Kompot is a Python package for differential abundance and gene expression analysis using Gaussian Process models with JAX backend.

Kompot implements methodologies from the Mellon package for computing differential abundance and gene expression, with a focus on using Mahalanobis distance as a measure of differential expression significance. It leverages JAX for efficient computations and provides a scikit-learn like API with .fit() and .predict() methods.

Key features:

• Computation of differential abundance between conditions
• Gene expression imputation and uncertainty estimation
• Mahalanobis distance calculation for differential expression significance
• JAX-accelerated computations with optional GPU support
• Disk-backed covariance storage for sample variance estimation
• Full scverse compatibility with direct AnnData integration
• Visualization tools for volcano plots, heatmaps, and embeddings
• Command-line interface for pipeline integration

pip install kompot

Or via conda:

conda install -c bioconda kompot

See the installation guide for optional dependencies and JAX GPU support.

import kompot
import anndata as ad

# Load data
adata = ad.read_h5ad("data.h5ad")

# Differential expression
kompot.compute_differential_expression(
    adata,
    groupby="condition",
    condition1="control",
    condition2="treatment",
    obsm_key="X_pca"
)

# Differential expression
kompot de input.h5ad -o output.h5ad \
  --groupby condition \
  --condition1 control \
  --condition2 treatment

• Full Documentation
• Tutorial Notebooks
  • Getting Started
  • Advanced Differential Expression
  • Sample Variance Analysis
• CLI Guide

If you use Kompot in your research, please cite:

@article{Otto2025.06.03.657769,
    author = {Otto, Dominik J. and Arriaga-Gomez, Erica and Thieme, Elana and Yang, Ruijin and Lee, Stanley C. and Setty, Manu},
    title = {Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution},
    year = {2025},
    doi = {10.1101/2025.06.03.657769},
    publisher = {Cold Spring Harbor Laboratory},
    journal = {bioRxiv},
    URL = {https://www.biorxiv.org/content/10.1101/2025.06.03.657769}
}

GNU General Public License v3 (GPLv3)