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sctransform

R package for normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

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.

Quick start

devtools::install_github(repo = 'ChristophH/sctransform')
normalized_data <- sctransform::vst(umi_count_matrix)$y

Help

For usage examples see vignettes in inst/doc or use the built-in help after installation
?sctransform::vst

Available vignettes:
Variance stabilizing transformation
Using sctransform in Seurat

Reference

Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. bioRxiv 576827 (2019). doi:10.1101/576827

An early version of this work was used in the paper Developmental diversification of cortical inhibitory interneurons, Nature 555, 2018.

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Install

install.packages('sctransform')

Monthly Downloads

52,846

Version

0.2.1

License

GPL-3 | file LICENSE

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Maintainer

Christoph Hafemeister

Last Published

December 17th, 2019

Functions in sctransform (0.2.1)

plot_model_pars

Plot estimated and fitted model parameters
is_outlier

Identify outliers
smooth_via_pca

Smooth data by PCA
row_var

Variance per row
row_gmean

Geometric mean per row
robust_scale_binned

Robust scale using median and mad per bin
robust_scale

Robust scale using median and mad
vst

Variance stabilizing transformation for UMI count data
get_residuals

Return Pearson or deviance residuals of regularized models
compare_expression

Compare gene expression between two groups
plot_model

Plot observed UMI counts and model
pbmc

Peripheral Blood Mononuclear Cells (PBMCs)
get_model_var

Return average variance under negative binomial model
correct_counts

Correct data by setting all latent factors to their median values and reversing the regression model
generate

Generate data from regularized models.
correct

Correct data by setting all latent factors to their median values and reversing the regression model
get_residual_var

Return variance of residuals of regularized models