A normalization method for single-cell UMI count data using a
variance stabilizing transformation. The transformation is based on a
negative binomial regression model with regularized parameters. As part of the
same regression framework, this package also provides functions for
batch correction, and data correction. See Hafemeister and Satija 2019
This packaged was developed by Christoph Hafemeister in Rahul Satija's lab at the New York Genome Center. Core functionality of this package has been integrated into Seurat, an R package designed for QC, analysis, and exploration of single cell RNA-seq data.
devtools::install_github(repo = 'ChristophH/sctransform')
normalized_data <- sctransform::vst(umi_count_matrix)$y
For usage examples see vignettes in inst/doc or use the built-in help after installation
An early version of this work was used in the paper Developmental diversification of cortical inhibitory interneurons, Nature 555, 2018.