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

The COnstraint-Based Reconstruction and Analysis Toolbox. Documentation:

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

Annotated data.

An interactive explorer for single-cell transcriptomics data

Foundation Models for Genomics & Transcriptomics

Fusing Histology and Genomics via Deep Learning - IEEE TMI

🧬 gget enables efficient querying of genomic reference databases

A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.

starfish: unified pipelines for image-based transcriptomics

Single cell perturbation prediction

R package for analyzing single-cell RNA-seq data

An ontology of cell types

R package for analyzing and interactively exploring large-scale single-cell RNA-seq datasets

R/shiny interface for interactive visualization of data in SummarizedExperiment objects

Cellxgene Gateway allows you to use the Cellxgene Server provided by the Chan Zuckerberg Institute (https://github.com/chanzuckerberg/cellxgene) with multiple datasets.

Python package to perform enrichment analysis from omics data.

Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

Pipeline for processing spatially-resolved gene counts with spatial coordinates and image data. Designed for 10x Genomics Visium transcriptomics.

Hierarchical, iterative clustering for analysis of transcriptomics data in R