update readme

README.md

@@ -1,13 +1,44 @@
 # NichePCA
 
+[![Version](https://img.shields.io/pypi/v/nichepca)](https://pypi.org/project/nichepca/)
+[![License](https://img.shields.io/pypi/l/nichepca)](https://github.com/imsb-uke/nichepca)
+[![Python Version Required](https://img.shields.io/pypi/pyversions/nichepca)](https://pypi.org/project/nichepca/)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
+[![PyPI downloads](https://img.shields.io/pepy/dt/nichepca?label=PyPI%20downloads&logo=pypi)](https://pepy.tech/project/nichepca)
 [![Tests][badge-tests]][link-tests]
+
 <!-- [![Documentation][badge-docs]][link-docs] -->
 
 [badge-tests]: https://img.shields.io/github/actions/workflow/status/imsb-uke/nichepca/test.yaml?branch=main
 [link-tests]: https://github.com/imsb-uke/nichepca/actions/workflows/test.yaml
 [badge-docs]: https://img.shields.io/readthedocs/nichepca
 
-Package for PCA-based spatial domain identification in single-cell spatial transcriptomics data. The corresonding manuscript was published in [Bioinformatics](https://academic.oup.com/bioinformatics/article/41/1/btaf005/7945104?).
+Package for PCA-based spatial domain identification in single-cell spatial transcriptomics data. The corresponding manuscript was published in [Bioinformatics](https://academic.oup.com/bioinformatics/article/41/1/btaf005/7945104?).
+
+## Installation
+
+You need to have Python 3.10 or newer installed on your system. If you don't have
+Python installed, we recommend installing [Mambaforge](https://github.com/conda-forge/miniforge#mambaforge) or [Miniconda](https://docs.anaconda.com/miniconda/miniconda-install/).
+
+To create a new conda environment with Python 3.11:
+
+```bash
+conda create -n npc-env python=3.11 -y
+conda activate npc-env
+```
+
+NichePCA can be installed from PyPI:
+
+```bash
+pip install nichepca
+```
+
+To install the latest development version:
+
+```bash
+pip install git+https://github.com/imsb-uke/nichepca.git@main
+```
 
 ## Getting started
 
@@ -55,25 +86,8 @@ or without `"pca"` at all:
 npc.wf.nichepca(adata, knn=25, pipeline=["norm", "log1p", "agg"])
 ```
 
-## Setting parameters
+## Hyperparameter choice
 We found that higher number of neighbors e.g., `knn=25` lead to better results in brain tissue, while `knn=10` works well for kidney data. We recommend to qualitatively optimize these parameters on a small subset of your data. The number of PCs (`n_comps=30` by default) seems to have negligible effect on the results.
-## Installation
-
-You need to have Python 3.10 or newer installed on your system. If you don't have
-Python installed, we recommend installing [Mambaforge](https://github.com/conda-forge/miniforge#mambaforge) or [Miniconda](https://docs.anaconda.com/miniconda/miniconda-install/).
-
-To create a new conda environment with Python 3.10:
-
-```bash
-conda create -n npc-env python=3.10 -y
-conda activate npc-env
-```
-
-Install the latest development version:
-
-```bash
-pip install git+https://github.com/imsb-uke/nichepca.git@main
-```
 
 ## Contributing
 

pyproject.toml

@@ -18,6 +18,14 @@ maintainers = [
 urls.Documentation = "https://nichepca.readthedocs.io/"
 urls.Source = "https://github.com/imsb-uke/nichepca"
 urls.Home-page = "https://github.com/imsb-uke/nichepca"
+classifiers = [
+  "License :: OSI Approved :: MIT 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",
+]
 dependencies = [
     "scanpy (>=1.11.4,<2.0.0)",
     "torch (>=2.8.0,<3.0.0)",

src/nichepca/workflows/_nichepca.py

@@ -32,6 +32,7 @@ def nichepca(
     aggr: str = "mean",
     allow_harmony: bool = True,
     max_iter_harmony: int = 50,
+    remove_graph: bool = False,
     **kwargs,
 ):
     """
@@ -67,6 +68,9 @@ def nichepca(
         Whether to allow Harmony integration.
     max_iter_harmony : int, optional
         Maximum number of iterations for Harmony.
+    remove_graph : bool, optional
+        Whether to remove the constructed graph from ``adata.uns`` after the workflow
+        completes.
     **kwargs : dict
         Additional keyword arguments.
 
@@ -195,3 +199,6 @@ def nichepca(
             index=ad_tmp.var_names,
             columns=[f"PC{i}" for i in range(n_comps)],
         )
+
+    if remove_graph and "graph" in adata.uns:
+        del adata.uns["graph"]

tests/test_workflows.py

@@ -68,6 +68,11 @@ def test_nichepca_single():
 
     assert np.all(X_npca_0 == X_npca_1)
 
+    # test graph removal
+    adata = generate_dummy_adata()
+    npc.wf.nichepca(adata, knn=5, pipeline=pipeline, remove_graph=True)
+    assert "graph" not in adata.uns
+
     # test with pca on raw counts
     pipeline = ("pca", "agg")