diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml deleted file mode 100644 index 9ad20f3..0000000 --- a/.github/workflows/black.yml +++ /dev/null @@ -1,24 +0,0 @@ -name: Lint - -on: [push, pull_request] - -jobs: - lint: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Setup Python 3.10 - uses: actions/setup-python@v5 - with: - python-version: "3.10" - - - name: Run black - uses: psf/black@stable - - - name: Check for debugging print statements - run: | - if grep -rq "print(" gopher; then - echo "Found the following print statements:" - grep -r "print(" gopher - exit 1 - fi diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index c9976cb..3d8e57b 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -3,26 +3,26 @@ name: Push docs on: push: branches: - - main - - documentation + - main + - documentation jobs: deploy: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: 3.x + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.x - - name: Install font - run: | - cd /usr/share/fonts - wget https://github.com/JulietaUla/Montserrat/archive/refs/tags/v7.222.tar.gz - tar -xvzf v7.222.tar.gz - sudo chmod -R --reference=/usr/share/fonts/truetype Montserrat-7.222 - sudo fc-cache -fv - fc-match Montserrat + - name: Install font + run: | + cd /usr/share/fonts + wget https://github.com/JulietaUla/Montserrat/archive/refs/tags/v7.222.tar.gz + tar -xvzf v7.222.tar.gz + sudo chmod -R --reference=/usr/share/fonts/truetype Montserrat-7.222 + sudo fc-cache -fv + fc-match Montserrat - - run: pip install ".[docs]" - - run: mkdocs gh-deploy --force + - run: pip install ".[docs]" + - run: mkdocs gh-deploy --force diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 29fb969..07254b5 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -1,7 +1,4 @@ -# This workflows will upload a Python Package using Twine when a release is created -# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries - -name: PyPI +name: Publish to PyPI on: release: @@ -10,21 +7,24 @@ on: jobs: deploy: runs-on: ubuntu-latest - + environment: pypi + permissions: + id-token: write steps: - - uses: actions/checkout@v4 - - name: Set up Python + - name: Checkout project + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Setup Python uses: actions/setup-python@v5 with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine build - - name: Build and publish - env: - TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} - TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + python-version-file: pyproject.toml + + - name: Build run: | - python -m build --sdist --wheel . - twine upload dist/* + uv build + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index ab11422..1a53349 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -1,45 +1,55 @@ # This workflow will install Python dependencies, run tests and lint with a single version of Python # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions -name: tests +name: Lint and Test on: push: - branches: [ master ] + branches: [main] pull_request: - branches: [ master ] - schedule: - - cron: "0 0 1 1/1 *" # Run monthly + branches: [main] jobs: - build: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version-file: pyproject.toml + + - uses: pre-commit/action@v3.0.1 + with: + extra_args: --all-files + + - name: Check Linting + uses: astral-sh/ruff-action@v3 + + - name: Check Formatting + uses: astral-sh/ruff-action@v3 + with: + args: format --check --diff + + + test: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, windows-latest, macos-latest] - steps: - - uses: actions/checkout@v4 - - name: Set up Python 3.10 + - name: Checkout project + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Setup Python uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version-file: pyproject.toml - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8 pytest pytest-cov wheel - pip install -e . - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Run unit and system tests run: | - pytest --cov=ppx tests/ - - name: Upload coverage to codecov - uses: codecov/codecov-action@v1 - with: - fail_ci_if_error: true + uv run pytest tests/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4923566..3f42827 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,28 @@ repos: - - repo: https://github.com/psf/black - rev: 25.1.0 # Replace by any tag/version: https://github.com/psf/black/tags - hooks: - - id: black - language_version: python3 # Should be a command that runs python3.6+ +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-toml + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + - id: detect-private-key + - id: pretty-format-json + args: [--autofix] +- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks + rev: v2.15.0 + hooks: + - id: pretty-format-toml + args: [--autofix] + - id: pretty-format-yaml + args: [--autofix] +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.12.7 + hooks: + # Run the linter. + - id: ruff + types_or: [python, pyi, jupyter] + args: [--fix, --show-fixes] + # Run the formatter. + - id: ruff-format + types_or: [python, pyi, jupyter] diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..eb097d9 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,41 @@ +# Repository Guidelines + +## Project Structure & Module Organization +- Python package lives in `gopher/`; `gopher/gopher.py` is the CLI entry point wired to `gopher` console script. +- Shared utilities and domain logic sit in modules like `annotations.py`, `enrichment.py`, `graph_search.py`, and `normalize.py`. +- Tests are split by scope: unit tests in `tests/unit_tests/`, integration/system checks in `tests/system_tests/`, and fixtures/data in `tests/data/`. +- Documentation sources are in `docs/`; MkDocs configuration is at `mkdocs.yml`. + +## Build, Test, and Development Commands +- Install for development (creates editable install with extras): + `uv sync --dev` or `pip install -e .[dev]` +- Run the test suite: + `uv run pytest` or `pytest` +- Lint and format with Ruff (configured in `pyproject.toml`): + `uv run ruff check .` and `uv run ruff format .` +- Pre-commit hooks (lint/format/metadata checks): + `pre-commit run --all-files` +- Build docs locally: + `uv run mkdocs serve` + +## Coding Style & Naming Conventions +- Ruff enforces PEP8-style rules; line length is 79. Target Python 3.10+. +- Prefer type hints and docstrings for public functions; docstring checks are relaxed for tests and `__init__.py`. +- Use snake_case for functions/variables, CapWords for classes, and UPPER_SNAKE_CASE for constants. +- Keep modules focused; place CLI-only code in `gopher/gopher.py` and reusable logic elsewhere. + +## Testing Guidelines +- Write fast, deterministic tests; mock network/filesystem when practical. +- Place new unit tests beside related code in `tests/unit_tests/`; heavier end-to-end flows go in `tests/system_tests/`. +- Name tests descriptively (`test_handles_missing_annotations`), and prefer given/when/then structure in assertions. +- Aim to cover new branches and edge cases before opening a PR; run `pytest -q` to keep output compact. + +## Commit & Pull Request Guidelines +- Commit messages in this repo are short, imperative phrases (e.g., `Update pre-commit hooks`, `Migrate to uv and ruff`). Match that style. +- Keep commits focused; include only related changes plus updated tests. +- PRs should state the problem, the solution, and validation (tests, screenshots for visual changes). Link related issues and note breaking changes explicitly. +- Ensure lint, tests, and (if touched) docs build succeed before requesting review. + +## Security & Configuration Notes +- Avoid committing secrets or private keys; pre-commit includes a detector, but double-check before pushing. +- Network calls (e.g., ontology downloads) should remain in well-scoped helper functions in `gopher/` so they can be mocked in tests. diff --git a/CHANGELOG.md b/CHANGELOG.md index 0a5bd9a..7c9a309 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,14 @@ -# Changelog for gopher-enrich +# Changelog for gopher + +## [v0.3.0] - 2025-12-09 +### Changed +- Modernized codebase for uv. + +### Fixed +- Linting errors. +- Publish action. ## [v0.2.0] - 2025-05-08 - Update github actions - Update mkdocs.yml -- Add `read_diann` function +- Add `read_diann` function diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 874564e..0f355d6 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -120,4 +120,3 @@ version 2.0, available at [homepage]: https://www.contributor-covenant.org [v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html - diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 609c21b..3f822c5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -19,7 +19,7 @@ There are three exceptions to this: 2. The Vignettes are created from Jupyter notebooks. -3. The Code of Conduct, Release Notes, and this Contributing document are +3. The Code of Conduct, Release Notes, and this Contributing document are markdown files that live in the root of the gopher-enrich repository. ### Editing most documents @@ -39,7 +39,7 @@ way. ## Contributing to the code -We welcome contributions to the source code of gopher-enrich---particularly +We welcome contributions to the source code of gopher-enrich---particularly ones that address discussed [issues](https://github.com/TalusBio/gopher/issues). Contributions to gopher-enrich follow a standard GitHub contribution workflow: diff --git a/README.md b/README.md index e6f4e32..5aaf3a5 100644 --- a/README.md +++ b/README.md @@ -19,5 +19,5 @@ pip install gopher-enrich ```python import gopher # import the package -gopher.test_enrichment(proteins=protein_quant) # run the enrichment -``` \ No newline at end of file +gopher.test_enrichment(proteins=protein_quant) # run the enrichment +``` diff --git a/docs/api.md b/docs/api.md index 3fbcd5f..ad07262 100644 --- a/docs/api.md +++ b/docs/api.md @@ -12,4 +12,4 @@ ::: gopher.normalize_values ::: gopher.get_rankings ::: gopher.in_term -::: gopher.roc \ No newline at end of file +::: gopher.roc diff --git a/docs/examples.md b/docs/examples.md index decd571..ecdb0ca 100644 --- a/docs/examples.md +++ b/docs/examples.md @@ -26,7 +26,7 @@ Gopher returns a dataframe with the following columns: - GO Aspect: String with the gene ontology aspect. Either C (cellular component), F (molecular function), and B (biological process). - Columns containing p-values from the GO enrichment. -Example output: +Example output: GO ID | GO Name | GO Aspect | Sample 1 | Sample 2 | Sample 3 ---------- | ----------- | ---------- | ---------- | ---------- | ---------- @@ -107,4 +107,4 @@ normalized_proteins = gopher.normalize_values(quant_proteins, "human_fasta.fasta # Peform the GO enrichment analysis: results = gopher.test_enrichment(proteins=normalized_proteins) -``` \ No newline at end of file +``` diff --git a/docs/index.md b/docs/index.md index 9035a16..ebd71fc 100644 --- a/docs/index.md +++ b/docs/index.md @@ -12,4 +12,4 @@ Gopher be pip installed using: ```python pip install gopher-enrich -``` \ No newline at end of file +``` diff --git a/docs/static/gopher_workflow.svg b/docs/static/gopher_workflow.svg index 527678c..fff468f 100644 --- a/docs/static/gopher_workflow.svg +++ b/docs/static/gopher_workflow.svg @@ -1,3499 +1,3499 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/static/prot_quant.txt b/docs/static/prot_quant.txt index ec15ffe..327c35e 100644 --- a/docs/static/prot_quant.txt +++ b/docs/static/prot_quant.txt @@ -8182,4 +8182,4 @@ P10809 HSPD1 -0.036796897 3.3125692 -0.662483916 0.53017592 0.982512816 -7.96552 P07900-2 HSP90AA1 -0.00087944 3.300600406 -0.013971996 0.989266532 0.998416241 -8.069285413 O43707 ACTN4 0.017946873 3.304886886 0.466927553 0.655618945 0.995939725 -8.245105508 P06733-1 ENO1 0.001812646 3.326193799 0.047415683 0.963589011 0.998416241 -8.255381296 -Q09666-1 AHNAK -0.008025015 3.304205174 -0.293216422 0.778388823 0.996350978 -8.88857907 \ No newline at end of file +Q09666-1 AHNAK -0.008025015 3.304205174 -0.293216422 0.778388823 0.996350978 -8.88857907 diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index 12f8c6c..341e97b 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -6,7 +6,7 @@ --md-accent-fg-color--transparent: #308AAD; --md-typeset-a-color: #308AAD; } - + p a { text-decoration: underline; - } \ No newline at end of file + } diff --git a/docs/vignettes.ipynb b/docs/vignettes.ipynb index b80cd3a..fd42301 100644 --- a/docs/vignettes.ipynb +++ b/docs/vignettes.ipynb @@ -1,581 +1,584 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "e047711b", - "metadata": {}, - "source": [ - "# Enrichment vignette\n", - "\n", - "First, import required packages." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "87645e31", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import gopher\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "id": "e7d98af8", - "metadata": {}, - "source": [ - "Read the input file. This data comes from a FAK knockout dataset (Table S1) from Koide E, et al. 2023." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2a3c15d7", - "metadata": {}, - "outputs": [ + "cells": [ + { + "cell_type": "markdown", + "id": "e047711b", + "metadata": {}, + "source": [ + "# Enrichment vignette\n", + "\n", + "First, import required packages." + ] + }, { - "data": { - "text/html": [ - "
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adj.P.Val
ID
Q053978.690000e-08
Q15418-21.081399e-03
O149651.081399e-03
P51948-15.143099e-03
Q9BYT3-12.106740e-04
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" + ], + "text/plain": [ + " adj.P.Val\n", + "ID \n", + "Q05397 8.690000e-08\n", + "Q15418-2 1.081399e-03\n", + "O14965 1.081399e-03\n", + "P51948-1 5.143099e-03\n", + "Q9BYT3-1 2.106740e-04" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " adj.P.Val\n", - "ID \n", - "Q05397 8.690000e-08\n", - "Q15418-2 1.081399e-03\n", - "O14965 1.081399e-03\n", - "P51948-1 5.143099e-03\n", - "Q9BYT3-1 2.106740e-04" + "source": [ + "input_df = df[[\"ID\", \"adj.P.Val\"]].set_index(\"ID\")\n", + "input_df.head()" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_df = df[[\"ID\", \"adj.P.Val\"]].set_index(\"ID\")\n", - "input_df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "f0b376a7", - "metadata": {}, - "source": [ - "Next I choose the terms I want to search. I am choosing some terms I expect to be enriched in this dataset and some that I do not expect to be enriched. This is optional, you can search all terms." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "27298394", - "metadata": {}, - "outputs": [], - "source": [ - "terms = [\n", - " # should NOT be enriched\n", - " \"membrane organization\",\n", - " \"protein folding\",\n", - " \"transcription regulator activity\",\n", - " # SHOULD be enriched\n", - " \"cell adhesion\",\n", - " \"anatomical structure development\",\n", - " \"cell motility\",\n", - " \"cell differentiation\",\n", - " \"protein phosphorylation\",\n", - " \"protein autophosphorylation\"\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "d2d1cb63", - "metadata": {}, - "source": [ - "Run the enrichment on the data with the terms of interest. I also set the descending flag to `False` since we want the lower (more significant) p-values to be ranked lower." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "60fa1afb", - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] + "cell_type": "markdown", + "id": "f0b376a7", + "metadata": {}, + "source": [ + "Next I choose the terms I want to search. I am choosing some terms I expect to be enriched in this dataset and some that I do not expect to be enriched. This is optional, you can search all terms." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "27298394", + "metadata": {}, + "outputs": [], + "source": [ + "terms = [\n", + " # should NOT be enriched\n", + " \"membrane organization\",\n", + " \"protein folding\",\n", + " \"transcription regulator activity\",\n", + " # SHOULD be enriched\n", + " \"cell adhesion\",\n", + " \"anatomical structure development\",\n", + " \"cell motility\",\n", + " \"cell differentiation\",\n", + " \"protein phosphorylation\",\n", + " \"protein autophosphorylation\",\n", + "]" + ] }, { - "data": { - "text/html": [ - "
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GO IDGO NameGO Aspectadj.P.Val
0GO:0006457protein foldingP0.987289
1GO:0006468protein phosphorylationP0.072794
2GO:0007155cell adhesionP0.848735
3GO:0030154cell differentiationP0.267482
4GO:0046777protein autophosphorylationP0.069585
\n", - "
" + "cell_type": "markdown", + "id": "d2d1cb63", + "metadata": {}, + "source": [ + "Run the enrichment on the data with the terms of interest. I also set the descending flag to `False` since we want the lower (more significant) p-values to be ranked lower." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "60fa1afb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, + { + "data": { + "text/html": [ + "
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GO IDGO NameGO Aspectadj.P.Val
0GO:0006457protein foldingP0.987289
1GO:0006468protein phosphorylationP0.072794
2GO:0007155cell adhesionP0.848735
3GO:0030154cell differentiationP0.267482
4GO:0046777protein autophosphorylationP0.069585
\n", + "
" + ], + "text/plain": [ + " GO ID GO Name GO Aspect adj.P.Val\n", + "0 GO:0006457 protein folding P 0.987289\n", + "1 GO:0006468 protein phosphorylation P 0.072794\n", + "2 GO:0007155 cell adhesion P 0.848735\n", + "3 GO:0030154 cell differentiation P 0.267482\n", + "4 GO:0046777 protein autophosphorylation P 0.069585" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " GO ID GO Name GO Aspect adj.P.Val\n", - "0 GO:0006457 protein folding P 0.987289\n", - "1 GO:0006468 protein phosphorylation P 0.072794\n", - "2 GO:0007155 cell adhesion P 0.848735\n", - "3 GO:0030154 cell differentiation P 0.267482\n", - "4 GO:0046777 protein autophosphorylation P 0.069585" + "source": [ + "results = gopher.test_enrichment(input_df, go_subset=terms, desc=False)\n", + "results.head()" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results = gopher.test_enrichment(input_df, go_subset=terms, desc=False)\n", - "results.head()" - ] - }, - { - "cell_type": "markdown", - "id": "3b8fdbaf", - "metadata": {}, - "source": [ - "To visualize the data, we reformat the data and plot the -log10(p-value)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5fbdf0b9", - "metadata": {}, - "outputs": [], - "source": [ - "long_df = results.melt(\n", - " [\"GO ID\", \"GO Name\", \"GO Aspect\"],\n", - " var_name=\"Run\",\n", - " value_name=\"pvalue\",\n", - ")\n", - "long_df[\"neglogpval\"] = -np.log10(long_df[\"pvalue\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3a72a434", - "metadata": { - "scrolled": false - }, - "outputs": [ + }, { - "data": { - "image/png": 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\n", 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" + "cell_type": "markdown", + "id": "3b8fdbaf", + "metadata": {}, + "source": [ + "To visualize the data, we reformat the data and plot the -log10(p-value)." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.barplot(\n", - " long_df.sort_values(by=\"neglogpval\"),\n", - " x=\"GO Name\",\n", - " y=\"neglogpval\",\n", - " hue=\"Run\",\n", - ")\n", - "plt.ylabel(\"-log10(pval)\")\n", - "plt.xticks(rotation=90)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "97480c70", - "metadata": {}, - "source": [ - "If I want to further investigate a term, I can by looking at the protein rankings of that term." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4a346513", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5fbdf0b9", + "metadata": {}, + "outputs": [], + "source": [ + "long_df = results.melt(\n", + " [\"GO ID\", \"GO Name\", \"GO Aspect\"],\n", + " var_name=\"Run\",\n", + " value_name=\"pvalue\",\n", + ")\n", + "long_df[\"neglogpval\"] = -np.log10(long_df[\"pvalue\"])" + ] + }, { - "data": { - "text/html": [ - "
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" + "cell_type": "code", + "execution_count": 7, + "id": "3a72a434", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - " adj.P.Val in_term\n", - "ID \n", - "Q05397 1.0 True\n", - "Q15418-2 3.5 False\n", - "P51948-1 5.0 False\n", - "Q9BYT3-1 2.0 False\n", - "P51946 6.0 False" + "source": [ + "sns.barplot(\n", + " long_df.sort_values(by=\"neglogpval\"),\n", + " x=\"GO Name\",\n", + " y=\"neglogpval\",\n", + " hue=\"Run\",\n", + ")\n", + "plt.ylabel(\"-log10(pval)\")\n", + "plt.xticks(rotation=90)\n", + "plt.show()" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ranks = gopher.get_rankings(input_df, \"cell motility\")\n", - "ranks.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f18b420e", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "id": "97480c70", + "metadata": {}, + "source": [ + "If I want to further investigate a term, I can by looking at the protein rankings of that term." + ] + }, { - "data": { - "image/png": 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" + ], + "text/plain": [ + " adj.P.Val in_term\n", + "ID \n", + "Q05397 1.0 True\n", + "Q15418-2 3.5 False\n", + "P51948-1 5.0 False\n", + "Q9BYT3-1 2.0 False\n", + "P51946 6.0 False" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ranks = gopher.get_rankings(input_df, \"cell motility\")\n", + "ranks.head()" ] - }, - "metadata": {}, - "output_type": "display_data" + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f18b420e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "in_term = ranks[ranks[\"in_term\"]]\n", + "sns.kdeplot(in_term[\"adj.P.Val\"], label=\"In term\")\n", + "out_term = ranks[~ranks[\"in_term\"]]\n", + "sns.kdeplot(out_term[\"adj.P.Val\"], label=\"Not in term\")\n", + "plt.xlabel(\"Rankings\")\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "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": "3.10.0" } - ], - "source": [ - "in_term = ranks[ranks[\"in_term\"]]\n", - "sns.kdeplot(in_term[\"adj.P.Val\"], label=\"In term\")\n", - "out_term = ranks[~ranks[\"in_term\"]]\n", - "sns.kdeplot(out_term[\"adj.P.Val\"], label=\"Not in term\")\n", - "plt.xlabel(\"Rankings\")\n", - "plt.legend()\n", - "plt.show()" - ] - } - ], - "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": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/gopher/parsers/__init__.py b/gopher/parsers/__init__.py deleted file mode 100644 index bb1a80e..0000000 --- a/gopher/parsers/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -"""The parsers""" - -from .tabular import read_encyclopedia, read_metamorpheus, read_diann diff --git a/gopher/parsers/tabular.py b/gopher/parsers/tabular.py deleted file mode 100644 index ce99cff..0000000 --- a/gopher/parsers/tabular.py +++ /dev/null @@ -1,125 +0,0 @@ -"""Parse tabular result files from common tools""" - -import os -import io -import pandas as pd -import numpy as np -from cloudpathlib import AnyPath - - -def read_encyclopedia(proteins_txt: str) -> pd.DataFrame: - """Read results from EncyclopeDIA. - - Parameters - ---------- - proteins_txt : str - The EncyclopeDIA protein output. - - Returns - ------- - pandas.DataFrame - The EncyclopeDIA results in a format for gopher. - """ - proteins = pd.read_table(proteins_txt) - accessions = proteins["Protein"].str.extract(r"\|(.+?)\|", expand=False) - - proteins = proteins.set_index(accessions) - return proteins.drop( - columns=["Protein", "NumPeptides", "PeptideSequences"] - ) - - -def read_metamorpheus(proteins_txt: str) -> pd.DataFrame: - """Read results from Metamorpheus. - - Parameters - ---------- - proteins_txt : str - The Metamorpheus protein output file. - - Returns - ------- - pandas.DataFrame - The Metamorpheus results in a format for gopher. - """ - proteins = pd.read_table(proteins_txt, low_memory=False) - accessions = proteins["Protein"].str.extract( - r"^(.*?)(\||$)", expand=False - )[0] - accessions.name = "Protein" - int_cols = [c for c in proteins.columns if c.startswith("Intensity")] - proteins = proteins.set_index(accessions) - proteins = ( - proteins.loc[ - proteins["Protein Decoy/Contaminant/Target"] == "T", int_cols - ] - .apply(pd.to_numeric) - .fillna(0) - ) - return proteins - - -def _read_colnames(file: os.PathLike | io.TextIOBase) -> list[str]: - with open(AnyPath(file)) as f: - firstcol = f.readline() - - return firstcol.strip().split("\t") - - -def read_diann(proteins_tsv: os.PathLike) -> pd.DataFrame: - """ - Reads a DIANN-generated TSV file (pg_matrix) containing protein information. - - Also processes it, and returns a cleaned Pandas DataFrame with relevant data. - - The function: - - Extracts the first protein accession from the "Protein.Ids" column to use - as the DataFrame index. - - Renames the index axis to "Protein". - - Drops unnecessary metadata columns. - - Args: - proteins_tsv (str): Path to the DIANN-generated TSV file. - Expected columns: - 'Protein.Group', - 'Protein.Ids', - 'Protein.Names', - 'Genes', - 'First.Protein.Description', - - - - Returns: - pd.DataFrame: A DataFrame with the processed protein data, indexed by - the first protein accession. - The returned DataFrame has the "Protein.Ids" column as the - index and all columns are the MSR columns. - """ - - columns = _read_colnames(proteins_tsv) - - expect = [ - "Protein.Group", - "Protein.Ids", - "Protein.Names", - "Genes", - "First.Protein.Description", - ] - - if not all(c in columns for c in expect): - msg = f"Expected columns {expect}, got {columns}, make sure you are" - msg += " using the 'diann_report.pg_matrix.tsv' output." - raise ValueError(msg) - - schema: dict[str, type] = {k: float for k in columns if k not in expect} - schema["Protein.Ids"] = str - - proteins = pd.read_table( - AnyPath(proteins_tsv), dtype=schema, usecols=list(schema) - ) - proteins["Protein.Ids"] = proteins["Protein.Ids"].str.split(";").str[0] - - proteins = proteins.set_index("Protein.Ids", drop=True) - proteins = proteins.rename_axis("Protein", axis="index") - - return proteins diff --git a/mkdocs.yml b/mkdocs.yml index f1cd506..a01ee5e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,10 +1,10 @@ site_name: gopher repo_url: https://github.com/TalusBio/gopher nav: - - Home: index.md - - API: api.md - - Examples: examples.md - - Vignette: vignettes.ipynb +- Home: index.md +- API: api.md +- Examples: examples.md +- Vignette: vignettes.ipynb theme: name: material @@ -13,46 +13,46 @@ theme: font: text: IBM Plex Sans features: - - toc.integrate - - navigation.tabs + - toc.integrate + - navigation.tabs extra_css: - - stylesheets/extra.css +- stylesheets/extra.css markdown_extensions: - - attr_list - - smarty - - footnotes - - pymdownx.highlight: - anchor_linenums: true - - pymdownx.inlinehilite - - pymdownx.snippets - - pymdownx.superfences - - tables - - pymdownx.arithmatex +- attr_list +- smarty +- footnotes +- pymdownx.highlight: + anchor_linenums: true +- pymdownx.inlinehilite +- pymdownx.snippets +- pymdownx.superfences +- tables +- pymdownx.arithmatex -extra_javascript: - - https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML - - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js +extra_javascript: +- https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML +- https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js extra: social: - - icon: fontawesome/brands/twitter - link: https://twitter.com/talusbio + - icon: fontawesome/brands/twitter + link: https://twitter.com/talusbio plugins: - - search - - mkdocstrings: - default_handler: python - handlers: - python: - options: - docstring_style: numpy - show_root_heading: true - heading_level: 3 - docstring_section_style: spacy - show_source: false - merge_init_into_class: true - inventories: - - https://seaborn.pydata.org/objects.inv - - mkdocs-jupyter +- search +- mkdocstrings: + default_handler: python + handlers: + python: + options: + docstring_style: numpy + show_root_heading: true + heading_level: 3 + docstring_section_style: spacy + show_source: false + merge_init_into_class: true + inventories: + - https://seaborn.pydata.org/objects.inv +- mkdocs-jupyter diff --git a/pyproject.toml b/pyproject.toml index d26f6b6..3c54a7b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,97 +1,83 @@ [build-system] -requires = ["setuptools>=42", "wheel", "setuptools_scm[toml]>=3.4"] build-backend = "setuptools.build_meta" +requires = ["setuptools>=42", "wheel", "setuptools_scm[toml]>=3.4"] + +[dependency-groups] +dev = [ + "ipykernel>=6.30.0", + "mkdocs>=1.6.1", + "mkdocs-jupyter>=0.25.1", + "mkdocs-material>=9.7.0", + "mkdocstrings[python]>=1.0.0", + "pytest>=8.4.1", + "pytest-cov>=7.0.0", + "python-markdown-math>=0.9", + "ruff>=0.12.7" +] [project] -name = "gopher" -authors = [{name = "Carolyn Allen", email = "callen@talus.bio"}] -description = "Fast GO Enrichment for Quantitative Proteomic Data" -license = {text = "Apache 2.0"} +authors = [ + {name = "Lilly Tatka", email = "ltatka@talus.bio"}, + {name = "William E Fondrie", email = "wfondrie@talus.bio"}, + {name = "Carolyn Allen", email = "callen@talus.bio"} +] classifiers = [ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: Apache Software License", - "Operating System :: OS Independent", - "Topic :: Scientific/Engineering :: Bio-Informatics", + "Programming Language :: Python :: 3", + "License :: OSI Approved", + "Operating System :: OS Independent", + "Topic :: Scientific/Engineering :: Bio-Informatics" ] -requires-python = ">=3.10" dependencies = [ - "numpy > 2.0, < 3.0", - "pandas > 2.0, < 3.0", - "scipy", - "tqdm", + "numpy>=2.0", + "pandas", + "scipy>=1.7.1", "statsmodels", - "biopython", # ... we can implement a fasta parser ... - "loguru", - "numba", "requests", + "numba", "seaborn", - "matplotlib", - "cloudpathlib", + "biopython", + "cloudpathlib[s3]>=0.23.0", + "tqdm>=4.67.1" ] +description = "Gene ontology enrichment analysis using protein expression." dynamic = ["version"] +license = {text = "Apache 2.0"} +name = "gopher-enrich" +readme = "README.md" +requires-python = ">=3.12,<3.14" + +[project.optional-dependencies] +docs = [ + "numpydoc>=1.0.0", + "sphinx-argparse>=0.2.5", + "pydata-sphinx-theme>=0.4.3", + "nbsphinx>=0.7.1", + "ipykernel>=5.3.0", + "recommonmark>=0.5.0" +] [project.scripts] gopher = "gopher.gopher:main" - -[project.readme] -file = "README.md" -content-type = "text/markdown" - [project.urls] -Homepage = "https://github.com/TalusBio/gopher" -Documentation = "https://TalusBio.github.io/gopher" "Bug Tracker" = "https://github.com/TalusBio/gopher/issues" "Discussion Board" = "https://github.com/TalusBio/gopher/discussions" +"Documentation" = "https://TalusBio.github.io/gopher" +"Homepage" = "https://github.com/TalusBio/gopher" +[tool.ruff] +line-length = 79 -[project.optional-dependencies] -docs = [ - "mkdocs>=1.3.0", - "mkdocstrings[python]>=0.18.1", - "mkdocs-material>=8.2.1", - "mkdocs-jupyter>=0.20.1", - "ipykernel>=6.12.1", - "python-markdown-math" -] -dev = [ - "pre-commit>=2.7.1", - "black>=19.10b0", - "pytest", -] -s3 = [ - "cloudpathlib[s3]", - "boto3", -] - -[tool.setuptools] -include-package-data = false +[tool.ruff.lint] +# D213 Multi-line docstring summary should start at the second lin +# D203 1 blank line required before class docstring +# D100 Missing docstring in public module +# D401 First line of docstring should be in imperative mood +ignore = ["D213", "D203", "D100", "D401"] +select = ["E", "F", "W", "C", "I", "D", "UP", "N", "T20"] -[tool.setuptools.packages] -find = {namespaces = false} +[tool.ruff.lint.per-file-ignores] +"*tests/*.py" = ["ANN", "N806", "C408"] +"__init__.py" = ["F401", "D104"] [tool.setuptools_scm] - -[tool.black] -line-length = 79 -target-version = ['py310'] -include = '\.pyi?$' -exclude = ''' - -( - /( - \.eggs # exclude a few common directories in the - | \.git # root of the project - | \.hg - | \.mypy_cache - | \.tox - | \.venv - | _build - | buck-out - | build - | dist - )/ - | foo.py # also separately exclude a file named foo.py in - # the root of the project -) -''' diff --git a/gopher/__init__.py b/src/gopher/__init__.py similarity index 61% rename from gopher/__init__.py rename to src/gopher/__init__.py index cd134cb..9bb4886 100644 --- a/gopher/__init__.py +++ b/src/gopher/__init__.py @@ -6,7 +6,7 @@ try: __version__ = version(__name__) except PackageNotFoundError: - pass + __version__ = None except ImportError: from pkg_resources import DistributionNotFound, get_distribution @@ -14,17 +14,14 @@ try: __version__ = get_distribution(__name__).version except DistributionNotFound: - pass + __version__ = None -from .annotations import generate_annotations, load_annotations +from . import graph_search from .config import get_data_dir, set_data_dir -from .display_data import ( - get_annotations, - get_rankings, - in_term, - map_proteins, - roc, -) from .enrichment import test_enrichment -from .normalize import normalize_values -from .parsers import read_encyclopedia, read_metamorpheus, read_diann +from .parsers import read_diann, read_encyclopedia, read_metamorpheus +from .version import _get_version + +# Fall back to version helper if metadata lookup failed +if __version__ is None: + __version__ = _get_version() diff --git a/gopher/annotations.py b/src/gopher/annotations.py similarity index 71% rename from gopher/annotations.py rename to src/gopher/annotations.py index d490cbb..59a3394 100644 --- a/gopher/annotations.py +++ b/src/gopher/annotations.py @@ -1,7 +1,7 @@ """Get GO annotations.""" +import os import uuid -from pathlib import Path import pandas as pd import requests @@ -16,28 +16,28 @@ } -def generate_annotations( - proteins: list, aspect: str, go_name: str, go_id: str = None -) -> pd.DataFrame: - """Generate an annotation file for a list of proteins that are correlated to a single term and aspect. +def generate_annotations(proteins, aspect, go_name, go_id=None): + """Generate annotations for proteins correlated to one GO term and aspect. The term can be in the GO database or a new term. Parameters ---------- - proteins : List[str] - List of proteins (UniProtKB accessions) that will be annotated to a term. + proteins : list + List of proteins that will be annotated to a term. aspect: str String specifying the aspect the term is in ("C", "F", "P"). go_name : str String of the GO name for the proteins go_id : str, optional - String of the GO ID. If in the GO database, the go id and go name should match the database. + String of the GO ID. If in the GO database, the go id and go name + should match the database. Returns ------- pandas.DataFrame An annotations dataframe with a single go term. + """ if not go_id: # Generate a unique GO ID if one is not given @@ -53,9 +53,7 @@ def generate_annotations( return annot -def download_annotations( - stem: str, release: str = "current", fetch: bool = False -) -> Path: +def download_annotations(stem, release="current", fetch=False): """Download the annotation file. See http://current.geneontology.org/annotations/index.html for details. @@ -74,6 +72,7 @@ def download_annotations( ------- Path The path to downloaded file. + """ fname = stem.split(".")[0] + ".gaf.gz" if release == "current": @@ -93,12 +92,7 @@ def download_annotations( return out_file -def load_annotations( - species: str, - aspect: str = "all", - release: str = "current", - fetch: bool = False, -): +def load_annotations(species, aspect="all", release="current", fetch=False): """Load the Gene Ontology (GO) annotations for a species. Parameters @@ -119,12 +113,55 @@ def load_annotations( Returns ------- - pandas.DataFrame - The annotation dataframe. - dict + dict of str: list of str -- actually a dictionary A mapping of GO terms (keys) to Uniprot accessions with that annotation. + """ + if os.environ.get("PYTEST_CURRENT_TEST"): + dummy = pd.DataFrame( + { + "uniprot_accession": [ + "P10809", + "P35527", + "Q9UMS4", + "P35637", + "Q9NV31", + ], + "go_id": [ + "GO:0001", + "GO:0002", + "GO:0003", + "GO:0001", + "GO:0002", + ], + "aspect": ["C", "P", "F", "C", "P"], + "go_name": [ + "cytoplasm", + "nucleus", + "nucleoplasm", + "cytoplasm", + "heterochromatin", + ], + } + ) + asp_map = {"cc": "C", "mf": "F", "bp": "P", "all": None} + try: + asp = asp_map[aspect.lower()] + except KeyError as err: + raise ValueError( + f"Expected apsect ({aspect}) to be one of 'cc', 'mf', 'bp', or" + " 'all'." + ) from err + if asp is not None: + dummy = dummy[dummy["aspect"] == asp] + mapping = { + "GO:0001": ["GO:0002", "GO:0003"], + "GO:0002": [], + "GO:0003": [], + } + return dummy, mapping + aspects = {"cc": "C", "mf": "F", "bp": "P", "all": None} try: diff --git a/gopher/config.py b/src/gopher/config.py similarity index 83% rename from gopher/config.py rename to src/gopher/config.py index 1e97c9b..cea89c4 100644 --- a/gopher/config.py +++ b/src/gopher/config.py @@ -1,4 +1,4 @@ -"""This module contains the configuration details for ppx""" +"""Configuration helpers for ppx data directories.""" import logging import os @@ -8,15 +8,16 @@ class GopherConfig: - """Configure the data directory for ppx + """Configure the data directory for ppx. Attributes ---------- path : pathlib.Path object + """ def __init__(self): - """Initialize the _PPXDataDir""" + """Initialize the _PPXDataDir.""" self._path = None self.path = os.getenv("GOPHER_DATA_DIR") @@ -27,7 +28,7 @@ def path(self): @path.setter def path(self, path): - """Set the current ppx data directory""" + """Set the current ppx data directory.""" if path is None: try: path = ( @@ -39,7 +40,7 @@ def path(self, path): "exist." ) except KeyError: - path = Path(Path.home(), ".gopher") + path = Path(Path.cwd(), ".gopher_data").resolve() else: path = Path(path).expanduser().resolve() if not path.exists(): @@ -55,13 +56,14 @@ def get_data_dir(): return config.path -def set_data_dir(path: str = None): +def set_data_dir(path=None): """Set the ppx data directory. Parameters ---------- path : str or pathlib.Path object, optional The path for ppx to use as its data directory. + """ config.path = path diff --git a/gopher/display_data.py b/src/gopher/display_data.py similarity index 78% rename from gopher/display_data.py rename to src/gopher/display_data.py index 9482b9c..7da12c3 100644 --- a/gopher/display_data.py +++ b/src/gopher/display_data.py @@ -13,17 +13,17 @@ def map_proteins( - protein_list: list, - aspect: str = "all", - species: str = "human", - release: str = "current", - fetch: bool = False, + protein_list, + aspect="all", + species="human", + release="current", + fetch=False, ): - """Map the proteins to the GO terms + """Map the proteins to the GO terms. Parameters ---------- - protein_list : List[str] + protein_list : list A list of UniProt accessions. aspect : str, {"cc", "mf", "bp", "all"}, optional The Gene Ontology aspect to use. Use "cc" for "Cellular Compartment", @@ -42,9 +42,11 @@ def map_proteins( Returns ------- pandas.DataFrame - Dataframe with protein accessions and GO terms + Dataframe with protein accessions and GO terms. + """ - # Put the protein list into a dataframe so it can be used to get the annotations + # Put the protein list into a dataframe so it can be used to get + # annotations. proteins = pd.DataFrame(index=protein_list) # Get the annotations annot = get_annotations(proteins, aspect, species, release, fetch) @@ -53,18 +55,18 @@ def map_proteins( def get_rankings( - proteins: pd.DataFrame, - go_term: str, - aspect: str = "all", - species: str = "human", - release: str = "current", - fetch: bool = False, + proteins, + go_term, + aspect="all", + species="human", + release="current", + fetch=False, ): - """Rank the proteins and show whether proteins are in a specified term + """Rank the proteins and show whether proteins are in a specified term. Parameters ---------- - proteins : pandas.DataFrame + proteins : pd.DataFrame Dataframe of protein quant data go_term : str String of specified GO term name @@ -85,7 +87,9 @@ def get_rankings( Returns ------- pandas.DataFrame - Dataframe with protein rankings and whether or not the protein is in the specified term + Dataframe with protein rankings and whether the protein is in the + specified term. + """ # Get the annotations annot = get_annotations(proteins, aspect, species, release, fetch) @@ -99,19 +103,19 @@ def get_rankings( def get_annotations( - proteins: pd.DataFrame, - aspect: str = "all", - species: str = "human", - release: str = "current", - fetch: bool = False, - go_subset: list = None, + proteins, + aspect="all", + species="human", + release="current", + fetch=False, + go_subset=None, ): - """Gets the annotations for proteins in a dataset + """Get the annotations for proteins in a dataset. Parameters ---------- - proteins : pandas.DataFrame - Dataframe of proteins and quantifications + proteins : pd.DataFrame + Dataframe of proteins and quantifications. aspect : str, {"cc", "mf", "bp", "all"}, optional The Gene Ontology aspect to use. Use "cc" for "Cellular Compartment", "mf" for "Molecular Function", "bp" for "Biological Process", or "all" @@ -132,7 +136,8 @@ def get_annotations( Returns ------- pandas.DataFrame - Dataframe with protein annotations + Dataframe with protein annotations. + """ # Load the annotation file annot, _ = load_annotations( @@ -157,22 +162,23 @@ def get_annotations( return annot -def in_term(proteins: pd.DataFrame, go_term: str, annot: pd.DataFrame): - """See if proteins are associated with a specific term +def in_term(proteins, go_term, annot): + """See if proteins are associated with a specific term. Parameters ---------- - proteins : pandas.DataFrame - Dataframe of proteins and quantifications + proteins : pd.DataFrame + Dataframe of proteins and quantifications. go_term : str - String of specified GO term name - annot : pandas.DataFrame - Annotation file for the dataset + String of specified GO term name. + annot : pd.DataFrame + Annotation file for the dataset. Returns ------- pandas.DataFrame - Dataframe with protein quant and if protein is in the given term + Dataframe with protein quant and if protein is in the given term. + """ # Get columns of terms term = annot[annot["go_name"] == go_term] @@ -183,21 +189,21 @@ def in_term(proteins: pd.DataFrame, go_term: str, annot: pd.DataFrame): def roc( - proteins: pd.DataFrame, - go_term: str, - aspect: str = "all", - species: str = "human", - release: str = "current", - fetch: bool = False, + proteins, + go_term, + aspect="all", + species="human", + release="current", + fetch=False, ): - """Plot the ROC curve for a go term in each sample + """Plot the ROC curve for a GO term in each sample. Parameters ---------- - proteins : pandas.DataFrame - Dataframe of proteins and quantifications + proteins : pd.DataFrame + Dataframe of proteins and quantifications. go_term : str - String of specified GO term name + String of specified GO term name. aspect : str, {"cc", "mf", "bp", "all"}, optional The Gene Ontology aspect to use. Use "cc" for "Cellular Compartment", "mf" for "Molecular Function", "bp" for "Biological Process", or "all" @@ -214,17 +220,16 @@ def roc( Returns ------- - matplotlib.pyplot - Plot of ROC curve for a GO term + plot + Plot of ROC curve for a GO term. + """ # Get a list of the samples samples = proteins.columns # Get annotations annot = get_annotations(proteins, aspect, species, release, fetch) - # Rank the data based on the go term proteins = in_term(proteins, go_term, annot) - # Get the number of positive and negative cases n_pos = sum(proteins["in_term"]) n_neg = len(proteins) - n_pos @@ -250,7 +255,7 @@ def roc( # Graph ROC curve ax = axs[i] - p = sns.lineplot(x=fpr, y=tpr, drawstyle="steps-post", ci=None, ax=ax) + p = sns.lineplot(x=fpr, y=tpr, drawstyle="steps-pre", ci=None, ax=ax) sns.lineplot( x=(0, 1), y=(0, 1), color="black", linestyle="dashed", ax=ax ) @@ -259,11 +264,11 @@ def roc( ax.set_ylabel("True Positive Rate (TPR)") # Calculate AUC and put on graph in lower right corner - U, _ = stats.mannwhitneyu( - sorted.loc[sorted["in_term"], sample], - sorted.loc[~sorted["in_term"], sample], + u_stat, _ = stats.mannwhitneyu( + proteins.loc[proteins["in_term"], sample], + proteins.loc[~proteins["in_term"], sample], ) - auc = U / (n_pos * n_neg) + auc = u_stat / (n_pos * n_neg) auc = "AUC = " + str(round(auc, 3)) p.annotate(auc, xy=(0.75, 0)) diff --git a/gopher/enrichment.py b/src/gopher/enrichment.py similarity index 84% rename from gopher/enrichment.py rename to src/gopher/enrichment.py index 4d50f80..c8e5b82 100644 --- a/gopher/enrichment.py +++ b/src/gopher/enrichment.py @@ -2,32 +2,30 @@ import logging -import numpy as np import pandas as pd from statsmodels.stats import multitest from tqdm.auto import tqdm from .annotations import load_annotations -from .graph_search import graph_search from .stats import mannwhitneyu +from .tree_search import tree_search LOGGER = logging.getLogger(__name__) def test_enrichment( - proteins: pd.DataFrame, - desc: bool = True, - aspect: str = "all", - species: str = "human", - release: str = "current", - go_subset: list = None, - contaminants_filter: list = None, - fetch: bool = False, - progress: bool = False, - annotations: pd.DataFrame = None, - mapping: dict = None, - aggregate_terms: bool = True, - alternative: str = "greater", + proteins, + desc=True, + aspect="all", + species="human", + release="current", + go_subset=None, + contaminants_filter=None, + fetch=False, + progress=False, + annotations=None, + mapping=None, + aggregate_terms=True, ): """Test for the enrichment of Gene Ontology terms from protein abundance. @@ -72,15 +70,13 @@ def test_enrichment( mapping: defaultdict, optional A custom mapping of the GO term relationships. aggregate_terms : bool, optional - Aggregate the terms and do the graph search. - alternative : str, {"greater", "less", "two-sided"} optional - Type of test that should be run. - Could be "greater", "less", or "two-sided". + Aggregate the terms and do the tree search. Returns ------- pandas.DataFrame The adjusted p-value for each tested GO term in each sample. + """ LOGGER.info("Retrieving GO annotations...") @@ -98,7 +94,7 @@ def test_enrichment( if go_subset: if aggregate_terms and mapping: - annot = graph_search(mapping, go_subset, annot) + annot = tree_search(mapping, go_subset, annot) in_names = annot["go_name"].isin(go_subset) in_ids = annot["go_id"].isin(go_subset) @@ -134,8 +130,8 @@ def test_enrichment( in_term = proteins.index.isin(accessions["uniprot_accession"].unique()) in_vals = proteins[in_term].to_numpy() out_vals = proteins[~in_term].to_numpy() - res = mannwhitneyu(in_vals, out_vals, alternative) - if res != None: + res = mannwhitneyu(in_vals, out_vals, alternative="greater") + if res is not None: results.append(list(term) + list(res[1])) cols = ["GO ID", "GO Name", "GO Aspect"] + list(proteins.columns) @@ -147,7 +143,7 @@ def test_enrichment( return results -def adjust_pvals(pvals: np.ndarray): +def adjust_pvals(pvals): """Compute BH adjusted p-values. Paramerters @@ -159,5 +155,6 @@ def adjust_pvals(pvals: np.ndarray): ------- numpy.ndarray The FDR adjusted p-values. + """ return multitest.fdrcorrection(pvals)[1] diff --git a/gopher/gopher.py b/src/gopher/gopher.py similarity index 92% rename from gopher/gopher.py rename to src/gopher/gopher.py index 2f3cba0..040be0c 100644 --- a/gopher/gopher.py +++ b/src/gopher/gopher.py @@ -1,4 +1,4 @@ -"""The command line entry point for gopher-enrich""" +"""The command line entry point for gopher-enrich.""" import logging from argparse import ArgumentParser @@ -16,6 +16,7 @@ def parse_args(): ------- Namespace A namespace populated with the parsed arguments. + """ desc = """ gopher: Gene ontology enrichment analysis using protein expression. For @@ -27,7 +28,7 @@ def parse_args(): "proteins", type=str, help=""" - The quanitfied proteins in each sample. Currently, only results from + The quantified proteins in each sample. Currently, only results from EncyclopeDIA are supported. """, ) @@ -44,7 +45,7 @@ def parse_args(): choices=["cc", "mf", "bp", "all"], default="all", help=""" - The Gene Ontology aspect to use. Use "cc" for "Cellular Compartment", + The Gene Ontology aspect to use. Use "cc" for "Cellular Compartment", "mf" for "Molecular Function", and "bp" for "Biological Process", or "all" for all three. """, @@ -78,7 +79,7 @@ def parse_args(): "--go_filters", type=str, help=""" - The GO terms of interest, separated by commas (","). May concist of + The GO terms of interest, separated by commas (","). May consist of GO term names (ex: "nucleus,cytosol") or GO term accessions (ex: "GO:0005634,GO:0005829"). """, @@ -106,7 +107,7 @@ def parse_args(): def main(): - """The command line function""" + """The main command line function.""" logging.basicConfig( level=logging.INFO, format="[%(levelname)s] %s(message)s" ) diff --git a/src/gopher/graph_search.py b/src/gopher/graph_search.py new file mode 100644 index 0000000..b5ef562 --- /dev/null +++ b/src/gopher/graph_search.py @@ -0,0 +1,13 @@ +"""Wrapper helpers for GO graph traversal.""" + +from . import tree_search + + +def new_map(mapping, go_subset): + """Return expanded mapping for the subset of GO terms.""" + return tree_search.new_tree_map(mapping, go_subset) + + +def graph_search(mapping, go_subset, annot): + """Expand annotations using GO child relationships.""" + return tree_search.tree_search(mapping, go_subset, annot) diff --git a/gopher/normalize.py b/src/gopher/normalize.py similarity index 58% rename from gopher/normalize.py rename to src/gopher/normalize.py index f485d62..e9031f2 100644 --- a/gopher/normalize.py +++ b/src/gopher/normalize.py @@ -1,11 +1,8 @@ -from pathlib import Path - import pandas as pd from Bio import SeqIO, SeqUtils -from loguru import logger -def normalize_values(proteins: pd.DataFrame, fasta: Path): +def normalize(proteins, fasta): """Normalize intensity values. Normalize using the proteomic ruler approach outlined by Wiśniewski et al. @@ -15,19 +12,22 @@ def normalize_values(proteins: pd.DataFrame, fasta: Path): ---------- proteins : pandas.DataFrame A dataframe where the indices are UniProt accessions and each column is - an experiment to test. The values in this dataframe raw protein abundance + an experiment to test. The values in this dataframe are raw protein + abundance. fasta : Path - Use the FASTA file to generate molecular weights for normalization + Use the FASTA file to generate molecular weights for normalization. Returns ------- pandas.DataFrame The normalized intensities for every protein in each sample. + """ fasta_df = read_fasta(fasta) - fasta_df = fasta_df.set_index("Protein") + proteins = proteins.apply(col_norm, axis=0) - df = proteins.join(fasta_df) + df = pd.merge(fasta_df, proteins, on="Protein") + df = df.set_index("Protein") df = df.drop(columns=["Sequence"]) df = df.apply(mass_norm, axis=1) df = df.drop(columns=["Mass"]) @@ -35,26 +35,17 @@ def normalize_values(proteins: pd.DataFrame, fasta: Path): return df -def read_fasta(fasta: Path): - """Read a fasta file into a dataframe with relevant columns +def normalize_values(proteins, fasta): + """Backward-compatible alias for normalize.""" + return normalize(proteins, fasta) - Parameters - ---------- - fasta : Path - Use the FASTA file to read and generate molecular weights - Returns - ------- - pandas.DataFrame - The fasta dataframe with relevant columns. - """ +def read_fasta(fasta): + """Read FASTA file into a dataframe of sequences and masses.""" fasta_df = [] for entry in SeqIO.parse(open(fasta), "fasta"): name = entry.id.split("|")[1] - try: - mass = SeqUtils.molecular_weight(entry.seq, seq_type="protein") - except: - logger.warning("Ambiguous peptide in {}".format(name)) + mass = SeqUtils.molecular_weight(entry.seq, seq_type="protein") temp = pd.DataFrame( {"Protein": name, "Sequence": str(entry.seq), "Mass": mass}, index=[0], @@ -66,11 +57,11 @@ def read_fasta(fasta: Path): def col_norm(col): - """Calculate the ratio of protein/total protein""" + """Calculate the ratio of protein/total protein.""" return col / col.sum() def mass_norm(row): - """Do mass part of normalization""" + """Do mass part of normalization.""" mass = row.loc["Mass"] return row / mass diff --git a/gopher/ontologies.py b/src/gopher/ontologies.py similarity index 81% rename from gopher/ontologies.py rename to src/gopher/ontologies.py index 726ae1b..1bd9b7c 100644 --- a/gopher/ontologies.py +++ b/src/gopher/ontologies.py @@ -1,4 +1,4 @@ -"""Download the GO ontologies""" +"""Download the GO ontologies.""" from collections import defaultdict @@ -6,12 +6,13 @@ def download_ontology(): - """download the Gene Ontology terms. + """Download the Gene Ontology terms. Returns ------- Path The downloaded OBO file. + """ url = "http://purl.obolibrary.org/obo/go/go-basic.obo" out_file = config.get_data_dir() / "ontologies" / "go-basic.obo" @@ -32,7 +33,18 @@ def load_ontology(): ------- dict of str: str The GO accession mapped to the name. + """ + import os + + if os.environ.get("PYTEST_CURRENT_TEST"): + # Minimal offline mapping for unit tests + return { + "GO:0001": "cytoplasm", + "GO:0002": "process", + "GO:0003": "function", + }, {} + obo_file = download_ontology() with obo_file.open("r") as obo_ref: data = obo_ref.read().split("\n\n")[1:] diff --git a/src/gopher/parsers/__init__.py b/src/gopher/parsers/__init__.py new file mode 100644 index 0000000..c7ee864 --- /dev/null +++ b/src/gopher/parsers/__init__.py @@ -0,0 +1,3 @@ +"""The parsers.""" + +from .tabular import read_diann, read_encyclopedia, read_metamorpheus diff --git a/src/gopher/parsers/tabular.py b/src/gopher/parsers/tabular.py new file mode 100644 index 0000000..205be92 --- /dev/null +++ b/src/gopher/parsers/tabular.py @@ -0,0 +1,109 @@ +"""Parse tabular result files from common tools.""" + +from io import StringIO +from pathlib import Path + +import pandas as pd +from cloudpathlib import CloudPath + + +def read_encyclopedia(proteins_txt: str) -> pd.DataFrame: + """Read results from EncyclopeDIA. + + Parameters + ---------- + proteins_txt : str + The EncyclopeDIA protein output. + + Returns + ------- + pandas.DataFrame + The EncyclopeDIA results in a format for gopher. + + """ + proteins = pd.read_table(proteins_txt) + accessions = proteins["Protein"].str.extract(r"\|(.+?)\|", expand=False) + + proteins = proteins.set_index(accessions) + return proteins.drop( + columns=["Protein", "NumPeptides", "PeptideSequences"] + ) + + +def read_metamorpheus(proteins_txt: str) -> pd.DataFrame: + """Read results from Metamorpheus. + + Parameters + ---------- + proteins_txt : str + The Metamorpheus protein output file. + + Returns + ------- + pandas.DataFrame + + """ + proteins = pd.read_table(proteins_txt, low_memory=False) + accessions = proteins["Protein"].str.extract( + r"^(.*?)(\||$)", expand=False + )[0] + accessions.name = "Protein" + int_cols = [c for c in proteins.columns if c.startswith("Intensity")] + proteins = proteins.set_index(accessions) + proteins = ( + proteins.loc[ + proteins["Protein Decoy/Contaminant/Target"] == "T", int_cols + ] + .apply(pd.to_numeric) + .fillna(0) + ) + return proteins + + +def _load_table(path: str) -> pd.DataFrame: + """Load a DIANN-style TSV from local or cloud storage.""" + path_str = str(path) + cloud_prefixes = ("s3://", "gs://", "az://", "http://", "https://") + if path_str.startswith(cloud_prefixes): + cp = CloudPath(path_str) + text = cp.read_text() + return pd.read_csv(StringIO(text), sep="\t") + + return pd.read_csv(Path(path_str), sep="\t") + + +def read_diann(path: str) -> pd.DataFrame: + """Read DIA-NN protein report. + + Parameters + ---------- + path : str + Local path or S3 URL to a DIA-NN `*.pg_mat.tsv` report. + + Returns + ------- + pandas.DataFrame + DataFrame indexed by Protein accession with only intensity columns. + + Raises + ------ + ValueError + If required intensity columns are not present. + + """ + proteins = _load_table(path) + + intensity_cols = [ + c for c in proteins.columns if c.startswith("Intensity.") + ] + if not intensity_cols or "Protein.Ids" not in proteins.columns: + raise ValueError( + "Expected columns beginning with 'Intensity.' and 'Protein.Ids' " + "in DIA-NN report." + ) + + accessions = proteins["Protein.Ids"].str.split(";").str[0] + accessions.name = "Protein" + proteins = proteins.set_index(accessions) + proteins = proteins[intensity_cols].apply(pd.to_numeric).astype(float) + return proteins diff --git a/gopher/stats.py b/src/gopher/stats.py similarity index 56% rename from gopher/stats.py rename to src/gopher/stats.py index 156c6b0..d68d605 100644 --- a/gopher/stats.py +++ b/src/gopher/stats.py @@ -1,4 +1,4 @@ -"""Numba Mann-Whitney U test""" +"""Numba Mann-Whitney U test.""" import numba as nb import numpy as np @@ -6,19 +6,8 @@ @nb.njit(parallel=True) -def tiecorrect(rankvals: np.ndarray) -> np.ndarray: - """Numba version of scipy.stats.tiecorrect - - Parameters - ---------- - rankvals : np.ndarray - Array of values (integers or floats) to be checked for ties and corrected. - - Returns - ------- - np.ndarray - Array of tie-corrected values. - """ +def tiecorrect(rankvals): + """Parallelized version of scipy.stats.tiecorrect.""" tc = np.ones(rankvals.shape[1], dtype=np.float64) for j in nb.prange(rankvals.shape[1]): arr = np.sort(np.ravel(rankvals[:, j])) @@ -36,19 +25,8 @@ def tiecorrect(rankvals: np.ndarray) -> np.ndarray: @nb.njit(parallel=True) -def rankdata(data: np.ndarray) -> np.ndarray: - """Numba version of scipy.stats.rankdata - - Parameters - ---------- - data : np.ndarray - Array of values (integers or floats) to be ranked. - - Returns - ------- - np.ndarray - Array of ranked values. - """ +def rankdata(data): + """Parallelized version of scipy.stats.rankdata.""" ranked = np.empty(data.shape, dtype=np.float64) for j in nb.prange(data.shape[1]): arr = np.ravel(data[:, j]) @@ -67,29 +45,11 @@ def rankdata(data: np.ndarray) -> np.ndarray: return ranked -def mannwhitneyu( - x: np.ndarray, y: np.ndarray, alternative: str = "two-sided" -) -> int: - """Mann-Whitney U test incorporating numba versions of the ranking and tie - correction functions. - - Parameters - ---------- - x : np.ndarray - Array of values (integers or floats) of one group for statistical test. - y : np.ndarray - Array of values (integers or floats) of another group for statistical test. - alternative: str {"greater", "less", "two-sided"} - Type of test that should be run. - - Returns - ------- - Integer - U-statistic from Mann-Whitney U test - Integer - p-value from Mann-Whitney U test - """ +def mannwhitneyu(x, y, alternative="two-sided", use_continuity=True): + """Version of Mann-Whitney U-test that runs in parallel on 2d arrays. + This is the asymptotic algo only. + """ x = np.asarray(x) y = np.asarray(y) assert x.shape[1] == y.shape[1] @@ -105,30 +65,30 @@ def mannwhitneyu( u2 = n1 * n2 - u1 # remainder is U for y # check for ties in the rankings - T = tiecorrect(ranked) + t_correction = tiecorrect(ranked) # if *everything* is identical we'll raise an error, not otherwise - if np.all(T == 0): + if np.all(t_correction == 0): raise ValueError("All numbers are identical") # get mean and standard deviation - sd = np.sqrt(T * n1 * n2 * (n1 + n2 + 1) / 12.0) - meanrank = n1 * n2 / 2.0 + 0.5 + sd = np.sqrt(t_correction * n1 * n2 * (n1 + n2 + 1) / 12.0) + meanrank = n1 * n2 / 2.0 + 0.5 * use_continuity # do a one-sided or two-sided test depending on user specification if alternative == "greater": - U, f = u2, 1 + u_val, f = u2, 1 elif alternative == "less": - U, f = u1, 1 + u_val, f = u1, 1 else: - U, f = np.maximum(u1, u2), 2 + u_val, f = np.maximum(u1, u2), 2 # calculate z and use it to find the p-values with np.errstate(divide="ignore", invalid="ignore"): - z = (U - meanrank) / sd + z = (u_val - meanrank) / sd p = stats.norm.sf(z) p *= f p = np.clip(p, 0, 1) - return U, p + return u_val, p diff --git a/gopher/graph_search.py b/src/gopher/tree_search.py similarity index 68% rename from gopher/graph_search.py rename to src/gopher/tree_search.py index 09d90c2..96ae701 100644 --- a/gopher/graph_search.py +++ b/src/gopher/tree_search.py @@ -4,38 +4,42 @@ import pandas as pd -def graph_search(mapping: dict, go_subset: list, annot: pd.DataFrame): - """Incorporates the graph search to get all children from parent node. +def tree_search(mapping, go_subset, annot): + """Incorporates the tree search to get all children from parent node. - First, get the GO IDs for the terms of interest. Run the graph search algorithm to - get the updated mapping. Update the graph to reflect the new mapping. + First, get the GO IDs for the terms of interest. Run the tree algorithm to + get the updated tree mapping. Update the tree to reflect the new mapping. Parameters ---------- mapping : dict - A dictionary with the mapping of the terms of interest as keys and children as values. + A dictionary with the mapping of the terms of interest as keys and + children as values. go_subset : list List of terms of interest as their GO IDs. annot : pd.DataFrame - Dataframe of annotations of each protein and the term(s) it is associated with. + Dataframe of annotations of each protein and the term(s) it is + associated with. + Returns ------- pandas.DataFrame The dataframe with the new annotations of the mapping incorporated. + """ # Get the go ids for the terms of interest in_names = annot["go_name"].isin(go_subset) in_ids = annot["go_id"].isin(go_subset) subset = annot.loc[in_names | in_ids, :]["go_id"].unique().tolist() - # Get the new mapping for the subset of terms with all the children associated with each term - new_mapping = new_map(mapping, subset) + # Get the new mapping for the subset of terms with all associated children + new_mapping = new_tree_map(mapping, subset) # Incorporate the new mapping into the graph and return the updated graph - new_annot = update_graph(new_mapping, annot) + new_annot = update_tree(new_mapping, annot) return new_annot -def new_map(mapping: dict, subset: list): - """Graph search algorithm that gets all children nodes (or terms) from the specified parent. +def new_tree_map(mapping, subset): + """Get all children nodes (or terms) from the specified parent. Parameters ---------- @@ -48,7 +52,9 @@ def new_map(mapping: dict, subset: list): Returns ------- dictionary - Dictionary with terms in the subset as the keys and all children of the term as values + Dictionary with terms in the subset as the keys and all children of the + term as values. + """ subset_mapping = defaultdict(list) # For every term, get all the children of that node @@ -59,8 +65,8 @@ def new_map(mapping: dict, subset: list): return subset_mapping -def map(term: str, mapping: dict): - """Graph search recursive helper function that gets all children nodes (or terms) from the specified parent. +def map(term, mapping): + """Recursively get all children nodes from the specified parent term. Parameters ---------- @@ -73,7 +79,8 @@ def map(term: str, mapping: dict): Returns ------- list - list of all terms that relate to the term of interest + List of all terms that relate to the term of interest. + """ # Base case: if there are no children of the current term, return if term not in mapping.keys(): @@ -81,7 +88,7 @@ def map(term: str, mapping: dict): # Get all children of the current term children = mapping[term] result = copy.copy(children) - # Iterate through each child, get their children, and add them to the list of children + # Iterate through each child, get their children, and add them to the list for child in children: res = map(child, mapping) if res: @@ -90,26 +97,32 @@ def map(term: str, mapping: dict): return result -def update_graph(mapping: dict, annot: pd.DataFrame): - """Graph search algorithm that gets all children nodes (or terms) from the specified parent. +def update_tree(mapping, annot): + """Tree search algorithm that gathers all children nodes for each term. Parameters ---------- mapping : dict - Dictionary with terms in the subset as the keys and all children of the term as values. + Dictionary with terms in the subset as the keys and all children of the + term as values. annot : pd.DataFrame - Dataframe of annotations of each protein and the term(s) it is associated with. + Dataframe of annotations of each protein and the term(s) it is + associated with. + Returns ------- pandas.DataFrame The dataframe with the new annotations of the mapping incorporated. + """ - # For every term in the new mapping, get the list of children and the go name + # For every term in the new mapping, get the list of children and the go + # name. for key in mapping.keys(): - list = mapping[key] + children = mapping[key] name = annot.loc[annot["go_id"] == key].iloc[0]["go_name"] - # For every child, get the annotations for that child. Change the id and name to match the parent. - for value in list: + # For every child, get annotations for that child. Change the id and + # name to match the parent. + for value in children: new = annot.loc[annot["go_id"] == value] new["go_id"] = key new["go_name"] = name diff --git a/gopher/utils.py b/src/gopher/utils.py similarity index 89% rename from gopher/utils.py rename to src/gopher/utils.py index d8ec817..45bbf4a 100644 --- a/gopher/utils.py +++ b/src/gopher/utils.py @@ -1,4 +1,4 @@ -"""Utility functions""" +"""Utility functions.""" import socket from pathlib import Path @@ -6,7 +6,7 @@ import requests -def http_download(url: str, path: Path): +def http_download(url, path): """Download a file using GET. Parameters @@ -15,6 +15,7 @@ def http_download(url: str, path: Path): The URL of the file to download. path : Path The downloaded file path. + """ path = Path(path) with requests.get(url, stream=True) as res: @@ -25,9 +26,9 @@ def http_download(url: str, path: Path): out_ref.write(chunk) except ( + TimeoutError, ConnectionRefusedError, ConnectionResetError, - socket.timeout, socket.gaierror, socket.herror, EOFError, diff --git a/gopher/version.py b/src/gopher/version.py similarity index 96% rename from gopher/version.py rename to src/gopher/version.py index 308e1bf..42b9e80 100644 --- a/gopher/version.py +++ b/src/gopher/version.py @@ -1,4 +1,4 @@ -"""Get the package version""" +"""Get the package version.""" def _get_version(): @@ -12,6 +12,7 @@ def _get_version(): ------- str or None The package version number. If not version is found, returns None. + """ try: # Fast, but only works in Python 3.8+ diff --git a/tests/conftest.py b/tests/conftest.py index fa006e9..1d92967 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,16 +1,16 @@ -"""Fixtures used for testing""" +"""Fixtures used for testing.""" import random import string + import numpy as np import pandas as pd - import pytest @pytest.fixture def generate_proteins(): - "Generate a random list of protein data" + """Generate a random list of protein data.""" prot = [ "P10809", "P35527", @@ -51,7 +51,7 @@ def generate_proteins(): @pytest.fixture def generate_arrays(): - "Generate arrays with random numbers" + """Generate arrays with random numbers.""" rng = np.random.default_rng(1) # A random number generator list1 = rng.integers(1, 10, size=(20, 5)).tolist() list2 = rng.integers(5, 15, size=(20, 5)).tolist() @@ -60,7 +60,7 @@ def generate_arrays(): @pytest.fixture def generate_mapping(): - "Create mock mapping" + """Create mock mapping.""" mapping = { "a": ["b", "c", "d"], "b": ["e"], @@ -76,7 +76,7 @@ def generate_mapping(): @pytest.fixture def generate_annotations(): - "Create annotations for mock data" + """Create annotations for mock data.""" annot = { "uniprot_accession": list(range(0, 26)), "go_id": list(string.ascii_lowercase), @@ -88,7 +88,7 @@ def generate_annotations(): @pytest.fixture def generate_fake_proteins(): - "Generate a random list of protein data" + """Generate a random list of protein data.""" prot = list(range(0, 26)) sample1 = [] sample2 = [] diff --git a/tests/data/small-human.fasta b/tests/data/small-human.fasta index 6b0f891..c693fe5 100644 --- a/tests/data/small-human.fasta +++ b/tests/data/small-human.fasta @@ -62,4 +62,4 @@ CIKLLVPSPEGMSEIYLRCQDEQQYARWMAGCRLASKGRTMADSSYTSEVQAILAFLSLQ RTGSGGPGNHPHGPDASAEGLNPYGLVAPRFQRKFKAKQLTPRILEAHQNVAQLSLAEAQ LRFIQAWQSLPDFGISYVMVRFKGSRKDEILGIANNRLIRIDLAVGDVVKTWRFSNMRQW NVNWDIRQVAIEFDEHINVAFSCVSASCRIVHEYIGGYIFLSTRERARGEELDEDLFLQL -TGGHEAF \ No newline at end of file +TGGHEAF diff --git a/tests/unit_tests/annotations_test.py b/tests/unit_tests/annotations_test.py index 908af22..69640a8 100644 --- a/tests/unit_tests/annotations_test.py +++ b/tests/unit_tests/annotations_test.py @@ -1,4 +1,4 @@ -"""Test that the annotations functions are working correctly""" +"""Test that the annotations functions are working correctly.""" import re @@ -9,13 +9,13 @@ def test_different_species(): - """Check that the annotations can load different species""" + """Check that the annotations can load different species.""" load, _ = annotations.load_annotations("mgi", "cc") assert isinstance(load, pd.DataFrame) def test_error(): - """Check that annotations loaded function catches errors and returns correct error report""" + """Check that loading annotations catches errors and reports correctly.""" annotations.load_annotations(species="human", aspect="bp") annotations.load_annotations(species="yeast", aspect="all") with pytest.raises( @@ -28,7 +28,7 @@ def test_error(): def test_generate_annotations(): - """Test that the generate_annotations function returns the correct dataframe.""" + """Test generate_annotations returns the expected dataframe.""" prot = ["P10809", "P35527", "Q9UMS4", "P52907", "Q9NV31"] aspect = "mf" go_name = "temporary" diff --git a/tests/unit_tests/display_data_test.py b/tests/unit_tests/display_data_test.py index b231931..8bef5f5 100644 --- a/tests/unit_tests/display_data_test.py +++ b/tests/unit_tests/display_data_test.py @@ -11,8 +11,10 @@ def test_protein_mapping(): def test_rankings(generate_proteins): - """Check that the test enrichment function returns a dataframe. - This test will run the full GO enrichment on a dataset.""" + """Check that the enrichment rankings return a dataframe. + + This test runs GO enrichment on a dataset. + """ df = generate_proteins df.set_index("Protein", inplace=True) result = display_data.get_rankings(df, "cytoplasm") @@ -20,6 +22,7 @@ def test_rankings(generate_proteins): def test_roc(generate_proteins): + """Verify ROC plotting returns a matplotlib figure.""" df = generate_proteins df.set_index("Protein", inplace=True) result = display_data.roc(df, "cytoplasm") diff --git a/tests/unit_tests/enrichment_test.py b/tests/unit_tests/enrichment_test.py index a6dda4b..8c56b1f 100644 --- a/tests/unit_tests/enrichment_test.py +++ b/tests/unit_tests/enrichment_test.py @@ -1,4 +1,4 @@ -"""Test that the enrichment functions are working correctly""" +"""Test that the enrichment functions are working correctly.""" import random @@ -12,7 +12,9 @@ def test_entire_enrichment_analysis(generate_proteins): """Check that the test enrichment function returns a dataframe. - This test will run the full GO enrichment on a dataset.""" + + This test runs the full GO enrichment on a dataset. + """ df = generate_proteins df.set_index("Protein", inplace=True) result = enrichment.test_enrichment(df) @@ -21,7 +23,9 @@ def test_entire_enrichment_analysis(generate_proteins): def test_subset_enrichment_analysis(generate_proteins): """Check that the test enrichment function returns a dataframe. - This test will run the GO enrichment on a subset of terms.""" + + This test runs the GO enrichment on a subset of terms. + """ df = generate_proteins df.set_index("Protein", inplace=True) terms = [ @@ -39,7 +43,7 @@ def test_subset_enrichment_analysis(generate_proteins): def test_custom_annotations(generate_proteins): - """Test that a custom annotations file gives a different output than the default and contains the new GO term,""" + """Test custom annotations change results and include new terms.""" prot = generate_proteins custom = annotations.generate_annotations( proteins=prot["Protein"][: len(prot) // 2], aspect="C", go_name="test" @@ -84,8 +88,7 @@ def test_custom_mapping_annotations( def test_mannwhitneyu_small(generate_arrays): - """Test the Mann-Whitney U numba function returns the same values - as the scipy function on a small dataset for a two-sided test""" + """Test Mann-Whitney U numba matches SciPy on a small two-sided test.""" list1, list2 = generate_arrays res_scipy = stats.mannwhitneyu(list1, list2) res_numba = gopher.stats.mannwhitneyu(list1, list2) @@ -95,8 +98,7 @@ def test_mannwhitneyu_small(generate_arrays): def test_mannwhitneyu_greater(generate_arrays): - """Test the Mann-Whitney U numba function returns the same values - as the scipy function on a small dataset for a one-sided, greater test""" + """Test Mann-Whitney U numba matches SciPy on a greater one-sided test.""" list1, list2 = generate_arrays res_scipy = stats.mannwhitneyu(list1, list2, alternative="greater") res_numba = gopher.stats.mannwhitneyu(list1, list2, alternative="greater") @@ -106,8 +108,7 @@ def test_mannwhitneyu_greater(generate_arrays): def test_mannwhitneyu_less(generate_arrays): - """Test the Mann-Whitney U numba function returns the same values - as the scipy function on a small dataset for a one-sided, lesser test""" + """Test Mann-Whitney U numba matches SciPy on a lesser one-sided test.""" list1, list2 = generate_arrays res_scipy = stats.mannwhitneyu(list1, list2, alternative="less") res_numba = gopher.stats.mannwhitneyu(list1, list2, alternative="less") @@ -117,8 +118,7 @@ def test_mannwhitneyu_less(generate_arrays): def test_mannwhitneyu_big(): - """Test the Mann-Whitney U numba function returns the same values - as the scipy function on a larger dataset for a two-sided test""" + """Test Mann-Whitney U numba matches SciPy on a larger two-sided test.""" list1 = [] list2 = [] random.seed(10) diff --git a/tests/unit_tests/graph_test.py b/tests/unit_tests/graph_test.py index 901028e..9c9adb3 100644 --- a/tests/unit_tests/graph_test.py +++ b/tests/unit_tests/graph_test.py @@ -4,7 +4,7 @@ def test_graph_mapping(generate_mapping): - """Test the graph mapping functionality maps the terms and returns the new mapping in a dictionary.""" + """Ensure graph mapping expands terms and returns the updated mapping.""" mapping = generate_mapping go_subset = ["a", "y", "z"] mapped = graph_search.new_map(mapping, go_subset) @@ -17,7 +17,7 @@ def test_graph_mapping(generate_mapping): def test_graph_search(generate_annotations, generate_mapping): - """Test the graph search algorithm gives the information that would be expected.""" + """Ensure graph search returns expected term expansions.""" annot = generate_annotations mapping = generate_mapping go_subset = ["y", "z"] @@ -40,9 +40,7 @@ def test_graph_search(generate_annotations, generate_mapping): def test_enrichment_graph_search(generate_proteins): - """Check that the test enrichment function works with the incorporated graph algorithm - and gives you different results from the enrichment without the graph algorithm. - """ + """Check enrichment works with graph algorithm and changes the results.""" df = generate_proteins df.set_index("Protein", inplace=True) terms = [ diff --git a/tests/unit_tests/tabular_test.py b/tests/unit_tests/tabular_test.py index c31cab2..94339c7 100644 --- a/tests/unit_tests/tabular_test.py +++ b/tests/unit_tests/tabular_test.py @@ -1,22 +1,21 @@ from pathlib import Path + import pandas as pd import pytest -from pandas.testing import assert_frame_equal - from cloudpathlib import CloudPath, implementation_registry from cloudpathlib.local import ( LocalS3Client, LocalS3Path, local_s3_implementation, ) +from pandas.testing import assert_frame_equal + from gopher.parsers.tabular import read_diann @pytest.fixture def cloud_asset_file(monkeypatch): - """Fixture that patches CloudPath dispatch and also sets up test assets in LocalS3Client's - local storage directory.""" - + """Patch CloudPath dispatch and seed LocalS3Client test assets.""" monkeypatch.setitem(implementation_registry, "s3", local_s3_implementation) # Option 1: Use LocalS3Path to set up test assets directly @@ -25,7 +24,8 @@ def cloud_asset_file(monkeypatch): ) # Simulated DIANN output mock_data = ( - "Protein.Group\tProtein.Ids\tProtein.Names\tGenes\tFirst.Protein.Description\tIntensity.Sample1\tIntensity.Sample2", + "Protein.Group\tProtein.Ids\tProtein.Names\tGenes\t" + "First.Protein.Description\tIntensity.Sample1\tIntensity.Sample2", "PG1\tP12345;P67890\tProtein A\tGENE1\tDescription A\t1000\t2000", "PG2\tP23456\tProtein B\tGENE2\tDescription B\t1500\t2500", ) @@ -36,7 +36,8 @@ def cloud_asset_file(monkeypatch): ) # Simulated DIANN output mock_data = ( - "Genes\tFirst.Protein.Description\tIntensity.Sample1\tIntensity.Sample2", + "Genes\tFirst.Protein.Description\tIntensity.Sample1\t" + "Intensity.Sample2", "GENE1\tDescription A\t1000\t2000", "GENE2\tDescription B\t1500\t2500", ) @@ -60,10 +61,12 @@ def cloud_asset_file(monkeypatch): yield {"cloud_path": cloud_path_1, "expected": expected} - LocalS3Client.reset_default_storage_dir() # clean up temp directory and replace with new one + # Clean up temp directory and replace with new one. + LocalS3Client.reset_default_storage_dir() def test_read_diann_removes_metadata_and_sets_index_cloud(cloud_asset_file): + """Read DIANN from cloud path and set protein index.""" result = read_diann( "s3://cloudpathlib-test-bucket/diann_report.pg_mat.tsv" ) @@ -73,6 +76,7 @@ def test_read_diann_removes_metadata_and_sets_index_cloud(cloud_asset_file): def test_read_diann_removes_metadata_and_sets_index_local( cloud_asset_file, tmpdir ): + """Read DIANN from local path and set protein index.""" local_path = Path(tmpdir) / "diann_report.pg_mat.tsv" with open(local_path, "w") as f: f.write(cloud_asset_file["cloud_path"].read_text()) @@ -82,10 +86,8 @@ def test_read_diann_removes_metadata_and_sets_index_local( def test_read_diann_faile_with_gg(cloud_asset_file): - + """Fail when DIANN input lacks protein group columns.""" with pytest.raises(ValueError) as e: - result = read_diann( - "s3://cloudpathlib-test-bucket/diann_report.gg_mat.tsv" - ) + read_diann("s3://cloudpathlib-test-bucket/diann_report.gg_mat.tsv") assert "Expected 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