Deep probabilistic analysis of single-cell and spatial omics data

Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses

A tool for semi-automatic cell type classification

Single cell perturbation prediction

muon is a multimodal omics Python framework

Clustering scRNAseq by genotypes

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

A tool for semi-automatic cell type harmonization and integration

Cell-type Annotation for Single-cell Transcriptomics using Deep Learning with a Weighted Graph Neural Network

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.

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.

Doublet detection in single-cell RNA-seq data.

Demultiplexing pooled scRNA-seq data with or without genotype reference

ST Pipeline contains the tools and scripts needed to process and analyze the raw files generated with the Spatial Transcriptomics method in FASTQ format.

Single-cell Hierarchical Poisson Factorization

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

A tool for fast and accurate summarizing of variant calling format (VCF) files

A pipeline to identify pathogenic microorganisms from scRNA-seq raw data.

An unofficial demultiplexing strategy for SPLiT-seq RNA-Seq data

cTPnet Package