Computational analysis of single-cell RNA-seq data
In collaboration with many others, we develop and maintain this teaching resource for teaching scRNAseq analysis.
All of our computational tools are available at our GitHub site. Please note that some of the older tools may not be actively supported any more as the people responsible for coding have moved on.
SC3 is an R package for unsupervised clustering of scRNAseq data based on transcriptional profile.
scmap is an R package for mapping cells onto a reference dataset.
souporcell is a method for clustering cells by genotype rather than transcriptional profile. Note that souporcell does not require a reference genotype as it will automatically detect variants from your reads.
M3Drop is an R package for feature selection for scRNAseq analysis.
SC3s is a python package for unsupervised clustering based on transcription profiles. It is highly recommended to use this package instead of the original SC3.
scover is a neural network model in python for de novo discovery of regulatory motifs from single-cell data.
scfind is an R package for fast searches of single-cell data.