` command.\n"
    ]
   },
   {
@@ -1564,9 +1664,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2024-11-26 15:53:25,049: Creating a transcript-to-gene mapping file.\n",
-      "2024-11-26 15:53:25,057: Created the transcript-to-gene mapping file. Saving the file...\n",
-      "2024-11-26 15:53:25,059: Saved the transcript-to-gene mapping file to ./transcript_gene_map.tsv.\n"
+      "2025-10-23 23:53:33,997: Creating a transcript-to-gene mapping file.\n",
+      "2025-10-23 23:53:34,009: Created the transcript-to-gene mapping file. Saving the file...\n",
+      "2025-10-23 23:53:34,012: Saved the transcript-to-gene mapping file to ./transcript_gene_map.tsv.\n"
      ]
     }
    ],
@@ -1578,14 +1678,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Inferential replicates"
+    "## Inferential replicates\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`pytximport` can handle bootstraping replicates provided by `salmon` and `kallisto`. When `inferential_replicate_transformer` is set, the provided function is used to recalculate the counts and abundances for each sample based on the bootstraps."
+    "`pytximport` can handle bootstraping replicates provided by `salmon` and `kallisto`. When `inferential_replicate_transformer` is set, the provided function is used to recalculate the counts and abundances for each sample based on the bootstraps.\n"
    ]
   },
   {
@@ -1597,14 +1697,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Reading quantification files: 4it [00:00,  5.80it/s]\n",
-      "WARNING:root:Not all transcripts are present in the mapping. 31380 out of 253181 missing. Removing the missing transcripts.\n"
+      "Reading quantification files: 4it [00:01,  2.11it/s]\n",
+      "WARNING:root:Not all transcripts are present in the mapping. 29618 out of 253181 missing. Removing the missing transcripts.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "AnnData object with n_obs × n_vars = 4 × 40768\n",
+       "AnnData object with n_obs × n_vars = 4 × 41127\n",
        "    uns: 'counts_from_abundance', 'inferential_replicates'\n",
        "    obsm: 'length', 'abundance', 'variance'"
       ]
@@ -1636,14 +1736,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Biotype filtering"
+    "## Biotype filtering\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For some use cases, it may be of interest to restrict the results to certain gene_biotypes, e.g., protein-coding only. First, generate a transcript-to-gene mapping from an annotation GTF file that includes the gene biotype."
+    "For some use cases, it may be of interest to restrict the results to certain gene_biotypes, e.g., protein-coding only. First, generate a transcript-to-gene mapping from an annotation GTF file that includes the gene biotype.\n"
    ]
   },
   {
@@ -1738,7 +1838,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then, import quantification files using the transcript-to-gene mapping and filter the counts by biotype."
+    "Then, import quantification files using the transcript-to-gene mapping and filter the counts by biotype.\n"
    ]
   },
   {
@@ -1750,7 +1850,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Reading quantification files: 1it [00:00, 19.53it/s]\n",
+      "Reading quantification files: 1it [00:00,  2.60it/s]\n",
       "WARNING:root:Not all transcripts are present in the mapping. 253161 out of 253181 missing. Removing the missing transcripts.\n"
      ]
     },
@@ -1792,7 +1892,7 @@
    "source": [
     "## Downstream analysis with PyDESeq2\n",
     "\n",
-    "The output from `pytximport` can easily be used for downstream analysis with `PyDESeq2`. For more information on `PyDESeq2`, please consult its [documentation](https://pydeseq2.readthedocs.io/en/latest/)."
+    "The output from `pytximport` can easily be used for downstream analysis with `PyDESeq2`. For more information on `PyDESeq2`, please consult its [documentation](https://pydeseq2.readthedocs.io/en/latest/).\n"
    ]
   },
   {
@@ -1801,22 +1901,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
+      "/Users/malte/pytximport/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
    ],
-   "source": [
-    "%pip install pydeseq2 numba==0.60.0 decoupler adjustText omnipath tqdm ipywidgets seaborn -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "import decoupler as dc\n",
     "from pydeseq2.dds import DeseqDataSet\n",
@@ -1828,31 +1920,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Load the .csv file generated by `pytximport` via the `output_path` argument or create it directly from the output of `pytximport`. In this case, we are working with the salmon quantification files from a public bulk RNA sequencing dataset: [Podocyte injury in Fabry nephropathy](https://www.ebi.ac.uk/ena/browser/view/PRJNA773084)"
+    "Load the .csv file generated by `pytximport` via the `output_path` argument or create it directly from the output of `pytximport`. In this case, we are working with the salmon quantification files from a public bulk RNA sequencing dataset: [Podocyte injury in Fabry nephropathy](https://www.ebi.ac.uk/ena/browser/view/PRJNA773084)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Round count estimates (required by PyDESeq2) and add the corresponding metadata."
+    "Round count estimates (required by PyDESeq2) and add the corresponding metadata.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AnnData object with n_obs × n_vars = 4 × 40768\n",
+       "AnnData object with n_obs × n_vars = 4 × 41127\n",
        "    obs: 'condition'\n",
        "    uns: 'counts_from_abundance', 'inferential_replicates'\n",
        "    obsm: 'length', 'abundance', 'variance'"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1867,24 +1959,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Filter genes with low counts out."
+    "Filter genes with low counts out.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AnnData object with n_obs × n_vars = 4 × 14714\n",
+       "AnnData object with n_obs × n_vars = 4 × 14776\n",
        "    obs: 'condition'\n",
        "    uns: 'counts_from_abundance', 'inferential_replicates'\n",
        "    obsm: 'length', 'abundance', 'variance'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1898,18 +1990,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now perform your `PyDESeq2` analysis."
+    "Now perform your `PyDESeq2` analysis.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "dds = DeseqDataSet(\n",
     "    adata=result,\n",
-    "    design_factors=\"condition\",\n",
+    "    design=\"~condition\",\n",
     "    refit_cooks=True,\n",
     "    inference=DefaultInference(n_cpus=8),\n",
     "    quiet=True,\n",
@@ -1918,14 +2010,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/au734063/Documents/code/pytximport-publish/pytximport/.venv/lib/python3.12/site-packages/pydeseq2/dds.py:490: UserWarning: As the residual degrees of freedom is less than 3, the distribution of log dispersions is especially asymmetric and likely to be poorly estimated by the MAD.\n",
+      "/Users/malte/pytximport/.venv/lib/python3.13/site-packages/pydeseq2/dds.py:541: UserWarning: As the residual degrees of freedom is less than 3, the distribution of log dispersions is especially asymmetric and likely to be poorly estimated by the MAD.\n",
       "  self.fit_dispersion_prior()\n"
      ]
     }
@@ -1936,32 +2028,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "stat_result = DeseqStats(dds, contrast=(\"condition\", \"Disease\", \"Control\"), quiet=True)"
+    "stat_result = DeseqStats(dds, contrast=[\"condition\", \"Disease\", \"Control\"], quiet=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%capture\n",
     "stat_result.summary()\n",
-    "stat_result.lfc_shrink()"
+    "stat_result.lfc_shrink(coeff=\"condition[T.Disease]\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -1971,11 +2062,11 @@
     }
    ],
    "source": [
-    "dc.plot_volcano_df(\n",
+    "dc.pl.volcano(\n",
     "    stat_result.results_df,\n",
     "    x=\"log2FoldChange\",\n",
     "    y=\"padj\",\n",
-    "    sign_thr=0.01,\n",
+    "    thr_sign=0.01,\n",
     "    top=20,\n",
     "    figsize=(10, 5),\n",
     ")"
@@ -1983,19 +2074,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running ulm on mat with 1 samples and 14714 targets for 660 sources.\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x300 with 2 Axes>"
       ]
@@ -2005,37 +2089,37 @@
     }
    ],
    "source": [
-    "collectri = dc.get_collectri(organism=\"human\", split_complexes=False)\n",
-    "mat = stat_result.results_df[[\"stat\"]].T.rename(index={\"stat\": \"disease.vs.control\"})\n",
-    "tf_acts, tf_pvals = dc.run_ulm(mat=mat, net=collectri, verbose=True)\n",
-    "dc.plot_barplot(acts=tf_acts, contrast=\"disease.vs.control\", top=10, vertical=False, figsize=(5, 3))"
+    "collectri = dc.op.collectri(organism=\"human\", remove_complexes=False)\n",
+    "data = stat_result.results_df[[\"stat\"]].T.rename(index={\"stat\": \"disease.vs.control\"})\n",
+    "tf_acts, tf_pvals = dc.mt.ulm(data=data, net=collectri, tmin=5)\n",
+    "dc.pl.barplot(data=tf_acts, name=\"disease.vs.control\", top=10, vertical=True, figsize=(5, 3))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also evaluate known pathways."
+    "We can also evaluate known pathways.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
-    "progeny = dc.get_progeny(top=500)\n",
-    "pathway_acts, pathway_pvals = dc.run_mlm(mat=mat, net=progeny, min_n=5)"
+    "progeny = dc.op.progeny(organism=\"human\", top=500)\n",
+    "pathway_acts, pathway_pvals = dc.mt.ulm(data=data, net=progeny, tmin=5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 500x300 with 2 Axes>"
       ]
@@ -2045,11 +2129,11 @@
     }
    ],
    "source": [
-    "dc.plot_barplot(\n",
-    "    acts=pathway_acts,\n",
-    "    contrast=\"disease.vs.control\",\n",
+    "dc.pl.barplot(\n",
+    "    data=pathway_acts,\n",
+    "    name=\"disease.vs.control\",\n",
     "    top=40,\n",
-    "    vertical=False,\n",
+    "    vertical=True,\n",
     "    figsize=(5, 3),\n",
     ")"
    ]
@@ -2058,13 +2142,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Please refer to the `PyDESeq2` and the `decoupler` documentation for additional analyses."
+    "Please refer to the `PyDESeq2` and the `decoupler` documentation for additional analyses.\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "pytximport",
    "language": "python",
    "name": "python3"
   },
@@ -2078,7 +2162,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index afa99ff..3836fbb 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,11 +1,15 @@
 [build-system]
-requires = ["flit_core >=3.4,<4"]
-build-backend = "flit_core.buildapi"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["pytximport"]
 
 [project]
 name = "pytximport"
+version = "0.12.1"
 description = "A python implementation of `tximport` to transform transcript into gene counts"
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 license = { file = "LICENSE" }
 authors = [{ name = "Malte Kuehl", email = "malte.kuehl@clin.au.dk" }]
 readme = { file = "README.md", content-type = "text/markdown" }
@@ -17,20 +21,19 @@ classifiers = [
     "Natural Language :: English",
     "Typing :: Typed",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
     "Operating System :: OS Independent",
 ]
-dynamic = ["version"]
 dependencies = [
-    "anndata>=0.8.0",
+    "anndata>=0.11.0",
     "click>=8.0.0,<9",
     "click_default_group>=1.2.0,<2",
-    "flox>=0.9.0,<0.11.0",
+    "flox>=0.10.0,<0.11.0",
     "h5py>=3.0.0,<4",
-    "numpy>=1.19.0,<3",
+    "numpy>=2.0.0,<3",
     "pandas>=2.2.0,<3",
     "pybiomart==0.2.0",
     "tqdm>=4.0.0,<5",
@@ -45,6 +48,7 @@ dev = [
     "biocframe",
     "certifi",
     "coverage",
+    "decoupler",
     "dolomite",
     "dolomite-se",
     "flit",
@@ -59,8 +63,10 @@ dev = [
     "pandoc",
     "pre-commit",
     "pyarrow",
+    "pydeseq2",
+    "pypath-omnipath",
     "pytest",
-    "ruff==0.11.2",
+    "ruff==0.13.2",
     "sphinx",
     "sphinx-autoapi",
     "sphinx-autodoc-typehints",
diff --git a/pytximport/__init__.py b/pytximport/__init__.py
index 85a28c6..2a4b747 100755
--- a/pytximport/__init__.py
+++ b/pytximport/__init__.py
@@ -23,10 +23,13 @@
     differ between the two packages. Please refer to the documentation for more information.
 """
 
+from importlib.metadata import version
+
 from . import definitions, importers, utils
 from ._cli import cli
-from ._version import __version__
 from .core import tximport
 
+__version__ = version("pytximport")
+
 # Allow users to import the tximport function as pytximport as well
 pytximport = tximport
diff --git a/pytximport/_version.py b/pytximport/_version.py
deleted file mode 100644
index 1ec58cb..0000000
--- a/pytximport/_version.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""Version information for the pytximport package."""
-
-# This package will follow Semantic Versioning after version 1.0.0: https://semver.org/
-__version__ = "0.12.0"
diff --git a/pytximport/importers/_read_tsv.py b/pytximport/importers/_read_tsv.py
index 5c65b36..afd6230 100644
--- a/pytximport/importers/_read_tsv.py
+++ b/pytximport/importers/_read_tsv.py
@@ -1,11 +1,12 @@
 import importlib.util
 from logging import warning
 from pathlib import Path
-from typing import Literal, Optional, Union
+from typing import Literal, Optional, Union, cast
 
 import numpy as np
 import pandas as pd
-from numpy.typing import ArrayLike
+from numpy.typing import ArrayLike, DTypeLike
+from pandas._typing import DtypeArg
 
 from ..definitions import TranscriptData
 from ..utils._convert_counts_to_tpm import convert_counts_to_tpm
@@ -45,25 +46,25 @@ def parse_dataframe(
         if abundance_column is None:
             warning("Abundance column not provided, calculating TPM.")
             abundance = convert_counts_to_tpm(
-                counts=transcript_dataframe[counts_column].values,  # type: ignore
-                length=transcript_dataframe[length_column].values,  # type: ignore
+                counts=np.asarray(transcript_dataframe[counts_column].values),
+                length=np.asarray(transcript_dataframe[length_column].values),
             )
         else:
             assert abundance_column in transcript_dataframe.columns, (
                 f"Could not find the abundance column `{abundance_column}`."
             )
-            abundance = transcript_dataframe[abundance_column].values  # type: ignore
+            abundance = np.asarray(transcript_dataframe[abundance_column].values)
 
-        counts = transcript_dataframe[counts_column].values  # type: ignore
+        counts = np.asarray(transcript_dataframe[counts_column].values)
     else:
         counts = None
         abundance = None
 
     # Create a DataFrame with the transcript-level expression
     transcripts = TranscriptData(
-        transcript_id=transcript_dataframe[id_column].values,  # type: ignore
+        transcript_id=transcript_dataframe[id_column].values.astype(str).tolist(),
         counts=counts,
-        length=transcript_dataframe[length_column].values,  # type: ignore
+        length=np.asarray(transcript_dataframe[length_column].values),
         abundance=abundance,
         inferential_replicates=None,
     )
@@ -102,7 +103,7 @@ def read_tsv(
 
     # Read the quantification file as a tsv, tab separated with the first line being the column names
     usecols = [id_column, length_column]
-    dtype = {id_column: str, length_column: np.float64}
+    dtype: dict[str, DTypeLike] = {id_column: str, length_column: np.float64}
 
     if not recompute_counts:
         usecols.append(counts_column)
@@ -125,7 +126,7 @@ def read_tsv(
         compression=("gzip" if file_path.suffix == ".gz" else None),
         engine=engine,
         usecols=usecols,
-        dtype=dtype,
+        dtype=cast(DtypeArg, dtype),
         na_filter=False,
     )
 
diff --git a/pytximport/utils/_convert_counts_to_tpm.py b/pytximport/utils/_convert_counts_to_tpm.py
index 25b963d..2bb3010 100644
--- a/pytximport/utils/_convert_counts_to_tpm.py
+++ b/pytximport/utils/_convert_counts_to_tpm.py
@@ -1,17 +1,18 @@
 import numpy as np
+from numpy.typing import NDArray
 
 
 def convert_counts_to_tpm(
-    counts: np.ndarray,
-    length: np.ndarray,
-) -> np.ndarray:
+    counts: NDArray,
+    length: NDArray,
+) -> NDArray:
     """Convert transcript-level counts to TPM.
 
     Args:
-        counts (np.ndarray): The transcript-level counts.
-        length (np.ndarray): The length of the transcripts.
+        counts (NDArray): The transcript-level counts.
+        length (NDArray): The length of the transcripts.
 
     Returns:
-        np.ndarray: The transcript-level expression data with the TPM.
+        NDArray: The transcript-level expression data with the TPM.
     """
     return np.array(counts * (1e6 / np.sum(counts / length)) / length)
diff --git a/pytximport/utils/_convert_transcripts_to_genes.py b/pytximport/utils/_convert_transcripts_to_genes.py
index 414687c..99c3559 100644
--- a/pytximport/utils/_convert_transcripts_to_genes.py
+++ b/pytximport/utils/_convert_transcripts_to_genes.py
@@ -4,6 +4,7 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
+from numpy.typing import NDArray
 
 from ._convert_abundance_to_counts import convert_abundance_to_counts
 from ._remove_transcript_version import remove_transcript_version
@@ -29,7 +30,7 @@ def convert_transcripts_to_genes(
     Returns:
         xr.Dataset: The gene-level expression data from multiple samples.
     """
-    transcript_ids: Union[np.ndarray, List[str]] = transcript_data.coords["transcript_id"].values
+    transcript_ids: Union[NDArray, List[str]] = np.asarray(transcript_data.coords["transcript_id"].values)
     unique_transcripts = list(set(transcript_ids))
 
     # Avoid duplicates in the mapping
diff --git a/requirements.frozen.txt b/requirements.frozen.txt
deleted file mode 100644
index f6b5a02..0000000
--- a/requirements.frozen.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-anndata==0.11.3
-array_api_compat==1.11.2
-attrs==25.3.0
-cattrs==24.1.3
-certifi==2025.1.31
-charset-normalizer==3.4.1
-click==8.1.8
-click-default-group==1.2.4
-flox==0.10.1
-future==1.0.0
-h5py==3.13.0
-idna==3.10
-natsort==8.4.0
-numpy==2.2.4
-numpy-groupies==0.11.2
-packaging==24.2
-pandas==2.2.3
-platformdirs==4.3.7
-pybiomart==0.2.0
-python-dateutil==2.9.0.post0
-pytz==2025.2
-requests==2.32.3
-requests-cache==1.2.1
-scipy==1.15.2
-six==1.17.0
-toolz==1.0.0
-tqdm==4.67.1
-tzdata==2025.2
-url-normalize==1.4.3
-urllib3==2.3.0
-xarray==2025.3.0
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