Deep probabilistic analysis of single-cell and spatial omics data

Spatial Single Cell Analysis in Python

CellRank: dynamics from multi-view single-cell data

DANCE: a deep learning library and benchmark platform for single-cell analysis

LIANA+: an all-in-one framework for cell-cell communication

Differential expression analysis for single-cell RNA-seq data.

A wrapper for the kallisto | bustools workflow for single-cell RNA-seq pre-processing

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.

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.

A Package for Cas9-Enabled Single Cell Lineage Tracing Tree Reconstruction

A BLAST-like toolkit for large-scale scRNA-seq data querying and annotation.

Deep Learning based cell composition analysis with Scaden.

User-friendly tool to infer cell-cell interactions and communication from gene expression of interacting proteins

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics

Accurate and fast cell marker gene identification with COSG

An object oriented python library for topological data analysis of high-throughput single-cell RNA-seq data

𝒫robabilistic modeling of RNA velocity ⬱

CellNavi is a deep learning framework designed to predict genes driving cellular transitions.

scDCC: Single Cell Deep Constrained Clustering

Single-cell exploratory data analysis for RNA-Seq