Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

AltAnalyze is a multi-functional and easy-to-use software package for automated single-cell and bulk gene and splicing analyses. Easy-to-use precompiled graphical user-interface versions available from our website.

Coarse-graining of large single-cell RNA-seq data into metacells

Using Bulk Gene Expression to Estimate Cell-Type-Specific Gene Expression via Deconvolution

metadata and data from the database

The Cornell Single-Cell Muscle Project (scMuscle) aims to collect, analyze and provide to the research community skeletal muscle transcriptomic data

Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data

Single cell transcriptomic data from Tasic, et al. (2016)

V-SVA: An R Shiny application for detecting and annotating hidden sources of variation in single cell RNA-seq data

A package that performs cell type annotations on single cell resolution of spatial transcriptomics data, find the niche interactions and covariation patterns between interacted cell types.

PicturedRocks Single Cell Analysis Tool

ScRNA-seq Analysis Copilot serves as an interactive agent to learn single cell sequencing, answering questions and helping new learners to practice analysis.

Gene expression on C. elegans single cells

Benchmarking records for scRNA-seq CNV detection.

Large-scale Benchmarking of Single-cell Differential Expression Analysis Methods

Repo linked to our recent publication in Science Advances

V-SVA: An R Shiny application for detecting and annotating hidden sources of variation in single cell RNA-seq data

scRNA-seq datasets to support Jung et al, Sci Adv, 7(6): eabe5735, 2021. DOI: 10.1126/sciadv.abe5735

Predict human Erythroid cell tissue of origin using the BD Rhapsody single cell Immune responce panel gene expression data

This repository provides the python implementation of the AISTATS2025 paper "Global Ground Metric Learning with Applications to scRNA data" by Damin Kühn nad Michael T. Schaub.