Deep probabilistic analysis of single-cell and spatial omics data

muon is a multimodal omics Python framework

Single cell analysis in the browser

Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).

scPerturb: A resource and a python/R tool for single-cell perturbation data

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.

Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).

Normalize CITEseq Data

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

ADTnorm normalizes the cell surface protein measurement of CITE-seq data, facilitating across batches and across studies data integration.

A Snakemake workflow and MrBiomics module for processing and visualizing (multimodal) sc/snRNA-seq data generated with 10X Genomics Kits or in the MTX matrix file format powered by the R package Seurat.

scpca-nf is the Nextflow workflow for processing Single-cell Pediatric Cancer Atlas Portal data

Code and results from TotalSeqC antibody titration and pipeline benchmarking for CITE-seq experiments

Novel joint clustering method with scRNA-seq and CITE-seq data

MultiModal Classifier Hierarchy (MMoCHi)

experimental interface between R and scvi-tools

CITE-seq profiling of human PBMCs at baseline and activation conditions

Tools for single-cell feature barcoding analysis

Scripts related to bulkRNA-seq as well as single-cell CITE-seq and VDJ-seq data of Gearty et al. (2021)

Project analyzing multi-omics genomics data from a COVID-19 study.