R toolkit for single cell genomics

Deep probabilistic analysis of single-cell and spatial omics data

An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.

Spatial Single Cell Analysis in Python

CellRank: dynamics from multi-view single-cell data

Reference mapping for single-cell genomics

Community-provided extensions to Seurat

Single cell perturbation prediction

Single-cell epigenomics analysis tools

Single cell trajectory detection

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

Generate high quality, publication ready visualizations for single cell transcriptomics data.

Interfaces for HDF5-based Single Cell File Formats

Dataset distribution for Seurat

Convert Seurat objects to 10x Genomics Loupe files.

A Shiny web app for mapping datasets using Seurat v4

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

Python and R SOMA APIs using TileDB’s cloud-native format. Ideal for single-cell data at any scale.

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

A deep learning architecture for robust inference and accurate prediction of cellular dynamics

Semi-supervised adversarial neural networks for classification of single cell transcriptomics data

Predict RNA velocity through deep learning

Rails/Docker application for the Broad Institute's single cell RNA-seq data portal

An R-based interface for loom files

Various utility functions for Seurat v5 single-cell analysis

CREsted is a Python package for training sequence-based deep learning models on scATAC-seq data, for capturing enhancer code and for designing cell type-specific sequences.

Cloud-based single-cell copy-number variation analysis tool

Tutorials, workflows, and convenience scripts for Single Cell Portal