diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..57c2d27 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,40 @@ +# GitHub Dependabot configuration +# Note: this configuration controls Dependabot version updates. Dependabot security +# updates are configured separately in the repository security settings. +# See: +# https://docs.github.com/en/code-security/dependabot/dependabot-security-updates/about-dependabot-security-updates + +version: 2 +updates: + # GitHub Actions workflow dependencies under .github/workflows. + # See: + # https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuring-dependabot-version-updates + - package-ecosystem: "github-actions" + directory: "/" + schedule: + # Check for GitHub Actions updates every week. + interval: "weekly" + + # Python project dependencies managed by uv. + # Dependabot's uv ecosystem updates uv.lock for projects using uv. + # See: + # https://docs.github.com/en/code-security/dependabot/ecosystems-supported-by-dependabot/supported-ecosystems-and-repositories + - package-ecosystem: "uv" + directory: "/" + schedule: + # Check for project dependency updates every month. + interval: "monthly" + groups: + python-packages: + patterns: + - "*" + update-types: + - "minor" + - "patch" + + # Pre-commit hook revisions in .pre-commit-config.yaml. + - package-ecosystem: "pre-commit" + directory: "/" + schedule: + # Check for pre-commit hook updates every week. + interval: "weekly" diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..8755e6d --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,93 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + pre-commit: + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v6.0.2 + + - name: Run pre-commit (required) + uses: pre-commit/action@v3.0.1 + with: + extra_args: --all-files --show-diff-on-failure + + test: + needs: pre-commit + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] + + steps: + - name: Checkout + uses: actions/checkout@v6 + - name: Set up Python + uses: actions/setup-python@v6.2.0 + with: + python-version: ${{ matrix.python-version }} + + - name: Set up uv + uses: astral-sh/setup-uv@v8.1.0 + + - name: Install dependencies + run: uv sync --group dev --frozen + + - name: Run tests with coverage (Ubuntu 3.12) + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' + run: uv run --frozen pytest --cov=buscar --cov-report=term-missing --cov-report=xml --cov-report=html + + - name: Upload coverage artifact + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' + uses: actions/upload-artifact@v7.0.1 + with: + name: coverage-xml + path: coverage.xml + + - name: Upload HTML coverage artifact + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' + uses: actions/upload-artifact@v7.0.1 + with: + name: coverage-html + path: htmlcov + + - name: Run tests + if: matrix.os != 'ubuntu-latest' || matrix.python-version != '3.12' + run: uv run --frozen pytest + + build: + needs: test + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v6.0.2 + + - name: Set up Python + uses: actions/setup-python@v6.2.0 + with: + python-version: "3.12" + + - name: Set up uv + uses: astral-sh/setup-uv@v8.1.0 + + - name: Install dependencies + run: uv sync --group dev --frozen + + - name: Build package + run: uv build + + - name: Validate wheel import + shell: bash + run: | + uv pip install --force-reinstall dist/*.whl + uv run --frozen python -c "import buscar; print(buscar.__name__)" diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..1da5cf9 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,64 @@ +name: Publish + +on: + release: + types: [published] + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v6.0.2 + + - name: Set up Python + uses: actions/setup-python@v6.2.0 + with: + python-version: "3.12" + + - name: Set up uv + uses: astral-sh/setup-uv@v8.1.0 + + - name: Install dependencies + run: uv sync --group dev --frozen + + - name: Build package + run: uv build + + - name: Validate wheel import + shell: bash + run: | + uv pip install --force-reinstall dist/*.whl + uv run --frozen python -c "import buscar; print(buscar.__name__)" + + # Jobs run on separate runners, so pass the validated distributions to + # the publish job as an artifact instead of rebuilding them there. + - name: Upload package distributions + uses: actions/upload-artifact@v7.0.1 + with: + name: python-package-distributions + path: dist/ + + pypi-publish: + name: Upload release to PyPI + needs: build + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/buscar + # Required for PyPI Trusted Publishing. No API token or password is stored + # in GitHub; PyPI verifies this workflow run through OpenID Connect. + permissions: + id-token: write + + steps: + # Retrieve the exact distributions built and validated by the build job. + - name: Download package distributions + uses: actions/download-artifact@v8.0.1 + with: + name: python-package-distributions + path: dist/ + + - name: Publish package distributions to PyPI + uses: pypa/gh-action-pypi-publish@v1.13.0 diff --git a/.gitignore b/.gitignore index 92435f1..b26e387 100644 --- a/.gitignore +++ b/.gitignore @@ -166,6 +166,8 @@ data/ # ignoring prototype files _* +!**/__init__.py +!**/__main__.py # ignoring results files as they are generated when running the notebooks # also to prevent storing large datasets @@ -179,3 +181,9 @@ results/ # ignore ruff catch .ruff_cache/ + +# ignore for now +recipe/metal.yaml + +#ignore prototype files +_* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index dccadb0..6e936b9 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,6 +7,7 @@ repos: rev: v6.0.0 hooks: - id: check-yaml + exclude: ^recipe/meta\.yaml$ - id: check-json - id: check-merge-conflict - id: check-added-large-files diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..328d092 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,93 @@ +# Contributing + +Thanks for your interest in improving Buscar. Contributions that fix bugs, improve +documentation, add tests, or make the package easier to install and maintain are +welcome. + +## Development Setup + +Buscar uses [uv](https://docs.astral.sh/uv/) for environment and package +management. + +```bash +git clone https://github.com/WayScience/buscar.git +cd buscar +uv sync --frozen --group dev +``` + +Install the pre-commit hooks before making changes: + +```bash +uv tool install pre-commit +pre-commit install +``` + +## Checks + +Run the test suite: + +```bash +uv run --frozen pytest +``` + +Run tests with coverage: + +```bash +uv run --frozen pytest --cov=buscar --cov-report=term-missing --cov-report=xml --cov-report=html +``` + +The coverage run creates `coverage.xml` and an `htmlcov/` directory. CI uploads +these files from the Ubuntu Python 3.12 test job so maintainers can download the +machine-readable coverage report or inspect the browsable HTML coverage report +when reviewing pull requests. + +Run linting and formatting checks: + +```bash +pre-commit run --all-files +``` + +Build the package: + +```bash +uv build +uv run --frozen twine check dist/* +``` + +## Pull Requests + +Before opening a pull request: + +- Keep changes focused on one bug fix, feature, or documentation update. +- Add or update tests when behavior changes. +- Update documentation when user-facing behavior changes. +- Run `uv run --frozen pytest` and `pre-commit run --all-files`. +- Make sure generated files and large local artifacts are not committed. + +CI runs pre-commit checks, the test matrix across supported Python versions, and +package build validation. + +Dependabot checks GitHub Actions and pre-commit hooks weekly, and uv-managed +Python dependencies monthly. Review dependency update pull requests like any +other change and make sure CI passes before merging. + +## Releases + +Releases are published to PyPI through PyPI Trusted Publishing from the GitHub +Actions workflow. Maintainers should create a GitHub release only after the PyPI +project has a trusted publisher configured for this repository, the `ci.yml` +workflow, and the `pypi` GitHub environment. + +## Reporting Issues + +When reporting a bug, include: + +- The Buscar version or commit you are using. +- Your Python version and operating system. +- A minimal example or traceback that reproduces the issue. +- Any relevant input data shape, schema, or metadata column names. + +## License + +By contributing, you agree that your contribution will be licensed under the +BSD 3-Clause license used by this project. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..2ac11f6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,28 @@ +BSD 3-Clause License + +Copyright (c) 2026 Erik Serrano + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index ae00cb2..8a88fe4 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,9 @@ ![Buscar Logo](./logo/with-text-for-light-bg.svg) +[![DOI](https://img.shields.io/badge/DOI-10.64898%2F2026.04.15.718737-blue)](https://doi.org/10.64898/2026.04.15.718737) +[![Python](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13%20%7C%203.14-blue)](https://github.com/axiomcura/buscar/blob/main/pyproject.toml) + ## About Buscar (Bioactive Unbiased Single-cell Compound Assessment and Ranking) is an open-source Python package for reproducible perturbation hit calling in high-content screening that operates directly on distributions of single-cell image-based profiles. The name is also a play on the Spanish and Portuguese verb "buscar," meaning "to search" or "to seek," reflecting the framework's core goal of identifying biologically active perturbations. Traditional high-content screening approaches rely on population-based aggregated profiles to evaluate compound-induced morphological activity, which obscures the biological heterogeneity present across individual cells within a treatment group. Buscar addresses this limitation by operating directly on single-cell profiles, enabling a more nuanced and interpretable assessment of perturbation activity. @@ -16,43 +19,85 @@ Buscar requires two reference populations defining distinct morphology states, f | --- | --- | | Defining morphology signatures | Establishes the morphology reference for evaluating perturbations by comparing two control populations: a reference state (e.g., disease cells) and a target state (e.g., healthy cells). Non-parametric statistical tests (e.g., Kolmogorov-Smirnov test) are applied per feature with FDR correction to assign features to either an on-morphology signature (features significantly altered between states) or an off-morphology signature (features that remain unchanged). These signatures define which morphologies must change for a perturbation to achieve efficacy and which serve as indicators of off-target activity. | | Perturbation efficacy and specificity scoring | Scores each perturbation by computing two complementary metrics. The on-Buscar score quantifies efficacy by measuring the Earth Mover's Distance (EMD) between the perturbed and target single-cell populations using the on-morphology signature features, where a lower score indicates greater phenotypic rescue. The off-Buscar score quantifies specificity by measuring the proportion of off-morphology signature features that become significantly altered under perturbation, where a lower score indicates fewer off-target effects. -## How to install +## Installation -### 1. Clone the Repository +### Install from PyPI -Start by cloning the buscar repository and navigating into the project directory: +If you want to add Buscar to a uv-managed project, install it directly from +PyPI: + +```bash +uv add buscar +``` + +### Install from source + +If you want the current repository version before a release is available: + +```bash +uv add "buscar @ git+https://github.com/WayScience/buscar.git" +``` + +## Development + +Buscar uses [uv](https://docs.astral.sh/uv/) for environment and package management. + +1. Clone the repository ```bash git clone https://github.com/WayScience/buscar.git cd buscar ``` -### 2. Set Up a Conda Environment +2. Create the development environment + +```bash +uv sync --frozen --group dev +``` -Create and activate a dedicated Conda environment for buscar: +3. Run tests ```bash -conda create -n buscar python=3.12 -conda activate buscar +uv run --frozen pytest ``` -### 3. Install Poetry +4. Run tests with coverage -Install Poetry within your Conda environment to manage project dependencies: +```bash +uv run --frozen pytest --cov=buscar --cov-report=term-missing --cov-report=xml --cov-report=html +``` + +5. Run linting and formatting checks ```bash -conda install poetry +pre-commit run --all-files ``` -### 4. Install Project Dependencies +See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines and the full +local development checklist. + +## Build and validate -With Poetry installed and your environment activated, install all required dependencies: +Build the source distribution and wheel: ```bash -poetry install +uv build ``` -This command will set up all packages as specified in the `pyproject.toml` and `poetry.lock` files. +Validate the package metadata: + +```bash +uv run --frozen twine check dist/* +``` + +Install the built wheel locally to verify the package import: + +```bash +uv pip install --force-reinstall dist/*.whl +uv run --frozen python -c "import buscar; print(buscar.__name__, buscar.__version__)" +``` +Package metadata and dependencies are defined in `pyproject.toml`; the uv lockfile is +used for reproducible local and CI development environments. ## Notes diff --git a/buscar/preprocess.py b/buscar/preprocess.py deleted file mode 100644 index 1e60b99..0000000 --- a/buscar/preprocess.py +++ /dev/null @@ -1,200 +0,0 @@ -""" -This module contains utility functions for data preprocessing -""" - -from typing import Literal - -import polars as pl -import umap -from beartype import beartype -from sklearn.decomposition import PCA -from sklearn.preprocessing import StandardScaler - - -@beartype -def apply_pca( - profiles: pl.DataFrame, - meta_features: list[str], - morph_features: list[str], - var_explained: float | int = 0.95, - svd_solver="full", - standardize=False, - random_state=0, - **kwargs, -) -> tuple[pl.DataFrame, pl.DataFrame]: - """Apply PCA to the morphological features of the profiles DataFrame. - - Parameters - ---------- - profiles : pl.DataFrame - Input DataFrame containing morphological and metadata features. - meta_features : list[str] - List of column names corresponding to metadata features. - morph_features : list[str] - List of column names corresponding to morphological features. - var_explained : float or int, optional - If a float between 0 and 1 is provided, it specifies the fraction of variance - to be explained by the selected components (default is 0.95). If an integer is - provided, it specifies the exact number of principal components to keep. - svd_solver : str, optional - The SVD solver to use for PCA. Default is 'full'. Options include 'auto', 'full', - 'arpack', and 'randomized'. Note: The behavior of the 'n_components' parameter - depends on its type: - - If a float between 0 and 1 is provided (e.g., var_explained), it specifies - the fraction of variance to preserve, and the 'full' solver must be used. - - If an integer is provided, it specifies the exact number of principal - components to keep, and other solvers such as 'arpack' or 'randomized' may be - appropriate. - See scikit-learn's PCA documentation for details. - standardize : bool, optional - Whether to standardize the morphological features before applying PCA. - Default is False. - random_state : int, optional - Random state for reproducibility. Default is 0. - **kwargs - Additional keyword arguments for PCA that can be found here: - https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html - - Returns - ------- - pl.DataFrame - DataFrame containing the metadata features and the principal components. - - """ - - # check if there are nans in the feature space - if profiles.select(morph_features).null_count().sum_horizontal().sum() > 0: - raise ValueError( - "Input data contains NaNs. Please handle them before applying PCA." - ) - - # if the var explained is between 0 and 1 and is not "full" raise an error - if isinstance(var_explained, float) and not (0 < var_explained < 1): - raise ValueError( - "'var_explained' as a float must be between 0 and 1 (exclusive)." - "Provide an integer for number of components instead." - ) - # check if the solver is compatible with var_explained - if isinstance(var_explained, float) and svd_solver != "full": - raise ValueError( - "When 'var_explained' is a float between 0 and 1, 'svd_solver' must be set " - "to 'full' because PCA needs to determine the number of components that " - "explain the specified variance." - ) - - # standardize data if specified - if standardize: - scaler = StandardScaler() - data_scaled = scaler.fit_transform(profiles.select(morph_features).to_numpy()) - else: - data_scaled = profiles.select(morph_features).to_numpy() - - # apply PCA - pca = PCA( - n_components=var_explained, - random_state=random_state, - svd_solver=svd_solver, - **kwargs, - ) - principal_components = pca.fit_transform(data_scaled) - - # store explained variance ratio in a dictionary - explained_variance_df = pl.DataFrame( - { - "PC": [f"PC{i + 1}" for i in range(len(pca.explained_variance_ratio_))], - "Metadata_explained_variance": pca.explained_variance_, - "Metadata_explained_variance_ratio": pca.explained_variance_ratio_, - "Metadata_cumulative_explained_variance_ratio": pca.explained_variance_ratio_.cumsum(), - } - ) - - # concat metadata infromation with principal components - pca_colnames = [f"PC{i + 1}" for i in range(principal_components.shape[1])] - return pl.concat( - [ - profiles.select(meta_features), # metadata df - pl.DataFrame( - principal_components, schema=pca_colnames - ), # PCA components df - ], - how="horizontal", - ), explained_variance_df - - -@beartype -def apply_umap( - profiles: pl.DataFrame, - meta_features: list[str], - morph_features: list[str], - n_components: int = 2, - n_neighbors: int = 15, - min_dist: float = 0.1, - metric: Literal["euclidean", "cosine"] = "euclidean", - random_state: int = 0, - **kwargs, -) -> pl.DataFrame: - """Apply UMAP to the morphological features of the profiles DataFrame. - - Parameters - ---------- - profiles : pl.DataFrame - Input DataFrame containing morphological and metadata features. - meta_features : list[str] - List of column names corresponding to metadata features. - morph_features : list[str] - List of column names corresponding to morphological features. - n_components : int, optional - Number of UMAP components to keep. Default is 2. - n_neighbors : int, optional - The size of local neighborhood (in terms of number of neighboring sample points) - used for manifold approximation. Larger values result in more global views of the - manifold, while smaller values result in more local data being preserved. Default is 15. - min_dist : float, optional - The effective minimum distance between embedded points. Smaller values will result - in a more clustered/clumped embedding where nearby points on the manifold are drawn - closer together, while larger values will result in a more even dispersal of points. - Default is 0.1. - metric : str, optional - The metric to use to compute distances in high dimensional space. Default is 'euclidean'. - See UMAP documentation for other options. - random_state : int, optional - Random state for reproducibility. Default is 0. - **kwargs - Additional keyword arguments for UMAP that can be found here: - https://umap-learn.readthedocs.io/en/latest/parameters.html - - Returns - ------- - pl.DataFrame - DataFrame containing the metadata features and the UMAP components. - - """ - - # check if there are nans in the feature space - if profiles.select(morph_features).null_count().sum_horizontal().sum() > 0: - raise ValueError( - "Input data contains NaNs. Please handle them before applying UMAP." - ) - - # apply UMAP - umap_model = umap.UMAP( - n_components=n_components, - n_neighbors=n_neighbors, - min_dist=min_dist, - metric=metric, - random_state=random_state, - **kwargs, - ) - umap_components = umap_model.fit_transform( - profiles.select(morph_features).to_numpy() - ) - - # concat metadata information with UMAP components - umap_colnames = [f"UMAP{i + 1}" for i in range(umap_components.shape[1])] - return pl.concat( - [ - profiles.select(meta_features), # metadata df - pl.DataFrame(umap_components, schema=umap_colnames), # UMAP components df - ], - how="horizontal", - ) diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 8c990c9..0000000 --- a/poetry.lock +++ /dev/null @@ -1,4616 +0,0 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. - -[metadata] -lock-version = "2.1" -python-versions = ">=3.10,<3.14" -content-hash = "17392a252deecad6af514bd1ab035132b1f8dc2cc0ec662e68c9c605596f3d6d" - 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Alpha", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: BSD License", + "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", + "Topic :: Scientific/Engineering :: Bio-Informatics", + "Topic :: Scientific/Engineering :: Image Processing" +] +dependencies = [ + "beartype>=0.21.0,<0.22.0", + "numpy>=2.2.5,<3.0.0", + "polars>=1.29.0,<2.0.0", + "pot>=0.9.5,<0.10.0", + "scikit-learn>=1.6.1,<2.0.0", + "scipy>=1.15.3,<2.0.0", + "statsmodels>=0.14.4,<0.15.0", + "umap-learn>=0.5.9", + "pycytominer>=1.2.3,<2.0.0" +] + +[project.urls] +Homepage = "https://github.com/WayScience/buscar" +Repository = "https://github.com/WayScience/buscar" +Issues = "https://github.com/WayScience/buscar/issues" + +[tool.coverage.report] +show_missing = true +skip_covered = true + +[tool.coverage.run] +branch = true +source = ["buscar"] + +[tool.hatch.build.targets.wheel] +packages = ["src/buscar"] + +[tool.hatch.version] +source = "vcs" + +[tool.pytest.ini_options] +addopts = "-ra" +testpaths = ["tests"] + +[tool.ruff] +target-version = "py310" +line-length = 88 + +[tool.ruff.lint] +select = ["E", "F", "I"] -[tool.poetry.dependencies] -python = ">=3.10,<3.14" -numpy = ">=2.2.5,<3.0.0" -scipy = ">=1.15.3,<2.0.0" -matplotlib = ">=3.10.3,<4.0.0" -polars = ">=1.29.0,<2.0.0" -statsmodels = ">=0.14.4,<0.15.0" -scikit-learn = ">=1.6.1,<2.0.0" -pycytominer = ">=1.2.3,<2.0.0" -jupyter = "^1.1.1" -ipykernel = "^6.29.5" -tqdm = "^4.67.1" -pot = "^0.9.5" -beartype = "^0.21.0" -umap-learn = "^0.5.9.post2" -narwhals = "^2.4.0" -optuna = "^4.5.0" -scanpy = "^1.11.4" -igraph = "^0.11.9" -leidenalg = "^0.10.2" -louvain = "^0.8.2" -requests = "^2.32.5" - -[tool.poetry.group.dev.dependencies] -pytest = "^8.4.1" -pre-commit = "^4.2.0" - -# ruff configuration [tool.ruff.lint.pydocstyle] convention = "numpy" diff --git a/src/buscar/__init__.py b/src/buscar/__init__.py new file mode 100644 index 0000000..168cf2d --- /dev/null +++ b/src/buscar/__init__.py @@ -0,0 +1,19 @@ +"""Buscar: Bioactive Unbiased Single-cell Compound Assessment and Ranking. + +A Python framework for prioritizing compounds in high-content imaging drug screening +using single-cell profiles. +""" + +from importlib.metadata import PackageNotFoundError, version + +# from buscar._data_utils import add_cell_id_hash as add_cell_id_hash +from buscar.metrics import calculate_buscar_scores as calculate_buscar_scores +from buscar.metrics import ( + compute_earth_movers_distance as compute_earth_movers_distance, +) +from buscar.signatures import identify_signatures as identify_signatures + +try: + __version__ = version("buscar") +except PackageNotFoundError: + __version__ = "0.0.0" diff --git a/buscar/checks.py b/src/buscar/checks.py similarity index 100% rename from buscar/checks.py rename to src/buscar/checks.py diff --git a/buscar/metrics.py b/src/buscar/metrics.py similarity index 97% rename from buscar/metrics.py rename to src/buscar/metrics.py index 9354e7a..eacccf9 100644 --- a/buscar/metrics.py +++ b/src/buscar/metrics.py @@ -15,7 +15,7 @@ import polars as pl from beartype import beartype -from .signatures import get_signatures +from buscar.signatures import identify_signatures @beartype @@ -169,7 +169,8 @@ def affected_off_features_ratio( off_morphology_signature : list[str] List of feature names that constitute the off-morphological signature. method : str, optional - Statistical test method to use for determining significance, by default "ks_test" + Statistical test method to use for determining significance, + by default "ks_test" Returns ------- @@ -178,8 +179,8 @@ def affected_off_features_ratio( of off features). """ - # generate signatures for the off features and count how many are affected - affected_off_sig, _, _ = get_signatures( + # Generate signatures for the off features and count how many are affected. + affected_off_sig, _, _ = identify_signatures( ref_profiles, treated_profiles, morph_feats=off_morphology_signature, @@ -324,10 +325,10 @@ def calculate_buscar_scores( Column containing cell state or perturbation identifier. If None, defaults to perturbation_col, indicating the state of interest is in perturbation_col. on_method : Literal["emd"], optional - Method for computing on-scores. Currently only Earth Mover's Distance (EMD) - is supported, by default "emd" + Method for computing on_buscar_scores. Currently only Earth Mover's Distance + (EMD) is supported, by default "emd" off_method : Literal["affected_ratio", "emd"], optional - Method for computing off-scores: + Method for computing off_buscar_scores: - "affected_ratio": proportion of off features that became significant - "emd": Earth Mover's Distance in off-feature space by default "affected_ratio" @@ -367,7 +368,8 @@ def calculate_buscar_scores( raise ValueError("The profiles DataFrame is empty.") if perturbation_col not in profiles.columns: raise ValueError( - f"The perturbation column '{perturbation_col}' is not in the profiles DataFrame" + f"The perturbation column '{perturbation_col}' is not in the " + / "profiles DataFrame" ) if profiles[perturbation_col].is_null().any(): raise ValueError( diff --git a/buscar/signatures.py b/src/buscar/signatures.py similarity index 88% rename from buscar/signatures.py rename to src/buscar/signatures.py index f258b76..0ab1acb 100644 --- a/buscar/signatures.py +++ b/src/buscar/signatures.py @@ -19,6 +19,7 @@ target profiles and not associated with the cell state. """ +import warnings from typing import Literal import numpy as np @@ -30,13 +31,13 @@ @beartype -def apply_mann_whitney_u_test( +def apply_rank_test( ref_profiles: pl.DataFrame, target_profiles: pl.DataFrame, morph_feats: list[str], ) -> pl.DataFrame: - """Perform Mann-Whitney U test for each feature in the provided profiles and return a - DataFrame with p-values. + """Perform Mann-Whitney U rank test for each feature in the provided profiles + and return a DataFrame with p-values. The Mann-Whitney U test is a non-parametric statistical test that compares two independent samples to determine if they come from the same distribution. The test @@ -180,11 +181,11 @@ def apply_perm_test( conditions. A permutation test is a non-parametric statistical method that determines - if observed differences in cellular morphology between two conditions are + if observed differences in cell morphology between two conditions are statistically significant by comparing them to what would be expected by random chance alone. - In the context of image-based profiling: + Steps: 1. Calculates the actual difference in morphological features (mean or median) between reference and experimental cell populations 2. Creates thousands of "fake" comparisons by randomly shuffling cells between @@ -247,6 +248,10 @@ def _compute_median_difference( # If the test fails due to insufficient data or other issues, assign NaN # to the p-value and continue with the next feature. try: + # Seed a Generator once so each feature's permutations are independent + # but the overall result is reproducible from the same seed. + rng = np.random.default_rng(seed) + result = permutation_test( data=( ref_profiles[morph_feat].to_numpy(), @@ -255,7 +260,7 @@ def _compute_median_difference( statistic=statistic_func, alternative="two-sided", n_resamples=n_resamples, - random_state=seed, + random_state=rng, ) except Exception: # handle the exception @@ -284,10 +289,8 @@ def apply_ks_test( ) -> pl.DataFrame: """Perform KS-test for each feature in the morphology profiles and return p-values. - This function performs a Kolmogorov-Smirnov test for each feature in the morphology profiles - and returns a DataFrame containing feature names and raw p-values. P-value correction and - significance thresholding are not handled in this function and should be applied externally, - for example in the `get_signatures` function. + This function performs a Kolmogorov-Smirnov test for each feature in the morphology + profiles and returns a DataFrame containing feature names and raw p-values. Parameters ---------- @@ -346,14 +349,14 @@ def apply_ks_test( @beartype # handles type checking -def get_signatures( +def identify_signatures( ref_profiles: pl.DataFrame, target_profiles: pl.DataFrame, morph_feats: list[str], test_method: Literal[ - "ks_test", "permutation_test", "welchs_ttest", "mann_whitney_u" + "ks_test", "permutation_test", "welchs_ttest", "rank_test" ] = "ks_test", - fdr_method: Literal["fdr_bh"] = "fdr_bh", + fdr_method: Literal["fdr_bh", "bonferroni"] = "fdr_bh", p_threshold: float | None = 0.05, p_value_padding: float = 0.0, permutation_resamples: int | None = 1000, @@ -363,14 +366,11 @@ def get_signatures( """Identifies significant, non-significant, and ambiguous features between two profiles. - This function compares cellular morphology profiles using one of the statistical - methods, applies multiple testing correction, and categorizes features based on - their statistical significance. Features are classified into three groups: those - clearly associated with the cell state (significant), those are not - associated (non-significant), and those with uncertain significance (ambiguous). - Ambiguous features have corrected p-values within a buffer zone around the - significance threshold, defined by p_threshold ± p_value_padding, indicating - uncertain statistical evidence for their association with the cell state. + This function compares cell morphology profiles using a statistical method, + applies multiple testing correction, and categorizes features into three + groups: significant (clearly different), non-significant (no clear + difference), and ambiguous (p-values within a buffer zone defined by + p_threshold ± p_value_padding). P-value correction for multiple testing is always applied to the results, regardless of the test method chosen, using the method specified by the @@ -388,7 +388,7 @@ def get_signatures( morph_feats : list[str] List of morphology feature names to compare. test_method : Literal["ks_test", "permutation_test", "welchs_ttest", - "mann_whitney_u"], optional + "rank_test"], optional Statistical method to use for comparison. Default is "ks_test". fdr_method : str | None, optional Method for p-value correction. Default is "fdr_bh". @@ -442,8 +442,8 @@ def get_signatures( target_profiles=target_profiles, morph_feats=morph_feats, ) - elif test_method == "mann_whitney_u": - pvals_df = apply_mann_whitney_u_test( + elif test_method == "rank_test": + pvals_df = apply_rank_test( ref_profiles=ref_profiles, target_profiles=target_profiles, morph_feats=morph_feats, @@ -468,15 +468,32 @@ def get_signatures( (pl.col("significance_category") == "significant").alias("is_significant") ) + significant_features = pvals_df.filter( + pl.col("significance_category") == "significant" + )["features"].to_list() + non_significant_features = pvals_df.filter( + pl.col("significance_category") == "non_significant" + )["features"].to_list() + ambiguous_features = pvals_df.filter( + pl.col("significance_category") == "ambiguous" + )["features"].to_list() + + empty_signature_categories = [ + signature_category + for signature_category, features in [ + ("significant", significant_features), + ("non-significant", non_significant_features), + ("ambiguous", ambiguous_features), + ] + if len(features) == 0 + ] + if empty_signature_categories: + warnings.warn( + "No features were assigned to the following signature categories: " + f"{', '.join(empty_signature_categories)}.", + UserWarning, + stacklevel=2, + ) + # returns significant, non-significant, and variant features as lists - return ( - pvals_df.filter(pl.col("significance_category") == "significant")[ - "features" - ].to_list(), - pvals_df.filter(pl.col("significance_category") == "non_significant")[ - "features" - ].to_list(), - pvals_df.filter(pl.col("significance_category") == "ambiguous")[ - "features" - ].to_list(), - ) + return significant_features, non_significant_features, ambiguous_features diff --git a/tests/conftest.py b/tests/conftest.py index d27a5cf..42eb073 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,11 +1,93 @@ -""" -Pytest configuration file for test suite. -Adds the project root to sys.path so imports work correctly. -""" +"""Pytest configuration for local src-layout imports.""" import sys from pathlib import Path -# Add project root to Python path +import numpy as np +import polars as pl +import pytest + +# Add src directory to Python path for local test execution. project_root = Path(__file__).parent.parent -sys.path.insert(0, str(project_root)) +src_dir = project_root / "src" +sys.path.insert(0, str(src_dir)) + + +@pytest.fixture +def synthetic_profiles(): + """ + Generates simulated morphological profiling data for testing. + + Characteristics: + - 1000 rows for control. + - 300 features. + - ~30 non-significant features between control and disease, rest are significant. + """ + seed = 0 + np.random.seed(seed) + n_features = 300 + n_ctrl = 1000 + n_others = 300 + + feature_names = [f"Feature_{i}" for i in range(n_features)] + sig_indices = list(range(30, n_features)) + + # helper internal function + def create_profiles(n, mean=0, shift_indices=None, shift_val=2.0): + data = np.random.normal(mean, 1, (n, n_features)) + if shift_indices: + data[:, shift_indices] += shift_val + return data + + # Create states + ctrl = create_profiles(n_ctrl) + disease = create_profiles(n_others, shift_indices=sig_indices) + t_ctrl_like = create_profiles(n_others, shift_val=0.1, shift_indices=sig_indices) + t_disease_like = create_profiles(n_others, shift_val=1.9, shift_indices=sig_indices) + + # t_different should be unlike control and disease in BOTH on and off features. + # by shifting ALL features with a large value, it'll trigger high off_buscar_score + # and a high on_buscar_score + all_indices = list(range(n_features)) + t_different = create_profiles(n_others, shift_val=-5.0, shift_indices=all_indices) + + all_data = np.vstack([ctrl, disease, t_ctrl_like, t_disease_like, t_different]) + df = pl.DataFrame(all_data, schema=feature_names) + + treatments = ( + ["control"] * n_ctrl + + ["disease"] * n_others + + ["treatment_control_like"] * n_others + + ["treatment_disease_like"] * n_others + + ["treatment_different"] * n_others + ) + + df = df.with_columns(pl.Series("Metadata_treatment", treatments)) + + return df, feature_names + + +@pytest.fixture +def not_significant_data(): + """ + Generates simulated morphological profiling data where no features are significant. + """ + seed = 42 + np.random.seed(seed) + n_features = 50 + n_ctrl = 100 + n_disease = 100 + + feature_names = [f"Feature_{i}" for i in range(n_features)] + + # Both sets from same distribution (no shift) + ctrl_data = np.random.normal(0, 1, (n_ctrl, n_features)) + disease_data = np.random.normal(0, 1, (n_disease, n_features)) + + all_data = np.vstack([ctrl_data, disease_data]) + df = pl.DataFrame(all_data, schema=feature_names) + + treatments = ["control"] * n_ctrl + ["disease"] * n_disease + df = df.with_columns(pl.Series("Metadata_treatment", treatments)) + + return df, feature_names diff --git a/tests/test_checks.py b/tests/test_checks.py new file mode 100644 index 0000000..45848ec --- /dev/null +++ b/tests/test_checks.py @@ -0,0 +1,27 @@ +import numpy as np +import polars as pl +import pytest + +from buscar.checks import check_for_nans + + +def test_check_for_nans_clean(): + df = pl.DataFrame({"A": [1.0, 2.0, 3.0], "B": [4.0, 5.0, 6.0]}) + check_for_nans(df, ["A", "B"]) + + +def test_check_for_nans_with_null(): + df = pl.DataFrame({"A": [1.0, None, 3.0], "B": [4.0, 5.0, 6.0]}) + with pytest.raises(ValueError, match="Profiles contain NaN or Inf values."): + check_for_nans(df, ["A", "B"]) + + +def test_check_for_nans_with_inf(): + df = pl.DataFrame({"A": [1.0, np.inf, 3.0], "B": [4.0, 5.0, 6.0]}) + with pytest.raises(ValueError, match="Profiles contain NaN or Inf values."): + check_for_nans(df, ["A", "B"]) + + +def test_check_for_nans_ignore_other_columns(): + df = pl.DataFrame({"A": [1.0, 2.0, 3.0], "B": [4.0, None, 6.0]}) + check_for_nans(df, ["A"]) diff --git a/tests/test_hit_identifcation.py b/tests/test_hit_identifcation.py deleted file mode 100644 index b43d44e..0000000 --- a/tests/test_hit_identifcation.py +++ /dev/null @@ -1,239 +0,0 @@ -import sys -from pathlib import Path - -import polars as pl -import pytest - -# Add the parent directory to the path to import utils -sys.path.insert(0, str(Path(__file__).parent.parent)) - -from buscar._identify_hits import calculate_weighted_sum, identify_compound_hit - - -@pytest.fixture -def test_data_path(): - """Return the path to the test parquet file.""" - return Path(__file__).parent / "data" / "test_compound_cluster_scores.parquet" - - -@pytest.fixture -def distance_df(test_data_path): - """Load the test distance DataFrame from parquet.""" - return pl.read_parquet(test_data_path) - - -def test_distance_df_structure(distance_df): - """Test that the distance DataFrame has the expected structure.""" - expected_columns = [ - "control_cluster_id", - "treatment_cluster_id", - "treatment", - "on_score", - "off_score", - "ratio", - ] - - assert distance_df.columns == expected_columns - assert distance_df.shape == (40, 6) - - -def test_distance_df_has_three_treatments(distance_df): - """Test that there are exactly 3 treatments in the dataset.""" - treatments = distance_df.select("treatment").unique() - assert treatments.height == 3 - assert set(treatments["treatment"].to_list()) == {"drug_x", "drug_y", "drug_z"} - - -def test_ratios_sum_to_one_per_treatment(distance_df): - """Test that ratios sum to approximately 1.0 for each treatment.""" - ratio_sums = distance_df.group_by("treatment").agg( - pl.col("ratio").sum().alias("ratio_sum") - ) - - for ratio_sum in ratio_sums["ratio_sum"]: - assert abs(ratio_sum - 1.0) < 0.01, f"Ratio sum {ratio_sum} is not close to 1.0" - - -def test_calculate_weighted_sum_returns_correct_columns(distance_df): - """Test that calculate_weighted_sum returns the expected columns.""" - # First create paired scores (simulating the pairing step) - paired_df = ( - distance_df.sort(["treatment", "treatment_cluster_id", "on_score", "off_score"]) - .group_by(["treatment", "treatment_cluster_id"]) - .first() - ) - - result = calculate_weighted_sum(paired_df) - - assert "treatment" in result.columns - assert "compound_score" in result.columns - - -def test_calculate_weighted_sum_correct_number_of_rows(distance_df): - """Test that one row per treatment is returned.""" - paired_df = ( - distance_df.sort(["treatment", "treatment_cluster_id", "on_score", "off_score"]) - .group_by(["treatment", "treatment_cluster_id"]) - .first() - ) - - result = calculate_weighted_sum(paired_df) - - # 3 unique treatments in test data - assert result.height == 3 - - -def test_calculate_weighted_sum_sorted_ascending(distance_df): - """Test that results are sorted by compound_score in ascending order.""" - paired_df = ( - distance_df.sort(["treatment", "treatment_cluster_id", "on_score", "off_score"]) - .group_by(["treatment", "treatment_cluster_id"]) - .first() - ) - - result = calculate_weighted_sum(paired_df) - - scores = result["compound_score"].to_list() - assert scores == sorted(scores), "Compound scores are not sorted in ascending order" - - -def test_identify_compound_hit_returns_correct_columns(distance_df): - """Test that identify_compound_hit returns expected columns.""" - result = identify_compound_hit(distance_df, method="weighted_sum") - - assert "treatment" in result.columns - assert "compound_score" in result.columns - assert "rank" in result.columns - - -def test_identify_compound_hit_ranks_correctly(distance_df): - """Test that rank column is assigned correctly (1 = best/lowest score).""" - result = identify_compound_hit(distance_df, method="weighted_sum") - - # Check that rank 1 has the lowest compound_score - rank_1_row = result.filter(pl.col("rank") == 1) - min_score = result["compound_score"].min() - - assert rank_1_row["compound_score"][0] == min_score - - -def test_identify_compound_hit_one_row_per_treatment(distance_df): - """Test that one row per treatment is returned.""" - result = identify_compound_hit(distance_df, method="weighted_sum") - - # 3 unique treatments in test data - assert result.height == 3 - - -def test_identify_compound_hit_all_ranks_unique(distance_df): - """Test that all ranks are unique (no ties).""" - result = identify_compound_hit(distance_df, method="weighted_sum") - - ranks = result["rank"].to_list() - assert len(ranks) == len(set(ranks)), "Ranks are not unique" - assert set(ranks) == {1, 2, 3}, "Ranks should be 1, 2, 3" - - -def test_identify_compound_hit_drug_z_is_best(distance_df): - """Test that drug_z has the best (lowest) compound score based on test data.""" - result = identify_compound_hit(distance_df, method="weighted_sum") - - # drug_z should have rank 1 (best performance - lowest scores) - best_drug = result.filter(pl.col("rank") == 1)["treatment"][0] - - # Based on the test data, drug_z has the lowest on/off scores - assert best_drug == "drug_z", f"Expected drug_z to be best, but got {best_drug}" - - -def test_pairing_logic_each_treatment_cluster_paired_once(distance_df): - """Test that each treatment cluster is paired with exactly one control cluster.""" - # Replicate the pairing step from identify_compound_hit - paired = ( - distance_df.lazy() - .sort(["treatment", "treatment_cluster_id", "on_score", "off_score"]) - .group_by(["treatment", "treatment_cluster_id"]) - .agg([pl.all().first()]) - .collect() - ) - - # Each (treatment, treatment_cluster_id) should appear exactly once - unique_pairs = paired.select(["treatment", "treatment_cluster_id"]).unique() - assert paired.height == unique_pairs.height - - -def test_control_clusters_can_be_reused(distance_df): - """Test that control clusters can be paired with multiple treatment clusters.""" - # Replicate the pairing step - paired = ( - distance_df.sort(["treatment", "treatment_cluster_id", "on_score", "off_score"]) - .group_by(["treatment", "treatment_cluster_id"]) - .first() - ) - - # Check if any control cluster appears more than once - control_cluster_counts = paired.group_by("control_cluster_id").agg( - pl.len().alias("count") - ) - - # At least one control cluster should be reused - max_count = control_cluster_counts["count"].max() - assert max_count > 1, "Control clusters should be reusable" - - -def test_compound_score_is_weighted_sum(distance_df): - """Test that compound_score is calculated as weighted sum of on and off scores.""" - paired = ( - distance_df.sort(["treatment", "treatment_cluster_id", "on_score", "off_score"]) - .group_by(["treatment", "treatment_cluster_id"]) - .first() - ) - - result = calculate_weighted_sum(paired) - - # Manually calculate compound score for one treatment - drug_x_data = paired.filter(pl.col("treatment") == "drug_x") - expected_score = (drug_x_data["on_score"] * drug_x_data["ratio"]).sum() + ( - drug_x_data["off_score"] * drug_x_data["ratio"] - ).sum() - - actual_score = result.filter(pl.col("treatment") == "drug_x")["compound_score"][0] - - assert abs(actual_score - expected_score) < 0.01 - - -def test_empty_dataframe_handling(): - """Test that functions handle empty DataFrames gracefully.""" - empty_df = pl.DataFrame( - { - "control_cluster_id": [], - "treatment_cluster_id": [], - "treatment": [], - "on_score": [], - "off_score": [], - "ratio": [], - } - ).cast( - { - "control_cluster_id": pl.Int64, - "treatment_cluster_id": pl.Int64, - "treatment": pl.String, - "on_score": pl.Float64, - "off_score": pl.Float64, - "ratio": pl.Float64, - } - ) - - result = identify_compound_hit(empty_df, method="weighted_sum") - assert result.height == 0 - - -def test_all_scores_are_positive(distance_df): - """Test that all on_score and off_score values are non-negative.""" - assert (distance_df["on_score"] >= 0).all() - assert (distance_df["off_score"] >= 0).all() - - -def test_all_ratios_are_valid_probabilities(distance_df): - """Test that all ratio values are between 0 and 1.""" - assert (distance_df["ratio"] >= 0).all() - assert (distance_df["ratio"] <= 1).all() diff --git a/tests/test_metrics.py b/tests/test_metrics.py new file mode 100644 index 0000000..9a62dc9 --- /dev/null +++ b/tests/test_metrics.py @@ -0,0 +1,95 @@ +import polars as pl +import pytest + +from buscar.metrics import calculate_buscar_scores, compute_earth_movers_distance + + +def test_calculate_buscar_scores(synthetic_profiles): + df, features = synthetic_profiles + + # Identify signatures first (simple way, subsets for speed) + on_sig = features[30:80] # Part of significantly different ones + off_sig = features[:30] # Non-significant ones + + # Run the main scoring function + with pytest.warns( + UserWarning, + match="No features were assigned to the following signature categories", + ): + scored_df = calculate_buscar_scores( + profiles=df, + meta_cols=["Metadata_treatment"], + on_morphology_signature=on_sig, + off_morphology_signature=off_sig, + ref_state="disease", + target="control", + perturbation_col="Metadata_treatment", + on_method="emd", + off_method="affected_ratio", + raw_emd_scores=False, + ) + + # Column assertions + expected_cols = [ + "target", + "perturbation", + "on_buscar_scores", + "off_buscar_scores", + "is_reference_distance", + ] + assert all(col in scored_df.columns for col in expected_cols) + + # Ranking/Logic assertions base on data characteristics + # Disease-like should have higher on_buscar_score (far from control) + # Control-like should have lower on_buscar_score (near control) + # However, normalization makes target state (disease) score 1.0. + + # res = scored_df.to_pandas().set_index("treatment") + + # on_buscar_score for disease should be 1.0 (normalization target) + disease_score = scored_df.filter(pl.col("perturbation") == "disease")[ + "on_buscar_scores" + ][0] + assert disease_score == pytest.approx(1.0) + + # treatment_control_like should have lower on_buscar_score than disease (closer to + # reference) + ctrl_like_score = scored_df.filter( + pl.col("perturbation") == "treatment_control_like" + )["on_buscar_scores"][0] + assert ctrl_like_score < 1.0 + + # treatment_disease_like should have similar score to disease (~1.0) + disease_like_score = scored_df.filter( + pl.col("perturbation") == "treatment_disease_like" + )["on_buscar_scores"][0] + assert 0.8 < disease_like_score < 1.2 + + # t_different should have the highest on_buscar_score and off_buscar_score + t_diff_row = scored_df.filter(pl.col("perturbation") == "treatment_different") + t_diff_on = t_diff_row["on_buscar_scores"][0] + t_diff_off = t_diff_row["off_buscar_scores"][0] + + # compare t_diff on/off scores to all other treatment scores + all_other_on_scores = scored_df.filter( + pl.col("perturbation") != "treatment_different" + )["on_buscar_scores"] + all_other_off_scores = scored_df.filter( + pl.col("perturbation") != "treatment_different" + )["off_buscar_scores"] + + # Assert t_different is greater than all other treatments in on_buscar_score and + # off_buscar_score + assert all(t_diff_on > score for score in all_other_on_scores) + assert all(t_diff_off > score for score in all_other_off_scores) + + +def test_emd_direct(synthetic_profiles): + df, features = synthetic_profiles + ctrl_df = df.filter(pl.col("Metadata_treatment") == "control").select(features[:10]) + disease_df = df.filter(pl.col("Metadata_treatment") == "disease").select( + features[:10] + ) + + emd = compute_earth_movers_distance(ctrl_df, disease_df, subsample_size=50) + assert emd > 0.0 diff --git a/tests/test_preprocessing.py b/tests/test_preprocessing.py deleted file mode 100644 index d4d3cc6..0000000 --- a/tests/test_preprocessing.py +++ /dev/null @@ -1,294 +0,0 @@ -""" -Tests for utils.preprocess module -""" - -import contextlib - -import numpy as np -import polars as pl -import pytest -from buscar.preprocess import apply_pca - - -@contextlib.contextmanager -def temporary_seed(seed: int): - """Context manager to temporarily set numpy random seed""" - state = np.random.get_state() - np.random.seed(seed) - try: - yield - finally: - np.random.set_state(state) - - -# Fixtures at module level (shared across all test classes) -@pytest.fixture -def dummy_profiles(): - """Create dummy profiles DataFrame for testing""" - with temporary_seed(0): - n_samples = 100 - n_features = 50 - - # Create dummy data - morph_data = np.random.randn(n_samples, n_features) - feature_cols = [f"Feature_{i}" for i in range(n_features)] - - # Create metadata - metadata = { - "Metadata_Well": [f"A{i:02d}" for i in range(n_samples)], - "Metadata_Plate": ["Plate1"] * 50 + ["Plate2"] * 50, - "Metadata_Treatment": np.random.choice( - ["DMSO", "Drug_A", "Drug_B"], n_samples - ), - } - - # Combine into DataFrame - df_dict = {**metadata} - for i, col in enumerate(feature_cols): - df_dict[col] = morph_data[:, i] - - return pl.DataFrame(df_dict) - - -@pytest.fixture -def meta_features(): - """Metadata feature names""" - return ["Metadata_Well", "Metadata_Plate", "Metadata_Treatment"] - - -@pytest.fixture -def morph_features(): - """Morphological feature names""" - return [f"Feature_{i}" for i in range(50)] - - -class TestApplyPCA: - """Test suite for apply_pca function""" - - def test_basic_pca_application(self, dummy_profiles, meta_features, morph_features): - """Test basic PCA application returns correct structure""" - result = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.95, - ) - - # Check result is a DataFrame - assert isinstance(result, pl.DataFrame) - - # Check number of rows matches input - assert len(result) == len(dummy_profiles) - - # Check metadata columns are present - for col in meta_features: - assert col in result.columns - - # Check PC columns are present - pc_cols = [col for col in result.columns if col.startswith("PC")] - assert len(pc_cols) > 0 - - def test_pca_variance_explained( - self, dummy_profiles, meta_features, morph_features - ): - """Test that PCA captures requested variance""" - result = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.90, - ) - - # Check that we get fewer components with lower variance threshold - pc_cols = [col for col in result.columns if col.startswith("PC")] - assert len(pc_cols) > 0 - assert len(pc_cols) < len(morph_features) # Should reduce dimensions - - def test_pca_with_standardization( - self, dummy_profiles, meta_features, morph_features - ): - """Test PCA with standardization enabled""" - result_no_std = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.95, - standardize=False, - ) - - result_with_std = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.95, - standardize=True, - ) - - # Both should return valid DataFrames - assert isinstance(result_no_std, pl.DataFrame) - assert isinstance(result_with_std, pl.DataFrame) - - # Results should differ (standardization changes the transformation) - pc1_no_std = result_no_std["PC1"].to_numpy() - pc1_with_std = result_with_std["PC1"].to_numpy() - assert not np.allclose(pc1_no_std, pc1_with_std) - - def test_pca_column_names(self, dummy_profiles, meta_features, morph_features): - """Test that PC columns are named correctly""" - result = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.80, - ) - - pc_cols = [col for col in result.columns if col.startswith("PC")] - - # Check PC naming is sequential - for i, col in enumerate(pc_cols, start=1): - assert col == f"PC{i}" - - def test_pca_preserves_metadata( - self, dummy_profiles, meta_features, morph_features - ): - """Test that metadata values are preserved""" - result = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.95, - ) - - # Check metadata values match original - for col in meta_features: - assert result[col].to_list() == dummy_profiles[col].to_list() - - def test_pca_with_nan_raises_error( - self, dummy_profiles, meta_features, morph_features - ): - """Test that NaN values raise ValueError""" - # Add NaN to one feature - dummy_with_nan = dummy_profiles.with_columns( - pl.when(pl.col("Feature_0").abs() > 2) - .then(None) - .otherwise(pl.col("Feature_0")) - .alias("Feature_0") - ) - - with pytest.raises(ValueError, match="Input data contains NaNs"): - apply_pca( - profiles=dummy_with_nan, - meta_features=meta_features, - morph_features=morph_features, - ) - - def test_pca_random_state(self, dummy_profiles, meta_features, morph_features): - """Test that random state produces reproducible results""" - result1 = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.95, - random_state=42, - ) - - result2 = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.95, - random_state=42, - ) - - # Results should be identical with same random state - pc1_result1 = result1["PC1"].to_numpy() - pc1_result2 = result2["PC1"].to_numpy() - assert np.allclose(pc1_result1, pc1_result2) - - def test_pca_high_variance_threshold( - self, dummy_profiles, meta_features, morph_features - ): - """Test with high variance threshold (captures most variance)""" - result = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.99, - ) - - pc_cols = [col for col in result.columns if col.startswith("PC")] - # Should need more components for 99% variance - assert len(pc_cols) > 0 - - def test_pca_low_variance_threshold( - self, dummy_profiles, meta_features, morph_features - ): - """Test with low variance threshold (fewer components)""" - result = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.50, - ) - - pc_cols = [col for col in result.columns if col.startswith("PC")] - # Should need fewer components for 50% variance - assert len(pc_cols) > 0 - assert len(pc_cols) < len(morph_features) - - def test_pca_output_shape(self, dummy_profiles, meta_features, morph_features): - """Test that output shape is correct""" - result = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.95, - ) - - # Number of rows should match - assert len(result) == len(dummy_profiles) - - # Number of columns = metadata + PCs - pc_cols = [col for col in result.columns if col.startswith("PC")] - expected_cols = len(meta_features) + len(pc_cols) - assert len(result.columns) == expected_cols - - def test_pca_with_small_dataset(self, meta_features, morph_features): - """Test PCA with very small dataset""" - # Create minimal dataset - with temporary_seed(42): - small_data = { - "Metadata_Well": ["A01", "A02", "A03"], - "Metadata_Plate": ["P1", "P1", "P1"], - "Metadata_Treatment": ["DMSO", "Drug", "DMSO"], - } - for feat in morph_features[:10]: # Use only 10 features - small_data[feat] = np.random.randn(3) - - small_df = pl.DataFrame(small_data) - - result = apply_pca( - profiles=small_df, - meta_features=meta_features, - morph_features=morph_features[:10], - var_explained=0.95, - ) - - assert isinstance(result, pl.DataFrame) - assert len(result) == 3 - - def test_pca_kwargs_passed_to_sklearn( - self, dummy_profiles, meta_features, morph_features - ): - """Test that additional kwargs are passed to sklearn PCA""" - # Using whiten parameter as example - result = apply_pca( - profiles=dummy_profiles, - meta_features=meta_features, - morph_features=morph_features, - var_explained=0.95, - whiten=True, # Additional sklearn PCA parameter - ) - - assert isinstance(result, pl.DataFrame) - assert len(result) == len(dummy_profiles) diff --git a/tests/test_signatures.py b/tests/test_signatures.py new file mode 100644 index 0000000..b06c610 --- /dev/null +++ b/tests/test_signatures.py @@ -0,0 +1,78 @@ +import polars as pl +import pytest + +from buscar.signatures import identify_signatures + + +def test_identify_signatures(synthetic_profiles): + df, features = synthetic_profiles + + # Filter for control and disease + ctrl_df = df.filter(pl.col("Metadata_treatment") == "control") + disease_df = df.filter(pl.col("Metadata_treatment") == "disease") + + # Test KS-test + with pytest.warns( + UserWarning, + match="No features were assigned to the following signature categories", + ): + sig, non_sig, ambig = identify_signatures( + ref_profiles=ctrl_df, + target_profiles=disease_df, + morph_feats=features, + test_method="ks_test", + p_threshold=0.05, + ) + + # Assertions based on data characteristics (30 non-sig, 270 sig) + assert len(sig) > 200 # Should catch most sig ones + assert len(non_sig) > 0 # Should catch some non-sig ones + assert isinstance(sig, list) + assert isinstance(non_sig, list) + assert isinstance(ambig, list) + + +def test_identify_signatures_methods(synthetic_profiles): + df, features = synthetic_profiles + ctrl_df = df.filter(pl.col("Metadata_treatment") == "control") + disease_df = df.filter(pl.col("Metadata_treatment") == "disease") + + # Quick check for other methods (only checking it runs) + for method in ["welchs_ttest", "rank_test"]: + with pytest.warns( + UserWarning, + match="No features were assigned to the following signature categories", + ): + sig, _, _ = identify_signatures( + ref_profiles=ctrl_df, + target_profiles=disease_df, + morph_feats=features[:50], # Subset for speed + test_method=method, + ) + assert len(sig) >= 0 + + +def test_identify_signatures_no_significance(not_significant_data): + """ + Tests that identify_signatures returns an empty significant-feature list when no + features are significant. + """ + df, features = not_significant_data + + ctrl_df = df.filter(pl.col("Metadata_treatment") == "control") + disease_df = df.filter(pl.col("Metadata_treatment") == "disease") + + with pytest.warns( + UserWarning, + match="No features were assigned to the following signature categories", + ): + sig, non_sig, ambig = identify_signatures( + ref_profiles=ctrl_df, + target_profiles=disease_df, + morph_feats=features, + test_method="ks_test", + p_threshold=0.05, + ) + + assert sig == [] + assert len(non_sig) + len(ambig) == len(features) diff --git a/tests/test_utils.py b/tests/test_utils.py deleted file mode 100644 index 899771e..0000000 --- a/tests/test_utils.py +++ /dev/null @@ -1,272 +0,0 @@ -""" -Tests for utils.data_utils module -""" - -import polars as pl -import pytest - -from utils.data_utils import add_cell_id_hash - - -# Fixtures -@pytest.fixture -def simple_profiles(): - """Create simple test profiles DataFrame""" - return pl.DataFrame( - { - "Metadata_Well": ["A01", "A02", "A03"], - "Metadata_Plate": ["P1", "P1", "P1"], - "Feature_1": [1.5, 2.3, 3.1], - "Feature_2": [4.2, 5.1, 6.7], - } - ) - - -@pytest.fixture -def test_data(): - """Load test data from parquet file""" - return pl.read_parquet("tests/data/test_compound_cluster_scores.parquet") - - -class TestAddCellIdHash: - """Test suite for add_cell_id_hash function""" - - def test_adds_cell_id_column(self, simple_profiles): - """Test that Metadata_cell_id column is added""" - result = add_cell_id_hash(simple_profiles) - - assert "Metadata_cell_id" in result.columns - assert len(result) == len(simple_profiles) - - def test_cell_id_is_first_column(self, simple_profiles): - """Test that Metadata_cell_id is the first column""" - result = add_cell_id_hash(simple_profiles) - - assert result.columns[0] == "Metadata_cell_id" - - def test_cell_id_uniqueness(self, simple_profiles): - """Test that each row gets a unique hash""" - result = add_cell_id_hash(simple_profiles) - - unique_ids = result["Metadata_cell_id"].n_unique() - assert unique_ids == len(result) - - def test_deterministic_with_same_seed(self, simple_profiles): - """Test that same seed produces identical hashes""" - result1 = add_cell_id_hash(simple_profiles, seed=42) - result2 = add_cell_id_hash(simple_profiles, seed=42) - - assert ( - result1["Metadata_cell_id"].to_list() - == result2["Metadata_cell_id"].to_list() - ) - - def test_different_seeds_produce_different_hashes(self, simple_profiles): - """Test that different seeds produce different hashes""" - result1 = add_cell_id_hash(simple_profiles, seed=0) - result2 = add_cell_id_hash(simple_profiles, seed=42) - - assert ( - result1["Metadata_cell_id"].to_list() - != result2["Metadata_cell_id"].to_list() - ) - - def test_default_seed_is_zero(self, simple_profiles): - """Test that default seed is 0""" - result_default = add_cell_id_hash(simple_profiles) - result_seed_zero = add_cell_id_hash(simple_profiles, seed=0) - - assert ( - result_default["Metadata_cell_id"].to_list() - == result_seed_zero["Metadata_cell_id"].to_list() - ) - - def test_preserves_all_columns(self, simple_profiles): - """Test that all original columns are preserved""" - result = add_cell_id_hash(simple_profiles) - - original_cols = set(simple_profiles.columns) - result_cols = set(result.columns) - - # All original columns should be in result - assert original_cols.issubset(result_cols) - - def test_preserves_data_values(self, simple_profiles): - """Test that original data values are unchanged""" - result = add_cell_id_hash(simple_profiles) - - for col in simple_profiles.columns: - assert result[col].to_list() == simple_profiles[col].to_list() - - def test_hash_length_is_consistent(self, simple_profiles): - """Test that all hashes have the same length (MD5 = 32 chars)""" - result = add_cell_id_hash(simple_profiles) - - hash_lengths = result["Metadata_cell_id"].str.len_chars().unique().to_list() - assert len(hash_lengths) == 1 - assert hash_lengths[0] == 32 # MD5 hash length - - def test_existing_column_warning_without_force(self, simple_profiles): - """Test that existing Metadata_cell_id column triggers warning without force""" - # Add cell_id first - result1 = add_cell_id_hash(simple_profiles) - - # Try to add again without force (should print warning and return unchanged) - result2 = add_cell_id_hash(result1, seed=99) - - # Should be identical (not changed) because force=False - assert ( - result1["Metadata_cell_id"].to_list() - == result2["Metadata_cell_id"].to_list() - ) - - def test_force_overwrites_existing_column(self, simple_profiles): - """Test that force=True overwrites existing Metadata_cell_id column""" - # Add cell_id with seed=0 - result1 = add_cell_id_hash(simple_profiles, seed=0) - - # Overwrite with different seed - result2 = add_cell_id_hash(result1, seed=99, force=True) - - # Hashes should be different - assert ( - result1["Metadata_cell_id"].to_list() - != result2["Metadata_cell_id"].to_list() - ) - - def test_with_test_data(self, test_data): - """Test with actual test data from parquet file""" - result = add_cell_id_hash(test_data) - - assert "Metadata_cell_id" in result.columns - assert result.columns[0] == "Metadata_cell_id" - assert len(result) == len(test_data) - - # Each row should have unique hash - unique_ids = result["Metadata_cell_id"].n_unique() - assert unique_ids == len(result) - - def test_with_identical_rows(self): - """Test that identical rows produce different hashes (because of row position)""" - # Create DataFrame with identical rows - identical_profiles = pl.DataFrame( - { - "Feature_1": [1.0, 1.0, 1.0], - "Feature_2": [2.0, 2.0, 2.0], - } - ) - - result = add_cell_id_hash(identical_profiles) - - # Even identical data should produce unique hashes due to concatenation - # Actually, if data is truly identical, hashes will be the same - # Let's check this behavior - unique_ids = result["Metadata_cell_id"].n_unique() - - # With identical rows and same seed, hashes will be identical - assert unique_ids == 1 - - def test_with_empty_dataframe(self): - """Test with empty DataFrame""" - empty_df = pl.DataFrame( - { - "Feature_1": [], - "Feature_2": [], - } - ) - - result = add_cell_id_hash(empty_df) - - assert "Metadata_cell_id" in result.columns - assert len(result) == 0 - - def test_with_single_column(self): - """Test with DataFrame having only one column""" - single_col_df = pl.DataFrame( - { - "Feature_1": [1.0, 2.0, 3.0], - } - ) - - result = add_cell_id_hash(single_col_df) - - assert "Metadata_cell_id" in result.columns - assert len(result) == 3 - - def test_with_various_data_types(self): - """Test with various column data types""" - mixed_df = pl.DataFrame( - { - "int_col": [1, 2, 3], - "float_col": [1.1, 2.2, 3.3], - "str_col": ["a", "b", "c"], - "bool_col": [True, False, True], - } - ) - - result = add_cell_id_hash(mixed_df) - - assert "Metadata_cell_id" in result.columns - assert len(result) == 3 - assert result["Metadata_cell_id"].n_unique() == 3 - - def test_raises_error_for_non_polars_dataframe(self): - """Test that non-Polars DataFrame raises TypeError""" - import pandas as pd - - pandas_df = pd.DataFrame( - { - "Feature_1": [1, 2, 3], - "Feature_2": [4, 5, 6], - } - ) - - with pytest.raises(TypeError, match="profiles must be a Polars DataFrame"): - add_cell_id_hash(pandas_df) - - def test_with_null_values(self): - """Test with DataFrame containing null values""" - df_with_nulls = pl.DataFrame( - { - "Feature_1": [1.0, None, 3.0], - "Feature_2": [4.0, 5.0, None], - } - ) - - result = add_cell_id_hash(df_with_nulls) - - assert "Metadata_cell_id" in result.columns - assert len(result) == 3 - # Note: Rows with nulls may produce null hashes due to string conversion - # This is expected behavior - at least one non-null hash should be present - assert result["Metadata_cell_id"].null_count() <= 2 - - def test_large_dataframe_performance(self): - """Test with larger DataFrame to check performance""" - import numpy as np - - # Create a larger DataFrame - n_rows = 1000 - large_df = pl.DataFrame( - { - "Feature_1": np.random.randn(n_rows), - "Feature_2": 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