🔍 Explore multimodal omics data easily with `muon`, a powerful Python framework designed for efficient analysis and visualization of diverse biological datasets.

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

BANKSY: Spatial Clustering Algorithm that Unifies Cell-Typing and Tissue Domain Segmentation. Python package for spatial transcriptomics analysis.

scverse lineage tracing analysis toolkit

Annotated data.

Single-cell analysis in Python. Scales to >100M cells.

Convert between AnnData and SingleCellExperiment

anndata with trees

muon is a multimodal omics Python framework

One liner tool to check the quality of your single-cell atlases.

Multi-agent LLM driven cell type annotation for single-cell RNA-Seq data

Enables cellxgene to generate violin, stacked violin, stacked bar, heatmap, volcano, embedding, dot, track, density, 2D density, sankey and dual-gene plot in high-resolution SVG/PNG format. It also performs differential gene expression analysis and provides a Command Line Interface (CLI) for advanced users to perform analysis using python and R.

🏆 #1 Multi-LLM consensus framework | 550+ stars | 95% accuracy | 10+ LLM providers | Leading cell annotation tool

Python pipelines for analysis of adipocyte scRNA-seq data

Single-Cell Atlas Builder: A modular platform for analyzing, integrating, and visualizing single-cell RNA-seq datasets using FastAPI, Scanpy, CellTypist, and optional LLM-powered summaries.

GRaph-based Analysis of Subcellular/Spatial Proteomics

Lightweight, GPU-accelerated single-cell RNA-seq workflow for exploring the colorectal tumor microenvironment (CRC-TME) using Scanpy and scVI.

Single-cell temporal analysis of neutrophils using DRVI

A complete single-cell RNA-seq analysis pipeline implemented in Python/Scanpy, following the sc-best-practices guide up to Section 10: Clustering.

Bring your single-cell data to life