🔍 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.

Cell type deconvolution for spatial transcriptomics. Scales linearly to million-spot datasets (Visium, Visium HD, Stereo-seq).

Advanced VAE-based pipeline for automated cell type annotation in single-cell RNA-seq data with Optuna optimization and 99%+ accuracy

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

🧬 Analyze spatial transcriptomics data through natural language conversation. Stop writing code, start having conversations with your data. MCP server for Claude Desktop and other LLM agents.

GRaph-based Analysis of Subcellular/Spatial Proteomics

circrna single-cell bioinformatics genomics rna-seq cli python anndata scanpy nanopore

Convert between AnnData and SingleCellExperiment

A Python web application for automating RNA sequencing workflow management with interactive parameter collection, automated processing, and AI-powered chatbot support.

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

Annotated data.

anndata with trees

scverse lineage tracing analysis toolkit

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.

Python pipelines for analysis of adipocyte scRNA-seq data

scanaflow — a streamlined, reproducible Scanpy workflow for scRNA-seq: QC → normalize → cluster → annotate.

Bring your single-cell data to life

muon is a multimodal omics Python framework

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