Computational analysis of single-cell RNA-seq data

In collaboration with many others, we develop and maintain this teaching resource for teaching scRNAseq analysis.

Computational tools

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.

Spatial transcriptomics methods

• SCOTIA is a python package for discovery of ligand-receptor interactions from high-resolution spatial transcriptomics data.

• SpatialQuery is a python package for identifying cell type patterns in high resolution spatial transcriptomics data.

Single-cell methods

• 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.

• scHumanNet is an R package for reference guided construction of gene networks in human.

Other genomics methods

• MicroExonator is a snakemake pipeline for de novo discovery and quantification of short exons.

• TransposonUltimate is a bundle of tools in python for analyzing transposable elements.

• MPRAnator is a webtool for design of massively parallel reporter assays.