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. A previous version of this work was used in the paper Developmental diversification of cortical inhibitory interneurons, Nature 555, 2018. We are currently working on integrating the functionality of this package into Seurat, an R package designed for QC, analysis, and exploration of single cell RNA-seq data.
This package is in beta status, please sanity check any results, and kindly notify me of any issues you find.
devtools::install_github(repo = 'ChristophH/sctransform')
normalized_data <- sctransform::vst(umi_count_matrix)$y