🔬 Explore and analyze multimodal omics data with the muon framework, designed for efficient handling of diverse biological datasets in Python.

🧬 Analyze spatial gene expression in cancer tissues with this automated Python pipeline, uncovering insights into tumor microenvironments and biological regions.

Deep probabilistic analysis of single-cell and spatial omics data

Mesenchyme autoencoder for development and cancer

Identify variable genes in scRNA-seq and spatial transcriptomics data using approximate Bayesian inference

Useful functions to make your scRNA-seq plot more cool!

Variational Inference for Cell Type Evolution

The starter kit for the CAMDA 2025 Health Privacy Challenge.

An R package providing tools for single-cell RNA-seq data analysis, enhancing existing methods with helper functions.

Extract isoform-level gene expression data from raw SeekOne DD scFAST-seq FASTQ files

Convenient and user-friendly package to streamline common workflows in single-cell RNA sequencing data analysis

R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.

Port of symphony algorithm of single-cell reference atlas mapping to Python

ST Pipeline contains the tools and scripts needed to process and analyze the raw files generated with the Spatial Transcriptomics method in FASTQ format.

muon is a multimodal omics Python framework

Run embedding comparisons for single-cell data

Table of software for the analysis of single-cell RNA-seq data.

Python implementation of scppin for module detection

A Snakemake workflow and MrBiomics module for performing perturbation analyses of pooled (multimodal) CRISPR screens with sc/snRNA-seq read-out (scCRISPR-seq) powered by the R package Seurat's method Mixscape.

A Snakemake workflow and MrBiomics module for processing and visualizing (multimodal) sc/snRNA-seq data generated with 10X Genomics Kits or in the MTX matrix file format powered by the R package Seurat.