From ae959dd508d3158a8963ceca49449246edc15545 Mon Sep 17 00:00:00 2001 From: Sricharan Reddy Varra Date: Fri, 8 Nov 2024 20:38:12 -0800 Subject: [PATCH 1/7] =?UTF-8?q?=E2=9C=A8=20Fixed=20up=20filtering?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 1 + CITATION.cff | 27 ++ README.md | 36 +- docs/api.md | 29 ++ docs/conf.py | 4 +- docs/notebooks/example.ipynb | 106 ++++- pyproject.toml | 7 +- ruff.toml | 1 + src/annsel/__init__.py | 3 +- src/annsel/core/__init__.py | 18 +- src/annsel/core/col.py | 105 +++++ src/annsel/core/expr.py | 39 ++ src/annsel/core/extensions.py | 9 +- src/annsel/core/utils.py | 87 ++++ src/annsel/tl/_filter.py | 50 +++ src/annsel/tl/_utils.py | 199 --------- src/annsel/tl/basic.py | 183 +++++++-- uv.lock | 752 +++++++++++++++++++++++++++++++++- 18 files changed, 1375 insertions(+), 281 deletions(-) create mode 100644 CITATION.cff create mode 100644 src/annsel/core/col.py create mode 100644 src/annsel/core/expr.py create mode 100644 src/annsel/core/utils.py create mode 100644 src/annsel/tl/_filter.py delete mode 100644 src/annsel/tl/_utils.py diff --git a/.gitignore b/.gitignore index 31e10b3..85744c4 100644 --- a/.gitignore +++ b/.gitignore @@ -18,3 +18,4 @@ __pycache__/ # docs /docs/generated/ /docs/_build/ +/.vscode/* diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..a94c008 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,27 @@ +# This CITATION.cff file was generated with cffinit. +# Visit https://bit.ly/cffinit to generate yours today! + +cff-version: 1.2.0 +title: annsel +message: >- + If this software has been beneficial to you, please cite + it using the metadata from this file. +type: software +authors: + - given-names: Sricharan Reddy + family-names: Varra + email: srivarra@stanford.edu + affiliation: Stanford University + orcid: "https://orcid.org/0009-0000-5757-6818" +repository-code: "https://github.com/srivarra/annsel" +url: "https://annsel.readthedocs.io/en/latest/" +abstract: >- + annsel brings familiar DataFrame-style operations to + AnnData objects, letting you filter and select data + intuitively. Built on the narwhals library, annsel helps + you manipulate complex biological datasets stored in + AnnData format through a user-friendly interface. +keywords: + - anndata + - data-manipulation +license: MIT diff --git a/README.md b/README.md index ff32d02..1adf66f 100644 --- a/README.md +++ b/README.md @@ -19,32 +19,35 @@ [badge-license]: https://img.shields.io/badge/License-MIT-yellow.svg [badge-hatch]: https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg -A Narwhals powered DataFrame-style selection, filtering and indexing operations on AnnData Objects. +`annsel` brings familiar DataFrame-style operations to [`AnnData`](https://anndata.readthedocs.io/en/latest/) objects, making filtering and selection intuitive and straightforward. Built on the [narwhals][link-narwhals] library, it provides a seamless interface for manipulating complex biological datasets stored in `AnnData` format. + +> [!WARNING] +> This is a very early work in progress, there is set-in-stone API and breaking changes are likely to occur. ## Getting started -Please refer to the [documentation][], -in particular, the [API documentation][]. +Please refer to the [documentation][link-docs], +in particular, the [API documentation][link-api]. ## Installation You need to have Python 3.10 or newer installed on your system. If you don't have Python installed, we recommend installing [uv][link-uv]. -There are several alternative options to install annsel: +There are several alternative options to install `annsel`: + +1. Install the latest development version: - + ```zsh + pip install git+https://github.com/srivarra/annsel.git@main + ``` -1. Install the latest development version: + With `uv`: -```zsh -pip install git+https://github.com/srivarra/annsel.git@main -``` + ```zsh + uv add git+https://github.com/srivarra/annsel + ``` ## Release notes @@ -52,8 +55,8 @@ See the [changelog][changelog]. ## Contact -For questions and help requests, you can reach out in the [scverse discourse][]. -If you found a bug, please use the [issue tracker][]. +For questions and help requests, you can reach out in the [scverse discourse][scverse-discourse]. +If you found a bug, please use the [issue tracker][issue-tracker]. ## Citation @@ -72,3 +75,4 @@ If you found a bug, please use the [issue tracker][]. [link-uv]: https://github.com/astral-sh/uv [link-license]: https://opensource.org/licenses/MIT [link-hatch]: https://github.com/pypa/hatch +[link-narwhals]: https://github.com/narwhals-dev/narwhals diff --git a/docs/api.md b/docs/api.md index 657d2b3..d4ba244 100644 --- a/docs/api.md +++ b/docs/api.md @@ -1,5 +1,21 @@ # API +## AnnData Selectors + +```{eval-rst} +.. module:: annsel.core.col +.. currentmodule:: annsel +.. autosummary:: + :toctree: generated + + obs_col + obs_names + var_col + var_names + x + +``` + ## Tools ```{eval-rst} @@ -10,4 +26,17 @@ :template: autosummary/accessor_method.rst AnnselAccessor.filter + AnnselAccessor.select + AnnselAccessor.pipe +``` + +## Extensions + +```{eval-rst} +.. module:: annsel.core.extensions +.. currentmodule:: annsel +.. autosummary:: + :toctree: generated/ + + register_anndata_accessor ``` diff --git a/docs/conf.py b/docs/conf.py index 009d413..38cfbec 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -101,7 +101,9 @@ "anndata": ("https://anndata.readthedocs.io/en/stable/", None), "scanpy": ("https://scanpy.readthedocs.io/en/stable/", None), "numpy": ("https://numpy.org/doc/stable/", None), - "narwhals": ("https://narwhals-dev.github.io/narwhals/", None), + "narwhals": ("https://narwhals-dev.github.io/narwhals", None), + "xarray": ("https://docs.xarray.dev/en/stable/", None), + "pandas": ("https://pandas.pydata.org/docs", None), } # List of patterns, relative to source directory, that match files and diff --git a/docs/notebooks/example.ipynb b/docs/notebooks/example.ipynb index b748b0c..e0e412c 100644 --- a/docs/notebooks/example.ipynb +++ b/docs/notebooks/example.ipynb @@ -13,24 +13,111 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "from anndata import AnnData" + "%load_ext autoreload\n", + "%autoreload 2" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "rng = np.random.default_rng()\n", - "adata = AnnData(rng.normal(size=(20, 10)))" + "import scanpy as sc\n", + "import annsel as an" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'zarr'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m adata \u001b[38;5;241m=\u001b[39m sc\u001b[38;5;241m.\u001b[39mdatasets\u001b[38;5;241m.\u001b[39mpbmc68k_reduced()\n\u001b[0;32m----> 2\u001b[0m \u001b[43madata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_zarr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest.zarr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Dev/annsel/.venv/lib/python3.12/site-packages/anndata/_core/anndata.py:1926\u001b[0m, in \u001b[0;36mAnnData.write_zarr\u001b[0;34m(self, store, chunks)\u001b[0m\n\u001b[1;32m 1914\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\\\u001b[39;00m\n\u001b[1;32m 1915\u001b[0m \u001b[38;5;124;03mWrite a hierarchical Zarr array store.\u001b[39;00m\n\u001b[1;32m 1916\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1922\u001b[0m \u001b[38;5;124;03m Chunk shape.\u001b[39;00m\n\u001b[1;32m 1923\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1924\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_io\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m write_zarr\n\u001b[0;32m-> 1926\u001b[0m \u001b[43mwrite_zarr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunks\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Dev/annsel/.venv/lib/python3.12/site-packages/anndata/_io/__init__.py:18\u001b[0m, in \u001b[0;36mwrite_zarr\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrite_zarr\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mzarr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m write_zarr\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m write_zarr(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n", + "File \u001b[0;32m~/Dev/annsel/.venv/lib/python3.12/site-packages/anndata/_io/zarr.py:9\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mzarr\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sparse\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01manndata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_warnings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OldFormatWarning\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'zarr'" + ] + } + ], + "source": [ + "adata = sc.datasets.pbmc68k_reduced()\n", + "adata.write_zarr(\"test.zarr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 700 × 765\n", + " obs: 'bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'louvain'\n", + " var: 'n_counts', 'means', 'dispersions', 'dispersions_norm', 'highly_variable'\n", + " uns: 'bulk_labels_colors', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'\n", + " obsm: 'X_pca', 'X_umap'\n", + " varm: 'PCs'\n", + " obsp: 'distances', 'connectivities'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata" ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 224 × 4\n", + " obs: 'bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'louvain'\n", + " var: 'n_counts', 'means', 'dispersions', 'dispersions_norm', 'highly_variable'\n", + " uns: 'bulk_labels_colors', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'\n", + " obsm: 'X_pca', 'X_umap'\n", + " varm: 'PCs'\n", + " obsp: 'distances', 'connectivities'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.an.filter(\n", + " an.var_names().is_in([\"HES4\", \"TNFRSF4\", \"SSU72\", \"PARK7\"]),\n", + " an.obs_col(\"bulk_labels\").is_in([\"CD14+ Monocyte\", \"CD19+ B\"]),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.12 ('squidpy39')", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -44,12 +131,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" - }, - "vscode": { - "interpreter": { - "hash": "ae6466e8d4f517858789b5c9e8f0ed238fb8964458a36305fca7bddc149e9c64" - } + "version": "3.12.5" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index fac9833..0dbebe6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,9 +25,10 @@ classifiers = [ dependencies = [ "anndata", "more-itertools>=10.5", - "narwhals>=1.13.2", + "narwhals>=1.13.3", # for debug logging (referenced from the issue template) "session-info", + "zarr>=2.18.3", ] optional-dependencies.dev = [ "esbonio>=0.16.5", @@ -53,6 +54,10 @@ optional-dependencies.doc = [ "sphinxcontrib-bibtex>=2.6.3", "sphinxext-opengraph", ] +optional-dependencies.nb = [ + "jupyterlab>=4.3", + "marimo>=0.9.16", +] optional-dependencies.test = [ "coverage", "pytest", diff --git a/ruff.toml b/ruff.toml index 839ed1e..cdb0cd3 100644 --- a/ruff.toml +++ b/ruff.toml @@ -31,6 +31,7 @@ select = [ "NPY", # NumPy-specific rules "PD", # Pandas code "PTH", # pathlib + "R", # Refactor ] ignore = [ "B008", # Errors from function calls in argument defaults. These are fine when the result is immutable. diff --git a/src/annsel/__init__.py b/src/annsel/__init__.py index 7528ac3..4d36736 100644 --- a/src/annsel/__init__.py +++ b/src/annsel/__init__.py @@ -1,7 +1,8 @@ from importlib.metadata import version +from .core import obs_col, obs_names, register_anndata_accessor, var_col, var_names, x from .tl import AnnselAccessor -__all__ = ["AnnselAccessor"] +__all__ = ["AnnselAccessor", "obs_col", "var_col", "obs_names", "var_names", "register_anndata_accessor", "x"] __version__ = version("annsel") diff --git a/src/annsel/core/__init__.py b/src/annsel/core/__init__.py index baad4da..efe985a 100644 --- a/src/annsel/core/__init__.py +++ b/src/annsel/core/__init__.py @@ -1,3 +1,19 @@ +from .col import obs_col, obs_names, var_col, var_names, x +from .expr import ObsExpr, ObsNamesExpr, VarExpr, VarNamesExpr, XExpr from .extensions import register_anndata_accessor +from .utils import _map_predicates -__all__ = ["register_anndata_accessor"] +__all__ = [ + "obs_col", + "obs_names", + "var_col", + "var_names", + "register_anndata_accessor", + "x", + "XExpr", + "ObsExpr", + "VarExpr", + "ObsNamesExpr", + "VarNamesExpr", + "_map_predicates", +] diff --git a/src/annsel/core/col.py b/src/annsel/core/col.py new file mode 100644 index 0000000..a55298a --- /dev/null +++ b/src/annsel/core/col.py @@ -0,0 +1,105 @@ +from collections.abc import Callable, Iterable +from typing import Any + +from narwhals.utils import flatten + +from .expr import ObsExpr, ObsNamesExpr, VarExpr, VarNamesExpr, XExpr + + +def _with_names(func: Callable) -> Callable: + def wrapper(plx: Any, *names: str | Iterable[str]) -> Any: + return plx.col(*flatten(names)) + + return wrapper + + +@_with_names +def _func(plx: Any, *names: str | Iterable[str]) -> Any: + return plx.col(*names) + + +class ObsCol: + """Select columns from the :obj:`~anndata.AnnData.obs` DataFrame of an :obj:`~anndata.AnnData` object.""" + + def __call__(self, *names: str | Iterable[str]) -> ObsExpr: + """Select columns from the :obj:`~anndata.AnnData.obs` DataFrame of an :obj:`~anndata.AnnData` object. + + This is a wrapper around the `narwhals.col` function + + + Parameters + ---------- + names + The names of the obs columns to select. + + Returns + ------- + A `narwhals.Expr` object representing the selected columns. + """ + return ObsExpr(lambda plx: _func(plx, *names)) + + +class VarCol: + """Select columns from the :obj:`~anndata.AnnData.var` DataFrame of an :obj:`~anndata.AnnData` object.""" + + def __call__(self, *names: str | Iterable[str]) -> VarExpr: + """Select columns from the :obj:`~anndata.AnnData.var` DataFrame of an :obj:`~anndata.AnnData` object. + + This is a wrapper around the `narwhals.col` function + + Parameters + ---------- + names + The names of the var columns to select. + + Returns + ------- + An `annsel.core.expr.VarExpr` object representing the selected columns. + """ + return VarExpr(lambda plx: _func(plx, *names)) + + +class ObsNames: + """Selects the :obj:`~anndata.AnnData.obs_names` index of the :obj:`~anndata.AnnData` object.""" + + def __call__(self) -> ObsExpr: + """Selects the :obj:`~anndata.AnnData.obs_names` index of the :obj:`~anndata.AnnData` object. + + Returns + ------- + An `annsel.core.expr.ObsExpr` object representing the selected columns. + """ + return ObsNamesExpr(lambda plx: _func(plx, "obs_names")) + + +class VarNames: + """Selects the :obj:`~anndata.AnnData.var_names` index of the :obj:`~anndata.AnnData` object.""" + + def __call__(self) -> VarExpr: + """Selects the :obj:`~anndata.AnnData.var_names` index of the :obj:`~anndata.AnnData` object. + + Returns + ------- + An `annsel.core.expr.VarExpr` object representing the selected columns. + """ + return VarNamesExpr(lambda plx: _func(plx, "var_names")) + + +class X: + """Selects the :obj:`~anndata.AnnData.X` matrix of the :obj:`~anndata.AnnData` object.""" + + def __call__(self, names: str | Iterable[str]) -> XExpr: + """Selects the :obj:`~anndata.AnnData.X` matrix of the :obj:`~anndata.AnnData` object. + + Returns + ------- + An `annsel.core.expr.XExpr` object representing the selected columns. + """ + return XExpr(lambda plx: _func(plx, *names)) + + +obs_col: ObsCol = ObsCol() +var_col: VarCol = VarCol() +obs_names: ObsNames = ObsNames() +var_names: VarNames = VarNames() +x: X = X() diff --git a/src/annsel/core/expr.py b/src/annsel/core/expr.py new file mode 100644 index 0000000..d1ad9bf --- /dev/null +++ b/src/annsel/core/expr.py @@ -0,0 +1,39 @@ +from collections.abc import Callable +from typing import Any + +import narwhals as nw + + +class ObsExpr(nw.Expr): + """A Obs DataFrame wrapper for the `narwhals.Expr` class.""" + + def __init__(self, call: Callable[[Any], Any]) -> None: + super().__init__(call) + + +class VarExpr(nw.Expr): + """A Var DataFrame wrapper for the `narwhals.Expr` class.""" + + def __init__(self, call: Callable[[Any], Any]) -> None: + super().__init__(call) + + +class XExpr(nw.Expr): + """A X DataFrame wrapper for the `narwhals.Expr` class.""" + + def __init__(self, call: Callable[[Any], Any]) -> None: + super().__init__(call) + + +class ObsNamesExpr(nw.Expr): + """A Obs Names DataFrame wrapper for the `narwhals.Expr` class.""" + + def __init__(self, call: Callable[[Any], Any]) -> None: + super().__init__(call) + + +class VarNamesExpr(nw.Expr): + """A Var Names DataFrame wrapper for the `narwhals.Expr` class.""" + + def __init__(self, call: Callable[[Any], Any]) -> None: + super().__init__(call) diff --git a/src/annsel/core/extensions.py b/src/annsel/core/extensions.py index b119b56..cc26637 100644 --- a/src/annsel/core/extensions.py +++ b/src/annsel/core/extensions.py @@ -58,17 +58,18 @@ def decorator(accessor): return decorator -def register_anndata_accessor(name): - """Register a custom accessor on anndata.AnnData objects. +def register_anndata_accessor(name: str): + """Register a custom accessor on :class:`~anndata.AnnData` objects. Parameters ---------- - name : str + name Name under which the accessor should be registered. A warning is issued if this name conflicts with a preexisting attribute. See Also -------- - register_dataset_accessor + xarray.register_dataset_accessor + xarray.register_dataarray_accessor """ return _register_accessor(name, ad.AnnData) diff --git a/src/annsel/core/utils.py b/src/annsel/core/utils.py new file mode 100644 index 0000000..46d8c9f --- /dev/null +++ b/src/annsel/core/utils.py @@ -0,0 +1,87 @@ +from collections.abc import Iterable +from typing import Literal + +import anndata as ad +import pandas as pd +from attr import dataclass +from narwhals.typing import IntoExpr +from scipy import sparse + +from annsel.core import ObsExpr, ObsNamesExpr, VarExpr, VarNamesExpr, XExpr + + +def _extract_X( + adata: ad.AnnData, + layer: str | None = None, + keep_sparse: bool = True, + sparse_method: Literal["csr", "csc"] | None = None, +) -> pd.DataFrame: + match (keep_sparse, sparse.issparse(adata.layers[layer]), sparse_method): + case (True, True, _): + _X = adata.layers[layer] + return pd.DataFrame.sparse.from_spmatrix(data=_X, columns=adata.var_names, index=adata.obs_names) + case (True, False, sparse_method): + # Conver to sparse csr + if sparse_method == "csr": + _X = sparse.csr_matrix(adata.layers[layer]) + elif sparse_method == "csc": + _X = sparse.csc_matrix(adata.layers[layer]) + else: + raise ValueError(f"Invalid sparse_method: {sparse_method}") + return pd.DataFrame.sparse.from_spmatrix(data=_X, columns=adata.var_names, index=adata.obs_names) + case _: + return adata.to_df(layer=layer) + + +@dataclass +class GroupedPredicates: + """Grouped predicates.""" + + obs: list[IntoExpr] + var: list[IntoExpr] + x: list[IntoExpr] + obs_names: list[IntoExpr] + var_names: list[IntoExpr] + + +def _map_predicates( + *predicates: Iterable[IntoExpr | Iterable[IntoExpr] | list[bool]], +) -> GroupedPredicates: + _obs = [] + _var = [] + _obs_names = [] + _var_names = [] + _x = [] + for predicate in predicates: + match p := predicate[0] if isinstance(predicate, tuple) else predicate: + case ObsExpr(): + _obs.append(p) + case VarExpr(): + _var.append(p) + case XExpr(): + _x.append(p) + case ObsNamesExpr(): + _obs_names.append(p) + case VarNamesExpr(): + _var_names.append(p) + case _: + raise ValueError(f"Invalid predicate: {predicate}") + return GroupedPredicates( + _obs, + _var, + _x, + _obs_names, + _var_names, + ) + + +# @nw.narwhalify +# def _compare_indices(first: IntoDataFrameT, second: IntoDataFrameT) -> IntoSeriesT: +# return first & second + + +# def _fold_compare(*indices: pd.DataFrame) -> pd.Series: +# start = indices[0] +# for i in indices[1:]: +# start = _compare_indices(start, i) +# return start diff --git a/src/annsel/tl/_filter.py b/src/annsel/tl/_filter.py new file mode 100644 index 0000000..0782f3b --- /dev/null +++ b/src/annsel/tl/_filter.py @@ -0,0 +1,50 @@ +from collections.abc import Iterable +from typing import Literal + +import anndata as ad +import narwhals as nw +import pandas as pd +from attr import dataclass +from narwhals.typing import IntoDataFrame, IntoExpr + +from annsel.core.utils import _extract_X + + +@nw.narwhalify +def _filter_df(df: IntoDataFrame, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> IntoDataFrame: + return df.filter(*predicates) + + +def _filter_adata_by_obs(adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> pd.Index: + return _filter_df(adata.obs, *predicates).index + + +def _filter_adata_by_var(adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> pd.Index: + return _filter_df(adata.var, *predicates).index + + +def _filter_adata_by_var_names(adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> pd.Index: + return _filter_df(adata.var_names.to_frame(name="var_names"), *predicates).index + + +def _filter_adata_by_obs_names(adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> pd.Index: + return _filter_df(adata.obs_names.to_frame(name="obs_names"), *predicates).index + + +@dataclass +class FilteredXIndicies: + obs: pd.Index + var: pd.Index + + +def _filter_adata_by_X( + adata: ad.AnnData, + *predicates: IntoExpr | Iterable[IntoExpr] | list[bool], + layer=str | None, + keep_sparse: bool = True, + sparse_method: Literal["csr", "csc"] | None = None, +) -> FilteredXIndicies: + _X_df: pd.DataFrame = _extract_X(adata, layer=layer, keep_sparse=keep_sparse, sparse_method=sparse_method) + + _X_df = _filter_df(_X_df, *predicates) + return FilteredXIndicies(_X_df.index, _X_df.columns) diff --git a/src/annsel/tl/_utils.py b/src/annsel/tl/_utils.py deleted file mode 100644 index c67266e..0000000 --- a/src/annsel/tl/_utils.py +++ /dev/null @@ -1,199 +0,0 @@ -from collections.abc import Callable, Iterable -from functools import reduce -from typing import Any, Self - -import anndata as ad -import narwhals as nw -import pandas as pd -from narwhals.typing import DataFrameT, IntoExpr, IntoSeriesT - - -class PendingOps: - def __init__(self): - self._expr: Any = None - self._pending_ops: list[tuple[Callable, tuple, dict]] = [] - - def __getattr__(self, name: str): - match getattr(self._expr, name, None): - case None: - raise AttributeError(f"'{self.__class__.__name__}' has no attribute '{name}'") - case method: - - def wrapper(*args, **kwargs): - self._pending_ops.append((method, args, kwargs)) - return self - - return wrapper - - # --- binary --- - def __eq__(self, other: object) -> Self: # type: ignore[override] - self._pending_ops.append((self._expr.__eq__, (other,), {})) - return self - - def __ne__(self, other: object) -> Self: # type: ignore[override] - self._pending_ops.append((self._expr.__ne__, (other,), {})) - return self - - def __and__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__and__, (other,), {})) - return self - - def __rand__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__rand__, (other,), {})) - return self - - def __or__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__or__, (other,), {})) - return self - - def __ror__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__ror__, (other,), {})) - return self - - def __add__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__add__, (other,), {})) - return self - - def __radd__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__radd__, (other,), {})) - return self - - def __sub__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__sub__, (other,), {})) - return self - - def __rsub__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__rsub__, (other,), {})) - return self - - def __truediv__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__truediv__, (other,), {})) - return self - - def __rtruediv__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__rtruediv__, (other,), {})) - return self - - def __mul__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__mul__, (other,), {})) - return self - - def __rmul__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__rmul__, (other,), {})) - return self - - def __le__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__le__, (other,), {})) - return self - - def __lt__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__lt__, (other,), {})) - return self - - def __gt__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__gt__, (other,), {})) - return self - - def __ge__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__ge__, (other,), {})) - return self - - def __pow__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__pow__, (other,), {})) - return self - - def __rpow__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__rpow__, (other,), {})) - return self - - def __floordiv__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__floordiv__, (other,), {})) - return self - - def __rfloordiv__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__rfloordiv__, (other,), {})) - return self - - def __mod__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__mod__, (other,), {})) - return self - - def __rmod__(self, other: Any) -> Self: - self._pending_ops.append((self._expr.__rmod__, (other,), {})) - return self - - # --- unary --- - def __invert__(self) -> Self: - self._pending_ops.append((self._expr.__invert__, (), {})) - return self - - def execute(self): - def apply_op(expr, op_tuple): - op, args, kwargs = op_tuple - return op(*args, **kwargs) - - return reduce(apply_op, self._pending_ops, self._expr) - - -class ObsCol(PendingOps): - def __init__(self, names: str | Iterable[str]): - super().__init__() - self.names = [names] if isinstance(names, str) else list(names) - self._expr = nw.col(self.names) - - -class VarCol(PendingOps): - def __init__(self, names: str | Iterable[str]): - super().__init__() - self.names = [names] if isinstance(names, str) else list(names) - self._expr = nw.col(self.names) - - -class ObsNames(PendingOps): - def __init__(self, names: str | Iterable[str]): - super().__init__() - self.names = [names] if isinstance(names, str) else list(names) - self._expr = nw.col(self.names) - - -class VarNames(PendingOps): - def __init__(self, names: str | Iterable[str]): - super().__init__() - self.names = [names] if isinstance(names, str) else list(names) - self._expr = nw.col(self.names) - - -@nw.narwhalify -def _filter_var_names(var_series: IntoSeriesT, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> IntoSeriesT: - return var_series.filter(*predicates) - - -@nw.narwhalify -def _filter_obs_names(obs_series: IntoSeriesT, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> IntoSeriesT: - return obs_series.filter(*predicates) - - -@nw.narwhalify -def _filter_obs(obs_df: DataFrameT, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> DataFrameT: - return obs_df.filter(*predicates) - - -@nw.narwhalify -def _filter_var(var_df: DataFrameT, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> DataFrameT: - return var_df.filter(*predicates) - - -def _filter_adata_by_var(adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> pd.Index: - return _filter_var(adata.var, *predicates).index - - -def _filter_adata_by_obs(adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> pd.Index: - return _filter_obs(adata.obs, *predicates).index - - -def _filter_adata_by_var_names(adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> pd.Index: - return _filter_var_names(adata.var_names.to_series(), *predicates) - - -def _filter_adata_by_obs_names(adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]) -> pd.Index: - return _filter_obs_names(adata.obs_names.to_series(), *predicates) diff --git a/src/annsel/tl/basic.py b/src/annsel/tl/basic.py index c6fc9a0..128ed05 100644 --- a/src/annsel/tl/basic.py +++ b/src/annsel/tl/basic.py @@ -1,19 +1,25 @@ -from collections.abc import Iterable -from typing import Literal +from collections.abc import Callable, Iterable +from functools import reduce +from operator import and_ +from typing import Any, TypeVar import anndata as ad -from more_itertools import map_reduce +import pandas as pd from narwhals.typing import IntoExpr from annsel.core.extensions import register_anndata_accessor - -from ._utils import ( - ObsCol, - VarCol, +from annsel.core.utils import _map_predicates +from annsel.tl._filter import ( + FilteredXIndicies, _filter_adata_by_obs, + _filter_adata_by_obs_names, _filter_adata_by_var, + _filter_adata_by_var_names, + _filter_adata_by_X, ) +T = TypeVar("T") + @register_anndata_accessor("an") class AnnselAccessor: @@ -22,7 +28,13 @@ class AnnselAccessor: def __init__(self, adata: ad.AnnData): self._obj: ad.AnnData = adata - def filter(self, *predicates: IntoExpr | Iterable[IntoExpr], copy: bool = True) -> ad.AnnData: + def filter( + self, + *predicates: IntoExpr | Iterable[IntoExpr], + copy: bool = True, + layer: str | None = None, + keep_sparse: bool = True, + ) -> ad.AnnData: """Filters the AnnData object by the given predicates. Parameters @@ -32,50 +44,137 @@ def filter(self, *predicates: IntoExpr | Iterable[IntoExpr], copy: bool = True) Returns ------- - _description_ + The filtered AnnData object. + + Examples + -------- + >>> import annsel as an + >>> import scanpy as sc + >>> adata = sc.datasets.pbmc68k_reduced() + >>> adata.an.filter( + >>> an.var_names().is_in(["HES4", "TNFRSF4", "SSU72", "PARK7"]),) + >>> an.obs_col("bulk_labels").is_in(["CD14+ Monocyte", "CD19+ B"]), + >>> ) + AnnData object with n_obs × n_vars = 224 × 4 + obs: 'bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'louvain' + var: 'n_counts', 'means', 'dispersions', 'dispersions_norm', 'highly_variable' + uns: 'bulk_labels_colors', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups' + obsm: 'X_pca', 'X_umap' + varm: 'PCs' + obsp: 'distances', 'connectivities' """ + grouped_predicates = _map_predicates(*predicates) - def keyfunc(pred) -> Literal["obs", "var"]: - match pred: - case ObsCol(): - return "obs" - case VarCol(): - return "var" - case _: - return "obs" - - def valuefunc(pred): - match pred: - case ObsCol() | VarCol(): - return pred.execute() - case _: - return pred - - grouped_predicates = map_reduce(predicates, keyfunc=keyfunc, valuefunc=valuefunc, reducefunc=list) - - if len(var_predicates := grouped_predicates.get("var", [])) > 0: + if len(var_predicates := grouped_predicates.var) > 0: filtered_var_idx = _filter_adata_by_var(self._obj, *var_predicates) else: filtered_var_idx = self._obj.var_names - if len(obs_predicates := grouped_predicates.get("obs", [])) > 0: + + if len(var_names_predicates := grouped_predicates.var_names) > 0: + filtered_var_names = _filter_adata_by_var_names(self._obj, *var_names_predicates) + else: + filtered_var_names = self._obj.var_names + + if len(obs_predicates := grouped_predicates.obs) > 0: filtered_obs_idx = _filter_adata_by_obs(self._obj, *obs_predicates) else: filtered_obs_idx = self._obj.obs_names - filtered_adata = self._obj[filtered_obs_idx, filtered_var_idx] + + if len(obs_names_predicates := grouped_predicates.obs_names) > 0: + filtered_obs_names = _filter_adata_by_obs_names(self._obj, *obs_names_predicates) + else: + filtered_obs_names = self._obj.obs_names + + if len(x_predicates := grouped_predicates.x) > 0: + filtered_x_indices: FilteredXIndicies = _filter_adata_by_X( + self._obj, *x_predicates, layer=layer, keep_sparse=keep_sparse + ) + else: + filtered_x_indices = FilteredXIndicies(self._obj.obs_names, self._obj.var_names) + + final_obs_idx = pd.Series( + list(reduce(and_, [set(filtered_obs_idx), set(filtered_x_indices.obs), set(filtered_obs_names)])) + ) + + final_var_idx = pd.Series( + list(reduce(and_, [set(filtered_var_idx), set(filtered_x_indices.var), set(filtered_var_names)])) + ) + + filtered_adata = self._obj[final_obs_idx, final_var_idx] if copy: return filtered_adata.copy() - else: - return filtered_adata + return filtered_adata def select(self, *predicates: IntoExpr | Iterable[IntoExpr], copy: bool = True) -> ad.AnnData: - """#TODO""" + """Selects the AnnData object by the given predicates. + + Parameters + ---------- + copy, optional + _description_, by default True + + Returns + ------- + _description_ + """ raise NotImplementedError - # """Selects the AnnData object by the given predicates.""" - # if len(obs_names_predicates := grouped_predicates.get("obs_names", [])) > 0: - # filtered_obs_names = _filter_adata_by_obs_names(self._obj, *obs_names_predicates) - # else: - # filtered_obs_names = [] - # if len(var_names_predicates := grouped_predicates.get("var_names", [])) > 0: - # filtered_var_names = _filter_adata_by_var_names(self._obj, *var_names_predicates) - # else: - # filtered_var_names = [] + + def pipe(self, func: Callable[..., T] | tuple[Callable[..., T], str], *args: Any, **kwargs: Any) -> Any: + """ + Apply chainable functions ``func(self, *args, **kwargs)`` that expect :class:`~anndata.AnnData`. + + Parameters + ---------- + func + Function to apply to this :class:`~anndata.AnnData` object. ``args``, and ``kwargs`` are passed into ``func``. + Alternatively a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a string indicating + the keyword of ``callable`` that expects the xarray object. + *args + Positional arguments passed into ``func``. + **kwargs + Keyword arguments passed into ``func``. + + Returns + ------- + The return type of ``func``. + + Raises + ------ + ValueError + When the data target is both the pipe target and a keyword argument. + + Notes + ----- + Use ``.pipe`` when chaining together functions that expect AnnData objects, e.g., instead of writing + + .. code:: python + + f(g(h(adata), arg1=a), arg2=b, arg3=cs) + + You can write + + .. code:: python + + (adata.an.pipe(h).pipe(g, arg1=a).an.pipe(f, arg2=b, arg3=c)) + + If you have a function that takes the data as (say) the second argument, pass a tuple indicating + which keyword expects the data. For example suppose ``f`` takes its data as ``arg2`` + + .. code:: python + + (adata.an.pipe(h).pipe(g, arg1=a).an.pipe((f, "arg2"), arg1=a, arg3=c)) + + See Also + -------- + pandas.DataFrame.pipe + xarray.DataArray.pipe + xarray.Dataset.pipe + """ + if isinstance(func, tuple): + func, target = func + if target in kwargs: + raise ValueError(f"{target} is both the pipe target and a keyword argument") + kwargs[target] = self + else: + args = (self,) + args + return func(*args, **kwargs) diff --git a/uv.lock b/uv.lock index 0018a7a..80a190b 100644 --- a/uv.lock +++ b/uv.lock @@ -55,6 +55,7 @@ dependencies = [ { name = "more-itertools" }, { name = "narwhals" }, { name = "session-info" }, + { name = "zarr" }, ] [package.optional-dependencies] @@ -81,6 +82,10 @@ doc = [ { name = "sphinxcontrib-bibtex" }, { name = "sphinxext-opengraph" }, ] +nb = [ + { name = "jupyterlab" }, + { name = "marimo" }, +] test = [ { name = "coverage" }, { name = "pytest" }, @@ -96,10 +101,12 @@ requires-dist = [ { name = "esbonio", marker = "extra == 'dev'", specifier = ">=0.16.5" }, { name = "ipykernel", marker = "extra == 'doc'" }, { name = "ipython", marker = "extra == 'doc'" }, + { name = "jupyterlab", marker = "extra == 'nb'", specifier = ">=4.3" }, + { name = "marimo", marker = "extra == 'nb'", specifier = ">=0.9.16" }, { name = "matplotlib", marker = "extra == 'doc'", specifier = ">=3.9.2" }, { name = "more-itertools", specifier = ">=10.5" }, { name = "myst-nb", marker = "extra == 'doc'", specifier = ">=1.1.2" }, - { name = "narwhals", specifier = ">=1.13.2" }, + { name = "narwhals", specifier = ">=1.13.3" }, { name = "pandas", marker = "extra == 'doc'" }, { name = "pre-commit", marker = "extra == 'dev'" }, { name = "pytest", marker = "extra == 'test'" }, @@ -117,6 +124,22 @@ requires-dist = [ { name = "sphinx-tabs", marker = "extra == 'doc'" }, { name = "sphinxcontrib-bibtex", marker = "extra == 'doc'", specifier = ">=2.6.3" }, { name = "sphinxext-opengraph", marker = "extra == 'doc'" 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"asciitree" }, + { name = "fasteners", marker = "sys_platform != 'emscripten'" }, + { name = "numcodecs" }, + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/23/c4/187a21ce7cf7c8f00c060dd0e04c2a81139bb7b1ab178bba83f2e1134ce2/zarr-2.18.3.tar.gz", hash = "sha256:2580d8cb6dd84621771a10d31c4d777dca8a27706a1a89b29f42d2d37e2df5ce", size = 3603224 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ed/c9/142095e654c2b97133ff71df60979422717b29738b08bc8a1709a5d5e0d0/zarr-2.18.3-py3-none-any.whl", hash = "sha256:b1f7dfd2496f436745cdd4c7bcf8d3b4bc1dceef5fdd0d589c87130d842496dd", size = 210723 }, +] + [[package]] name = "zipp" version = "3.20.2" From 039fbddf6038eaa09702534021ddcc45d4593908 Mon Sep 17 00:00:00 2001 From: Sricharan Reddy Varra Date: Fri, 8 Nov 2024 20:40:01 -0800 Subject: [PATCH 2/7] =?UTF-8?q?=F0=9F=8F=B7=EF=B8=8F=20Fixed=20dataclasses?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/annsel/core/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/annsel/core/utils.py b/src/annsel/core/utils.py index 46d8c9f..d0fb338 100644 --- a/src/annsel/core/utils.py +++ b/src/annsel/core/utils.py @@ -1,9 +1,9 @@ from collections.abc import Iterable +from dataclasses import dataclass from typing import Literal import anndata as ad import pandas as pd -from attr import dataclass from narwhals.typing import IntoExpr from scipy import sparse From 594b1a4d49b1193b1de9c5d8ae10ea4828ce2b3f Mon Sep 17 00:00:00 2001 From: Sricharan Reddy Varra Date: Fri, 8 Nov 2024 20:44:52 -0800 Subject: [PATCH 3/7] =?UTF-8?q?=E2=9C=85=20Dataclasses=20from=20attrs=20ag?= =?UTF-8?q?ain?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/annsel/tl/_filter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/annsel/tl/_filter.py b/src/annsel/tl/_filter.py index 0782f3b..175f23f 100644 --- a/src/annsel/tl/_filter.py +++ b/src/annsel/tl/_filter.py @@ -1,10 +1,10 @@ from collections.abc import Iterable +from dataclasses import dataclass from typing import Literal import anndata as ad import narwhals as nw import pandas as pd -from attr import dataclass from narwhals.typing import IntoDataFrame, IntoExpr from annsel.core.utils import _extract_X From 3ab168d9e0b364a6c4c7e827cb07cb4df82d87d9 Mon Sep 17 00:00:00 2001 From: Sricharan Reddy Varra Date: Fri, 8 Nov 2024 20:46:41 -0800 Subject: [PATCH 4/7] =?UTF-8?q?=F0=9F=93=9D=20Add=20a=20link=20to=20the=20?= =?UTF-8?q?discussions.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1adf66f..16ac7d3 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ See the [changelog][changelog]. ## Contact -For questions and help requests, you can reach out in the [scverse discourse][scverse-discourse]. +For questions and help requests, you can reach out in the [scverse discourse][scverse-discourse] or the [discussions] tab. If you found a bug, please use the [issue tracker][issue-tracker]. ## Citation @@ -76,3 +76,4 @@ If you found a bug, please use the [issue tracker][issue-tracker]. [link-license]: https://opensource.org/licenses/MIT [link-hatch]: https://github.com/pypa/hatch [link-narwhals]: https://github.com/narwhals-dev/narwhals +[link-disucssions]: https://github.com/srivarra/annsel/discussions From 4e1d35cf3d16613c3da47e27791efd0c4e9fe067 Mon Sep 17 00:00:00 2001 From: Sricharan Reddy Varra Date: Sun, 10 Nov 2024 02:36:01 -0800 Subject: [PATCH 5/7] =?UTF-8?q?=E2=9C=A8=20Filter=20mostly=20fixed?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .github/workflows/changelog.yaml | 6 +- .gitignore | 2 + CITATION.cff | 27 -- README.md | 16 +- docs/conf.py | 3 +- docs/index.md | 2 +- docs/notebooks/All of Annsel.ipynb | 307 +++++++++++++++++ docs/notebooks/example.ipynb | 139 -------- docs/references.bib | 48 ++- pyproject.toml | 12 +- ruff.toml | 1 + src/annsel/__init__.py | 12 +- src/annsel/core/__init__.py | 2 + src/annsel/core/methods.py | 85 +++++ src/annsel/core/types.py | 11 + src/annsel/core/utils.py | 35 +- src/annsel/datasets/__init__.py | 3 + src/annsel/datasets/basic.py | 69 ++++ src/annsel/tl/_filter.py | 70 +++- src/annsel/tl/basic.py | 58 +--- tests/conftest.py | 9 + tests/tl/test_basic.py | 38 +++ uv.lock | 527 +++++++++++++++++++++++------ 23 files changed, 1112 insertions(+), 370 deletions(-) delete mode 100644 CITATION.cff create mode 100644 docs/notebooks/All of Annsel.ipynb delete mode 100644 docs/notebooks/example.ipynb create mode 100644 src/annsel/core/methods.py create mode 100644 src/annsel/core/types.py create mode 100644 src/annsel/datasets/__init__.py create mode 100644 src/annsel/datasets/basic.py create mode 100644 tests/conftest.py create mode 100644 tests/tl/test_basic.py diff --git a/.github/workflows/changelog.yaml b/.github/workflows/changelog.yaml index 2d9c84c..caead64 100644 --- a/.github/workflows/changelog.yaml +++ b/.github/workflows/changelog.yaml @@ -34,9 +34,13 @@ jobs: - name: Generate a changelog uses: orhun/git-cliff-action@v4 + id: git-cliff with: config: cliff.toml - args: --verbose --latest --no-exec --github-repo ${{ github.repository }} + args: --verbose --latest --no-exec env: OUTPUT: CHANGELOG.md GITHUB_REPO: ${{ github.repository }} + + - name: Print the changelog + run: cat "${{ steps.git-cliff.outputs.changelog }}" diff --git a/.gitignore b/.gitignore index 85744c4..8c94019 100644 --- a/.gitignore +++ b/.gitignore @@ -19,3 +19,5 @@ __pycache__/ /docs/generated/ /docs/_build/ /.vscode/* + +.coverage* diff --git a/CITATION.cff b/CITATION.cff deleted file mode 100644 index a94c008..0000000 --- a/CITATION.cff +++ /dev/null @@ -1,27 +0,0 @@ -# This CITATION.cff file was generated with cffinit. -# Visit https://bit.ly/cffinit to generate yours today! - -cff-version: 1.2.0 -title: annsel -message: >- - If this software has been beneficial to you, please cite - it using the metadata from this file. -type: software -authors: - - given-names: Sricharan Reddy - family-names: Varra - email: srivarra@stanford.edu - affiliation: Stanford University - orcid: "https://orcid.org/0009-0000-5757-6818" -repository-code: "https://github.com/srivarra/annsel" -url: "https://annsel.readthedocs.io/en/latest/" -abstract: >- - annsel brings familiar DataFrame-style operations to - AnnData objects, letting you filter and select data - intuitively. Built on the narwhals library, annsel helps - you manipulate complex biological datasets stored in - AnnData format through a user-friendly interface. -keywords: - - anndata - - data-manipulation -license: MIT diff --git a/README.md b/README.md index 16ac7d3..73e2822 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,12 @@
-| | | -| :--------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | -| **Status** | [![Build][badge-build]][link-build] [![Tests][badge-test]][link-test] [![Documentation][badge-docs]][link-docs] [![codecov][badge-codecov]][link-codecov] | -| **Meta** | [![Hatch project][badge-hatch]][link-hatch] [![Ruff][badge-ruff]][link-ruff] [![uv][badge-uv]][link-uv] [![License][badge-license]][link-license] | -| | | +| | | +| :---------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | +| **Status** | [![Build][badge-build]][link-build] [![Tests][badge-test]][link-test] [![Documentation][badge-docs]][link-docs] [![codecov][badge-codecov]][link-codecov] | +| **Meta** | [![Hatch project][badge-hatch]][link-hatch] [![Ruff][badge-ruff]][link-ruff] [![uv][badge-uv]][link-uv] [![License][badge-license]][link-license] | +| **Package** | [![PyPI][badge-pypi]][link-pypi] [![PyPI][badge-python-versions]][link-pypi] | +| | |
@@ -18,12 +19,11 @@ [badge-uv]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json [badge-license]: https://img.shields.io/badge/License-MIT-yellow.svg [badge-hatch]: https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg +[badge-pypi]: https://img.shields.io/pypi/v/annsel.svg?logo=pypi&label=PyPI&logoColor=gold +[badge-python-versions]: https://img.shields.io/pypi/pyversions/annsel.svg?logo=python&label=Python&logoColor=gold `annsel` brings familiar DataFrame-style operations to [`AnnData`](https://anndata.readthedocs.io/en/latest/) objects, making filtering and selection intuitive and straightforward. Built on the [narwhals][link-narwhals] library, it provides a seamless interface for manipulating complex biological datasets stored in `AnnData` format. -> [!WARNING] -> This is a very early work in progress, there is set-in-stone API and breaking changes are likely to occur. - ## Getting started Please refer to the [documentation][link-docs], diff --git a/docs/conf.py b/docs/conf.py index 38cfbec..a44e34b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -65,6 +65,7 @@ "sphinx.ext.linkcode", "sphinx.ext.mathjax", "sphinx_autosummary_accessors", + "nbsphinx", ] autosummary_generate = True @@ -86,7 +87,7 @@ ] myst_url_schemes = ("http", "https", "mailto") nb_output_stderr = "remove" -nb_execution_mode = "off" +nb_execution_mode = "cache" nb_merge_streams = True typehints_defaults = "braces" diff --git a/docs/index.md b/docs/index.md index 8b5f298..bc872aa 100644 --- a/docs/index.md +++ b/docs/index.md @@ -11,5 +11,5 @@ changelog.md contributing.md references.md -notebooks/example +notebooks/All of Annsel ``` diff --git a/docs/notebooks/All of Annsel.ipynb b/docs/notebooks/All of Annsel.ipynb new file mode 100644 index 0000000..5de3600 --- /dev/null +++ b/docs/notebooks/All of Annsel.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# All of Annsel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook tells you just about everything you need to use `annsel`. It's a good starting point to get a feel for the package.\n", + "\n", + ":::{note}\n", + ":class: dropdown\n", + "\n", + "You should be familiar with [`AnnData` ](https://anndata.readthedocs.io/en/latest/) beforehand.\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import annsel as an" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will load a Leukemic bone marrow cytometry dataset :cite:t:`triana_single-cell_2021` You can view the dataset on [CellxGene](https://cellxgene.cziscience.com/e/b3a5a10f-b1cb-4e8e-abce-bf345448625b.cxg/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "adata = an.datasets.leukemic_bone_marrow_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Importing `annsel` will automatically register the `AnnData` accessors. You can access them with `anndata.AnnData.an`.\n", + "\n", + "The accessor allows you to perform operations on `AnnData` objects with respect to columns and indices.\n", + "\n", + "\n", + "In addition, the following `Narwhals` inherited expressions are also available:\n", + "\n", + "\n", + "You express the following:\n", + "1. Observation columns: `an.obs_col()`\n", + "2. Variable columns: `an.var_col()`\n", + "3. Observation names: `an.obs_names()`\n", + "4. Variable names: `an.var_names()`\n", + "5. In the future we will be able to filter by the X matrix: `an.x()`\n", + "\n", + "\n", + "These can be combined with the following methods:\n", + "1. Filter: `an.filter()`\n", + "\n", + "For now only `filter` is implemented. There are two others planned so far: `select` and `groupby`. If you have any other suggestions, let me know.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll find that there are many familiar expressions which you can use. View the supported [`Narwhals` `Expr`](https://narwhals-dev.github.io/narwhals/api-completeness/expr/) methods for a full list." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose we only want to select variables which are Protein Coding genes. We can use the `var_cols` callable to filter the `AnnData` object where `\"feature_type\"` is \"`protein_coding\"`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "adata.an.filter(\n", + " an.var_col([\"feature_type\"]) == \"protein_coding\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{note}\n", + ":class: dropdown\n", + "\n", + "This is equivalent to:\n", + "```python\n", + "\n", + "adata[:, adata.var[\"feature_type\"] == \"protein_coding\"]\n", + "```\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or if we want multiple feature types, we can use the method [`is_in`](https://narwhals-dev.github.io/narwhals/api-reference/expr/#narwhals.Expr.is_in). We can also use set operations to combine multiple predicates into a single predicate.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "adata.an.filter(\n", + " an.var_col([\"feature_type\"]).is_in([\"protein_coding\", \"lncRNA\"])\n", + " | an.var_col([\"feature_name\"]).is_in([\"IGHD\", \"IGHM\", \"IGKC\"])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{note}\n", + ":class: dropdown\n", + "\n", + "This is equivalent to:\n", + "```python\n", + "\n", + "adata[\n", + " :,\n", + " adata.var[\"feature_type\"].isin([\"protein_coding\", \"lncRNA\"])\n", + " | adata.var[\"feature_name\"].isin([\"IGHD\", \"IGHM\", \"IGKC\"]),\n", + "]\n", + "```\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's filter the dataset by the various cell labels using `obs_col`.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "adata.an.filter(an.obs_col([\"Cell_label\"]) == \"Lymphomyeloid prog\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{note}\n", + ":class: dropdown\n", + "\n", + "This is equivalent to:\n", + "```python\n", + "\n", + "adata[adata.obs[\"Cell_label\"] == \"Lymphomyeloid prog\", :]\n", + "```\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can combine multiple predicates to filter by both obs and var." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "adata.an.filter(\n", + " an.obs_col([\"Cell_label\"]) == \"Lymphomyeloid prog\",\n", + " an.var_col([\"feature_type\"]).is_in([\"protein_coding\"]),\n", + " an.var_col([\"vst.mean\"]) >= 0.5,\n", + " an.obs_col([\"sex\"]) == \"male\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{note}\n", + ":class: dropdown\n", + "\n", + "This is equivalent to \n", + "\n", + "```python\n", + "adata[\n", + " (adata.obs[\"Cell_label\"] == \"Lymphomyeloid prog\") & (adata.obs[\"sex\"] == \"male\"),\n", + " (adata.var[\"feature_type\"].isin([\"protein_coding\"])) & (adata.var[\"vst.mean\"] >= 0.5),\n", + "]\n", + "```\n", + ":::\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also filter by `var_names` and `obs_names`. We can also return a copy instead of a view of the original `AnnData` object.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "adata.an.filter(an.var_names().str.starts_with(\"ENSG0000018\"), an.obs_names().str.ends_with(\"1\"), copy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['645636_1', '396386_1', '388968_1', '836004_1', '861250_1', '647413_1',\n", + " '344731_1', '454887_1', '609289_1', '343153_1',\n", + " ...\n", + " '329422_4', '136236_4', '318639_4', '811727_4', '98046_4', '297857_4',\n", + " '454298_4', '815439_4', '53548_4', '545100_4'],\n", + " dtype='object', length=31586)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/notebooks/example.ipynb b/docs/notebooks/example.ipynb deleted file mode 100644 index e0e412c..0000000 --- a/docs/notebooks/example.ipynb +++ /dev/null @@ -1,139 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import scanpy as sc\n", - "import annsel as an" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'zarr'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m adata \u001b[38;5;241m=\u001b[39m sc\u001b[38;5;241m.\u001b[39mdatasets\u001b[38;5;241m.\u001b[39mpbmc68k_reduced()\n\u001b[0;32m----> 2\u001b[0m \u001b[43madata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_zarr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtest.zarr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Dev/annsel/.venv/lib/python3.12/site-packages/anndata/_core/anndata.py:1926\u001b[0m, in \u001b[0;36mAnnData.write_zarr\u001b[0;34m(self, store, chunks)\u001b[0m\n\u001b[1;32m 1914\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\\\u001b[39;00m\n\u001b[1;32m 1915\u001b[0m \u001b[38;5;124;03mWrite a hierarchical Zarr array store.\u001b[39;00m\n\u001b[1;32m 1916\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1922\u001b[0m \u001b[38;5;124;03m Chunk shape.\u001b[39;00m\n\u001b[1;32m 1923\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1924\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_io\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m write_zarr\n\u001b[0;32m-> 1926\u001b[0m \u001b[43mwrite_zarr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunks\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Dev/annsel/.venv/lib/python3.12/site-packages/anndata/_io/__init__.py:18\u001b[0m, in \u001b[0;36mwrite_zarr\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrite_zarr\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mzarr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m write_zarr\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m write_zarr(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n", - "File \u001b[0;32m~/Dev/annsel/.venv/lib/python3.12/site-packages/anndata/_io/zarr.py:9\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mzarr\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sparse\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01manndata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_warnings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OldFormatWarning\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'zarr'" - ] - } - ], - "source": [ - "adata = sc.datasets.pbmc68k_reduced()\n", - "adata.write_zarr(\"test.zarr\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 700 × 765\n", - " obs: 'bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'louvain'\n", - " var: 'n_counts', 'means', 'dispersions', 'dispersions_norm', 'highly_variable'\n", - " uns: 'bulk_labels_colors', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'\n", - " obsm: 'X_pca', 'X_umap'\n", - " varm: 'PCs'\n", - " obsp: 'distances', 'connectivities'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 224 × 4\n", - " obs: 'bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'louvain'\n", - " var: 'n_counts', 'means', 'dispersions', 'dispersions_norm', 'highly_variable'\n", - " uns: 'bulk_labels_colors', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'\n", - " obsm: 'X_pca', 'X_umap'\n", - " varm: 'PCs'\n", - " obsp: 'distances', 'connectivities'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata.an.filter(\n", - " an.var_names().is_in([\"HES4\", \"TNFRSF4\", \"SSU72\", \"PARK7\"]),\n", - " an.obs_col(\"bulk_labels\").is_in([\"CD14+ Monocyte\", \"CD19+ B\"]),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.12.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/references.bib b/docs/references.bib index 9f5bed4..bef8915 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -1,10 +1,42 @@ @article{Virshup_2023, - doi = {10.1038/s41587-023-01733-8}, - url = {https://doi.org/10.1038%2Fs41587-023-01733-8}, - year = 2023, - month = {apr}, - publisher = {Springer Science and Business Media {LLC}}, - author = {Isaac Virshup and Danila Bredikhin and Lukas Heumos and Giovanni Palla and Gregor Sturm and Adam Gayoso and Ilia Kats and Mikaela Koutrouli and Philipp Angerer and Volker Bergen and Pierre Boyeau and Maren Büttner and Gokcen Eraslan and David Fischer and Max Frank and Justin Hong and Michal Klein and Marius Lange and Romain Lopez and Mohammad Lotfollahi and Malte D. Luecken and Fidel Ramirez and Jeffrey Regier and Sergei Rybakov and Anna C. Schaar and Valeh Valiollah Pour Amiri and Philipp Weiler and Galen Xing and Bonnie Berger and Dana Pe'er and Aviv Regev and Sarah A. Teichmann and Francesca Finotello and F. Alexander Wolf and Nir Yosef and Oliver Stegle and Fabian J. Theis and}, - title = {The scverse project provides a computational ecosystem for single-cell omics data analysis}, - journal = {Nature Biotechnology} + doi = {10.1038/s41587-023-01733-8}, + url = {https://doi.org/10.1038%2Fs41587-023-01733-8}, + year = 2023, + month = {apr}, + publisher = {Springer Science and Business Media {LLC}}, + author = {Isaac Virshup and Danila Bredikhin and Lukas Heumos and Giovanni Palla and Gregor Sturm and Adam Gayoso and Ilia Kats and Mikaela Koutrouli and Philipp Angerer and Volker Bergen and Pierre Boyeau and Maren Büttner and Gokcen Eraslan and David Fischer and Max Frank and Justin Hong and Michal Klein and Marius Lange and Romain Lopez and Mohammad Lotfollahi and Malte D. Luecken and Fidel Ramirez and Jeffrey Regier and Sergei Rybakov and Anna C. Schaar and Valeh Valiollah Pour Amiri and Philipp Weiler and Galen Xing and Bonnie Berger and Dana Pe'er and Aviv Regev and Sarah A. Teichmann and Francesca Finotello and F. Alexander Wolf and Nir Yosef and Oliver Stegle and Fabian J. Theis and}, + title = {The scverse project provides a computational ecosystem for single-cell omics data analysis}, + journal = {Nature Biotechnology} +} +@article{uieda2020, + title = {{Pooch}: {A} friend to fetch your data files}, + author = {Leonardo Uieda and Santiago Soler and R{\'{e}}mi Rampin and Hugo van Kemenade and Matthew Turk and Daniel Shapero and Anderson Banihirwe and John Leeman}, + year = {2020}, + doi = {10.21105/joss.01943}, + url = {https://doi.org/10.21105/joss.01943}, + month = jan, + publisher = {The Open Journal}, + volume = {5}, + number = {45}, + pages = {1943}, + journal = {Journal of Open Source Software} +} +@article{triana_single-cell_2021, + title = {Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states}, + volume = {22}, + copyright = {2021 The Author(s)}, + issn = {1529-2916}, + url = {https://www.nature.com/articles/s41590-021-01059-0}, + doi = {10.1038/s41590-021-01059-0}, + abstract = {Single-cell genomics technology has transformed our understanding of complex cellular systems. However, excessive cost and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high-content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all main hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables the functional capacities of precisely mapped cell states to be measured at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry.}, + language = {en}, + number = {12}, + urldate = {2024-11-10}, + journal = {Nature Immunology}, + author = {Triana, Sergio and Vonficht, Dominik and Jopp-Saile, Lea and Raffel, Simon and Lutz, Raphael and Leonce, Daniel and Antes, Magdalena and Hernández-Malmierca, Pablo and Ordoñez-Rueda, Diana and Ramasz, Beáta and Boch, Tobias and Jann, Johann-Christoph and Nowak, Daniel and Hofmann, Wolf-Karsten and Müller-Tidow, Carsten and Hübschmann, Daniel and Alexandrov, Theodore and Benes, Vladimir and Trumpp, Andreas and Paulsen, Malte and Velten, Lars and Haas, Simon}, + month = dec, + year = {2021}, + publisher = {Nature Publishing Group}, + keywords = {/unread, Gene expression analysis, Haematopoiesis, Haematopoietic stem cells, Leukaemia}, + pages = {1577--1589}, } diff --git a/pyproject.toml b/pyproject.toml index 0dbebe6..1731c7c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,16 +23,22 @@ classifiers = [ "Programming Language :: Python :: 3.13", ] dependencies = [ - "anndata", + "anndata[dask]", + "kerchunk>=0.2.6", "more-itertools>=10.5", "narwhals>=1.13.3", + "pooch>=1.8.2", # for debug logging (referenced from the issue template) "session-info", + "tqdm>=4.67", + "universal-pathlib>=0.2.5", + "xxhash>=3.5", "zarr>=2.18.3", ] optional-dependencies.dev = [ "esbonio>=0.16.5", "pre-commit", + "pytest-sugar>=1", "scanpy>=1.10.3", ] optional-dependencies.doc = [ @@ -56,13 +62,15 @@ optional-dependencies.doc = [ ] optional-dependencies.nb = [ "jupyterlab>=4.3", - "marimo>=0.9.16", + "marimo>=0.9.17", ] optional-dependencies.test = [ "coverage", "pytest", "pytest-cov>=6", + "pytest-rich>=0.1.1", "pytest-xdist>=3.6.1", + "scanpy>=1.10.3", ] # https://docs.pypi.org/project_metadata/#project-urls urls.Documentation = "https://annsel.readthedocs.io/" diff --git a/ruff.toml b/ruff.toml index cdb0cd3..d240bab 100644 --- a/ruff.toml +++ b/ruff.toml @@ -32,6 +32,7 @@ select = [ "PD", # Pandas code "PTH", # pathlib "R", # Refactor + "PT", # Pytest ] ignore = [ "B008", # Errors from function calls in argument defaults. These are fine when the result is immutable. diff --git a/src/annsel/__init__.py b/src/annsel/__init__.py index 4d36736..f7be09e 100644 --- a/src/annsel/__init__.py +++ b/src/annsel/__init__.py @@ -1,8 +1,18 @@ from importlib.metadata import version +from . import datasets from .core import obs_col, obs_names, register_anndata_accessor, var_col, var_names, x from .tl import AnnselAccessor -__all__ = ["AnnselAccessor", "obs_col", "var_col", "obs_names", "var_names", "register_anndata_accessor", "x"] +__all__ = [ + "AnnselAccessor", + "obs_col", + "var_col", + "obs_names", + "var_names", + "register_anndata_accessor", + "x", + "datasets", +] __version__ = version("annsel") diff --git a/src/annsel/core/__init__.py b/src/annsel/core/__init__.py index efe985a..a50d314 100644 --- a/src/annsel/core/__init__.py +++ b/src/annsel/core/__init__.py @@ -1,6 +1,7 @@ from .col import obs_col, obs_names, var_col, var_names, x from .expr import ObsExpr, ObsNamesExpr, VarExpr, VarNamesExpr, XExpr from .extensions import register_anndata_accessor +from .methods import NarwhalsMethod from .utils import _map_predicates __all__ = [ @@ -16,4 +17,5 @@ "ObsNamesExpr", "VarNamesExpr", "_map_predicates", + "NarwhalsMethod", ] diff --git a/src/annsel/core/methods.py b/src/annsel/core/methods.py new file mode 100644 index 0000000..e2ef722 --- /dev/null +++ b/src/annsel/core/methods.py @@ -0,0 +1,85 @@ +from abc import ABC, abstractmethod +from collections.abc import Callable, Iterable +from functools import wraps +from typing import Concatenate, ParamSpec, TypeVar + +import anndata as ad +import pandas as pd +from narwhals.typing import IntoExpr + +from annsel.core.types import XIndicies +from annsel.core.utils import GroupedPredicates, _map_predicates + +T = TypeVar("T") +P = ParamSpec("P") + + +def predicate_guard( + predicate_type: str, +) -> Callable[[Callable[P, T]], Callable[Concatenate["NarwhalsMethod", P], T | None]]: + """Decorator that only executes the method if corresponding predicates exist.""" + + def decorator(func: Callable[..., T]) -> Callable[..., T | None]: + @wraps(func) + def wrapper(self: "NarwhalsMethod", *args, **kwargs) -> T | None: + predicates = getattr(self._predicates, predicate_type) + if len(predicates) > 0: + return func(self, *args, **kwargs) + return None + + return wrapper + + return decorator + + +class NarwhalsMethod(ABC): + """Base class for Narwhals methods that operate on AnnData objects. + + This abstract base class defines the interface for methods that filter or transform + AnnData objects using predicates. Subclasses must implement the abstract methods + to define specific filtering/transformation behavior. + + Parameters + ---------- + adata + The AnnData object to operate on + predicates + The predicates to apply to the AnnData object + """ + + def __init__(self, adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]): + self._adata = adata + self._predicates: GroupedPredicates = _map_predicates(*predicates) + + @abstractmethod + def __call__(self, *args, **kwargs): + """Execute the command on the AnnData object.""" + pass + + @abstractmethod + def _run_var_predicates(self) -> pd.Index | None: + pass + + @abstractmethod + def _run_var_names_predicates(self) -> pd.Index | None: + pass + + @abstractmethod + def _run_obs_predicates(self) -> pd.Index | None: + pass + + @abstractmethod + def _run_obs_names_predicates(self) -> pd.Index | None: + pass + + @abstractmethod + def _run_x_predicates(self, layer: str | None = None, keep_sparse: bool = True) -> XIndicies | None: + pass + + @abstractmethod + def _finalize_indices_obs(self, *idx: pd.Index) -> pd.Index: + pass + + @abstractmethod + def _finalize_indices_var(self, *idx: pd.Index) -> pd.Index: + pass diff --git a/src/annsel/core/types.py b/src/annsel/core/types.py new file mode 100644 index 0000000..1834d85 --- /dev/null +++ b/src/annsel/core/types.py @@ -0,0 +1,11 @@ +from dataclasses import dataclass + +import pandas as pd + + +@dataclass +class XIndicies: + """A dataclass representing the indices of the X matrix of an AnnData object.""" + + obs: pd.Index + var: pd.Index diff --git a/src/annsel/core/utils.py b/src/annsel/core/utils.py index d0fb338..7cf6783 100644 --- a/src/annsel/core/utils.py +++ b/src/annsel/core/utils.py @@ -1,5 +1,7 @@ from collections.abc import Iterable from dataclasses import dataclass +from functools import reduce +from operator import and_ from typing import Literal import anndata as ad @@ -16,19 +18,21 @@ def _extract_X( keep_sparse: bool = True, sparse_method: Literal["csr", "csc"] | None = None, ) -> pd.DataFrame: - match (keep_sparse, sparse.issparse(adata.layers[layer]), sparse_method): + match (keep_sparse, sparse.issparse(adata.layers[layer] if layer else adata.X), sparse_method): case (True, True, _): _X = adata.layers[layer] return pd.DataFrame.sparse.from_spmatrix(data=_X, columns=adata.var_names, index=adata.obs_names) - case (True, False, sparse_method): + case (True, False, "csr"): # Conver to sparse csr - if sparse_method == "csr": - _X = sparse.csr_matrix(adata.layers[layer]) - elif sparse_method == "csc": - _X = sparse.csc_matrix(adata.layers[layer]) - else: - raise ValueError(f"Invalid sparse_method: {sparse_method}") + _X = adata.layers[layer] if layer else adata.X + _X = sparse.csr_matrix(_X) return pd.DataFrame.sparse.from_spmatrix(data=_X, columns=adata.var_names, index=adata.obs_names) + case (True, False, "csc"): + _X = sparse.csc_matrix(_X) + return pd.DataFrame.sparse.from_spmatrix(data=_X, columns=adata.var_names, index=adata.obs_names) + case (False, _, _): + _X = adata.layers[layer] if layer else adata.X + return pd.DataFrame(data=_X, columns=adata.var_names, index=adata.obs_names) case _: return adata.to_df(layer=layer) @@ -75,13 +79,8 @@ def _map_predicates( ) -# @nw.narwhalify -# def _compare_indices(first: IntoDataFrameT, second: IntoDataFrameT) -> IntoSeriesT: -# return first & second - - -# def _fold_compare(*indices: pd.DataFrame) -> pd.Series: -# start = indices[0] -# for i in indices[1:]: -# start = _compare_indices(start, i) -# return start +def _get_final_indices( + obj_names: pd.Index, + *idx: pd.Index, +) -> pd.Index: + return obj_names.intersection(pd.Index(list(reduce(and_, map(set, *idx))))) diff --git a/src/annsel/datasets/__init__.py b/src/annsel/datasets/__init__.py new file mode 100644 index 0000000..0266a66 --- /dev/null +++ b/src/annsel/datasets/__init__.py @@ -0,0 +1,3 @@ +from .basic import leukemic_bone_marrow_dataset + +__all__ = ["leukemic_bone_marrow_dataset"] diff --git a/src/annsel/datasets/basic.py b/src/annsel/datasets/basic.py new file mode 100644 index 0000000..cbfb3e5 --- /dev/null +++ b/src/annsel/datasets/basic.py @@ -0,0 +1,69 @@ +from enum import Enum + +import anndata as ad +import pooch +from upath import UPath + +ANNSEL_OS_CACHE = "annsel-cxg-datasets" + + +class CXRDatasetsRegistry(str, Enum): + """A collection of cell x gene datasets.""" + + LEUKEMIC_BONE_MARROW_DONORS = "a48b7e8a-9db3-45f1-9729-c87738c0082f.h5ad" + "15 leukemic bone marrow donors" + + @property + def fname(self) -> str: + """Return the filename. + + Returns + ------- + The record's filename. + """ + return self.value + + +cxg_pooch = pooch.create( + path=pooch.os_cache(ANNSEL_OS_CACHE), + base_url=UPath("https://datasets.cellxgene.cziscience.com/").as_uri(), + registry={ + CXRDatasetsRegistry.LEUKEMIC_BONE_MARROW_DONORS.fname: "e09ea1acda5c7ca39ae42da134ea8cf9e553d99931ee3d6372316ff0137dc93a" + }, +) + + +def leukemic_bone_marrow_dataset() -> ad.AnnData: + """Load a leukemic bone marrow cytometry dataset. + + This single cell cytometry dataset is from 15 leukemic bone marrow donors. It consists of + 31,586 cells and 458 genes. + + The saved file contains the following notable metrics (and more): + - `obs`: clustering information, cell type associations, developmental stage, + - `var`: vst.mean, vst.variance, feature_name, feature_type + - `uns`: cell_type_ontology_term_id_colors + - `obsm`: PCA, UMAP and tSNE embeddings + + Notes + ----- + View the dataset at `CxG`_. + + Citation: :cite:b:`triana_single-cell_2021` + + Download Size: 25.7 MB + + .. _CxG: https://cellxgene.cziscience.com/e/b3a5a10f-b1cb-4e8e-abce-bf345448625b.cxg/ + + Returns + ------- + The Leukemic Bone Marrow Donors dataset as an AnnData object. + + """ + _adata_path = UPath(cxg_pooch.fetch(CXRDatasetsRegistry.LEUKEMIC_BONE_MARROW_DONORS.fname, progressbar=True)) + + adata = ad.io.read_h5ad( + _adata_path, + ) + adata.strings_to_categoricals() + return adata diff --git a/src/annsel/tl/_filter.py b/src/annsel/tl/_filter.py index 175f23f..c5dc87c 100644 --- a/src/annsel/tl/_filter.py +++ b/src/annsel/tl/_filter.py @@ -1,5 +1,4 @@ from collections.abc import Iterable -from dataclasses import dataclass from typing import Literal import anndata as ad @@ -7,7 +6,9 @@ import pandas as pd from narwhals.typing import IntoDataFrame, IntoExpr -from annsel.core.utils import _extract_X +from annsel.core.methods import NarwhalsMethod, predicate_guard +from annsel.core.types import XIndicies +from annsel.core.utils import _extract_X, _get_final_indices, _map_predicates @nw.narwhalify @@ -31,20 +32,69 @@ def _filter_adata_by_obs_names(adata: ad.AnnData, *predicates: IntoExpr | Iterab return _filter_df(adata.obs_names.to_frame(name="obs_names"), *predicates).index -@dataclass -class FilteredXIndicies: - obs: pd.Index - var: pd.Index - - def _filter_adata_by_X( adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool], layer=str | None, keep_sparse: bool = True, sparse_method: Literal["csr", "csc"] | None = None, -) -> FilteredXIndicies: +) -> XIndicies: _X_df: pd.DataFrame = _extract_X(adata, layer=layer, keep_sparse=keep_sparse, sparse_method=sparse_method) _X_df = _filter_df(_X_df, *predicates) - return FilteredXIndicies(_X_df.index, _X_df.columns) + return XIndicies(_X_df.index, _X_df.columns) + + +class FilterAnnData(NarwhalsMethod): + def __init__(self, adata: ad.AnnData, *predicates: IntoExpr | Iterable[IntoExpr] | list[bool]): + self._adata = adata + self._predicates = _map_predicates(*predicates) + + @predicate_guard("var") + def _run_var_predicates(self) -> pd.Index: + return _filter_adata_by_var(self._adata, *self._predicates.var) + + @predicate_guard("var_names") + def _run_var_names_predicates(self) -> pd.Index: + return _filter_adata_by_var_names(self._adata, *self._predicates.var_names) + + @predicate_guard("obs") + def _run_obs_predicates(self) -> pd.Index: + return _filter_adata_by_obs(self._adata, *self._predicates.obs) + + @predicate_guard("obs_names") + def _run_obs_names_predicates(self) -> pd.Index: + return _filter_adata_by_obs_names(self._adata, *self._predicates.obs_names) + + @predicate_guard("x") + def _run_x_predicates(self, layer: str | None = None, keep_sparse: bool = True) -> XIndicies: + return _filter_adata_by_X(self._adata, *self._predicates.x, layer=layer, keep_sparse=keep_sparse) + + def _finalize_indices_obs(self, *idx: pd.Index) -> pd.Index: + return _get_final_indices(self._adata.obs_names, *idx) + + def _finalize_indices_var(self, *idx: pd.Index) -> pd.Index: + return _get_final_indices(self._adata.var_names, *idx) + + def __call__(self, layer: str | None = None, keep_sparse: bool = True) -> tuple[pd.Index, pd.Index]: + obs_indices = [] + var_indices = [] + + # Run predicates - guards will return None if no predicates exist + if (var_idx := self._run_var_predicates()) is not None: + var_indices.append(var_idx) + if (var_names_idx := self._run_var_names_predicates()) is not None: + var_indices.append(var_names_idx) + if (obs_idx := self._run_obs_predicates()) is not None: + obs_indices.append(obs_idx) + if (obs_names_idx := self._run_obs_names_predicates()) is not None: + obs_indices.append(obs_names_idx) + if x_indices := self._run_x_predicates(layer=layer, keep_sparse=keep_sparse): + obs_indices.append(x_indices.obs) + var_indices.append(x_indices.var) + + # Finalize indices or use defaults + final_obs_idx = self._finalize_indices_obs(obs_indices) if obs_indices else self._adata.obs_names + final_var_idx = self._finalize_indices_var(var_indices) if var_indices else self._adata.var_names + + return final_obs_idx, final_var_idx diff --git a/src/annsel/tl/basic.py b/src/annsel/tl/basic.py index 128ed05..f2405ca 100644 --- a/src/annsel/tl/basic.py +++ b/src/annsel/tl/basic.py @@ -1,21 +1,12 @@ from collections.abc import Callable, Iterable -from functools import reduce -from operator import and_ from typing import Any, TypeVar import anndata as ad -import pandas as pd from narwhals.typing import IntoExpr from annsel.core.extensions import register_anndata_accessor -from annsel.core.utils import _map_predicates from annsel.tl._filter import ( - FilteredXIndicies, - _filter_adata_by_obs, - _filter_adata_by_obs_names, - _filter_adata_by_var, - _filter_adata_by_var_names, - _filter_adata_by_X, + FilterAnnData, ) T = TypeVar("T") @@ -31,7 +22,7 @@ def __init__(self, adata: ad.AnnData): def filter( self, *predicates: IntoExpr | Iterable[IntoExpr], - copy: bool = True, + copy: bool = False, layer: str | None = None, keep_sparse: bool = True, ) -> ad.AnnData: @@ -41,6 +32,13 @@ def filter( ---------- predicates The predicates to filter the AnnData object by. + copy, optional + Whether to return a copy of the AnnData object, or return a view of the original. + Defaults to `False`, which returns a view. + layer, optional + The layer to filter the AnnData object by. + keep_sparse, optional + Whether to keep the sparse matrix. Returns ------- @@ -63,42 +61,8 @@ def filter( varm: 'PCs' obsp: 'distances', 'connectivities' """ - grouped_predicates = _map_predicates(*predicates) - - if len(var_predicates := grouped_predicates.var) > 0: - filtered_var_idx = _filter_adata_by_var(self._obj, *var_predicates) - else: - filtered_var_idx = self._obj.var_names - - if len(var_names_predicates := grouped_predicates.var_names) > 0: - filtered_var_names = _filter_adata_by_var_names(self._obj, *var_names_predicates) - else: - filtered_var_names = self._obj.var_names - - if len(obs_predicates := grouped_predicates.obs) > 0: - filtered_obs_idx = _filter_adata_by_obs(self._obj, *obs_predicates) - else: - filtered_obs_idx = self._obj.obs_names - - if len(obs_names_predicates := grouped_predicates.obs_names) > 0: - filtered_obs_names = _filter_adata_by_obs_names(self._obj, *obs_names_predicates) - else: - filtered_obs_names = self._obj.obs_names - - if len(x_predicates := grouped_predicates.x) > 0: - filtered_x_indices: FilteredXIndicies = _filter_adata_by_X( - self._obj, *x_predicates, layer=layer, keep_sparse=keep_sparse - ) - else: - filtered_x_indices = FilteredXIndicies(self._obj.obs_names, self._obj.var_names) - - final_obs_idx = pd.Series( - list(reduce(and_, [set(filtered_obs_idx), set(filtered_x_indices.obs), set(filtered_obs_names)])) - ) - - final_var_idx = pd.Series( - list(reduce(and_, [set(filtered_var_idx), set(filtered_x_indices.var), set(filtered_var_names)])) - ) + filter_adata = FilterAnnData(self._obj, *predicates) + final_obs_idx, final_var_idx = filter_adata(layer=layer, keep_sparse=keep_sparse) filtered_adata = self._obj[final_obs_idx, final_var_idx] if copy: diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..3737346 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,9 @@ +import anndata as ad +import pytest + +from annsel.datasets import leukemic_bone_marrow_dataset + + +@pytest.fixture +def lbm_dataset() -> ad.AnnData: + return leukemic_bone_marrow_dataset() diff --git a/tests/tl/test_basic.py b/tests/tl/test_basic.py new file mode 100644 index 0000000..6533796 --- /dev/null +++ b/tests/tl/test_basic.py @@ -0,0 +1,38 @@ +import anndata as ad +import anndata.tests.helpers as ath +import pytest + +import annsel as an + + +class TestFilter: + def test_filter_var_names(self, lbm_dataset: ad.AnnData): + adata = lbm_dataset.an.filter(an.var_names().str.starts_with("ENSG0000018")) + verify_adata = lbm_dataset[:, lbm_dataset.var_names.str.startswith("ENSG0000018")] + + ath.assert_adata_equal(adata, verify_adata) + + def test_filter_var(self, lbm_dataset: ad.AnnData): + adata = lbm_dataset.an.filter(an.var_col(["vst.mean"]) >= 1) + verify_adata = lbm_dataset[:, lbm_dataset.var["vst.mean"] >= 1] + + ath.assert_adata_equal(adata, verify_adata) + + def test_filter_obs(self, lbm_dataset: ad.AnnData): + adata = lbm_dataset.an.filter(an.obs_col("Cell_label") == "Lymphomyeloid prog") + verify_adata = lbm_dataset[lbm_dataset.obs["Cell_label"] == "Lymphomyeloid prog"] + + ath.assert_adata_equal(adata, verify_adata) + + def test_filter_obs_names(self, lbm_dataset: ad.AnnData): + adata = lbm_dataset.an.filter(an.obs_names().str.ends_with("4")) + verify_adata = lbm_dataset[lbm_dataset.obs_names.str.endswith("4")] + + ath.assert_adata_equal(adata, verify_adata) + + @pytest.mark.xfail(reason="Issues with spare X") + def test_filter_X(self, lbm_dataset: ad.AnnData): + adata = lbm_dataset.an.filter(an.x(["HES4"]) >= 1) + verify_adata = lbm_dataset[lbm_dataset[:, "HES4"].X >= 1, :] + + ath.assert_adata_equal(adata, verify_adata) diff --git a/uv.lock b/uv.lock index 80a190b..06ac6ca 100644 --- a/uv.lock +++ b/uv.lock @@ -29,7 +29,7 @@ wheels = [ [[package]] name = "anndata" -version = "0.10.9" +version = "0.11.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "array-api-compat" }, @@ -41,9 +41,14 @@ dependencies = [ { name = "pandas" }, { name = "scipy" }, ] 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141670 }, +] + +[package.optional-dependencies] +dask = [ + { name = "dask", extra = ["array"] }, ] [[package]] @@ -51,10 +56,15 @@ name = "annsel" version = "0.0.1" source = { editable = "." } dependencies = [ - { name = "anndata" }, + { name = "anndata", extra = ["dask"] }, + { name = "kerchunk" }, { name = "more-itertools" }, { name = "narwhals" }, + { name = "pooch" }, { name = "session-info" }, + { name = "tqdm" }, + { name = "universal-pathlib" }, + { name = "xxhash" }, { name = "zarr" }, ] @@ -62,6 +72,7 @@ dependencies = [ dev = [ { name = "esbonio" }, { name = "pre-commit" }, + { name = "pytest-sugar" }, { name = "scanpy" }, ] doc = [ @@ -90,29 +101,36 @@ test = [ { name = "coverage" }, { name = "pytest" }, { name = "pytest-cov" }, + { name = "pytest-rich" }, { name = "pytest-xdist" }, + { name = "scanpy" }, ] [package.metadata] requires-dist = [ - { name = "anndata" }, + { name = "anndata", extras = ["dask"] }, { name = "coverage", marker = "extra == 'test'" }, { name = 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