insitucnv 0.1.0

Reusable tools for in situ CNV inference in image-based spatial transcriptomics.

InSituCNV

Reusable notebook workflow for inferring copy-number variation (CNV) profiles from image-based spatial transcriptomics data.

This repository contains the general package version of the workflow used in In Situ Inference of Copy Number Variations in Image-Based Spatial Transcriptomics by Jensen et al. The manuscript reproduction code is kept in a separate repository: https://github.com/Moldia/InSituCNV-manuscript

References

InSituCNV builds upon several excellent packages in the single-cell and spatial transcriptomics ecosystem:

• infercnvpy: Core CNV inference logic.
• scVelo: Transcriptomic smoothing and analysis.
• Scanpy: General single-cell analysis framework.
• AnnData: Data structures for single-cell data.

If you use InSituCNV in your research, please cite:

Jensen et al. In Situ inference of copy number variations in image-based spatial transcriptomics. bioRxiv (2025). https://doi.org/10.1101/2025.07.02.662761

Installation

Install the released package from PyPI when available:

pip install insitucnv

Clone the repository and install the package in editable mode:

git clone https://github.com/Moldia/InSituCNV.git
cd InSituCNV
conda env create -f insitucnv.yml
conda activate insitucnv_env
pip install -e ".[dev,docs]"
jupyter lab

Open the notebooks from the notebooks/ directory in JupyterLab.

Development Setup

Install the package and development tools into your active environment:

pip install -e ".[dev,docs]"

Notebooks and examples should import the installed package directly:

import insitucnv as icv

No sys.path edits are required when the editable install is active.

Running Tests

pytest

Building Docs Locally

sphinx-build -b html docs docs/_build/html

The documentation uses Sphinx, the Read the Docs theme, autodoc/autosummary API pages, and myst-nb for notebook tutorials.

Releasing Package

Build the source distribution and wheel locally:

python -m build

Publish TestPyPI releases through the publish-testpypi.yml GitHub Actions workflow after configuring Trusted Publishing in TestPyPI. Do not store PyPI or TestPyPI API tokens in the repository.

What You Need Before Running

Prepare an .h5ad file with the information needed by the notebooks:

• raw counts, preferably in adata.layers["raw_counts"]; if raw counts are in adata.X, the first notebook can copy them into adata.layers["raw_counts"];
• spatial coordinates in adata.obsm["spatial"];
• a nearest-neighbor graph for smoothing, usually from scanpy.pp.neighbors;
• an adata.obs column that identifies normal or healthy reference cells for infercnvpy;
• the exact category names in that reference column that should be used as the normal reference, for example immune, stromal, or other non-tumor cell types;
• gene names that can be matched to genomic coordinates. The package includes the default infercnvpy gene coordinate table, and the notebooks show where to adjust this if your gene annotation differs.

Do not use normalized or log-transformed values as raw counts. The CNV workflow normalizes, smooths, and log-transforms the raw counts itself.

Notebook Workflow

Run the notebooks in order for your own dataset, editing the setup cells at the top of each notebook.

1. Run InSituCNV

notebooks/run_insitucnv.ipynb

Use this notebook to run the full CNV analysis step by step:

• load your spatial transcriptomics .h5ad file;
• choose the raw count layer name with RAW_LAYER;
• choose the normal/reference annotation with REFERENCE_KEY and REFERENCE_CATEGORIES;
• normalize raw counts;
• smooth normalized counts over the neighbor graph;
• add genomic positions;
• run infercnvpy;
• cluster CNV profiles across selected Leiden resolutions;
• visualize chromosome heatmaps and spatial CNV cluster plots.

After reviewing the heatmap and spatial plot, manually edit:

• NORMAL_CLUSTERS;
• TUMOR_CLUSTERS, if you want to specify tumor clusters directly;
• TUMOR_CLONE_CLUSTERS, if some tumor clusters have distinct enough CNV profiles to report as separate tumor clones.

No cluster is marked as normal automatically.

Outputs

The notebooks write results under their configured output directories, usually results/... or outputs/.... Typical outputs include:

• checked input .h5ad files;
• AnnData objects with CNV values and CNV cluster labels;
• CNV chromosome heatmaps;
• spatial CNV cluster plots;
• optional manually annotated tumor/normal and tumor-clone cell group tables;
• optional mean CNV profiles for manually selected tumor cells.