cran. See also theR-universe documentation.Package: sctransform 0.4.1
Saket Choudhary
sctransform: Variance Stabilizing Transformations for Single Cell UMI Data
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) <doi:10.1186/s13059-019-1874-1>, and Choudhary and Satija (2022) <doi:10.1186/s13059-021-02584-9> for more details.
Authors:
sctransform_0.4.1.tar.gz
sctransform_0.4.1.tar.gz(r-4.5-noble)sctransform_0.4.1.tar.gz(r-4.4-noble)
sctransform_0.4.1.tgz(r-4.4-emscripten)sctransform_0.4.1.tgz(r-4.3-emscripten)
sctransform.pdf |sctransform.html✨
sctransform/json (API)
NEWS
| # Install 'sctransform' in R: |
| install.packages('sctransform', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/satijalab/sctransform/issues
- pbmc - Peripheral Blood Mononuclear Cells
- umify_data - Transformation functions for umify
Last updated 1 years agofrom:de74747424. Checks:2 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 10 2025 |
| R-4.5-linux-x86_64 | OK | Feb 10 2025 |
Exports:correctcorrect_countsdiff_mean_testdiff_mean_test_conservedgenerateget_model_varget_residual_varget_residualsplot_modelplot_model_parssmooth_via_pcaumifyvst
Dependencies:clicodetoolscolorspacedigestdplyrfansifarverfuturefuture.applygenericsggplot2globalsgluegridExtragtableisobandlabelinglatticelifecyclelistenvmagrittrMASSMatrixmatrixStatsmgcvmunsellnlmeparallellypillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangscalesstringistringrtibbletidyselectutf8vctrsviridisLitewithr
Citation
To cite sctransform in publications, please use:
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 20, 296 (2019).
Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biology 23, 1-20 (2022).
Corresponding BibTeX entries:
@Article{,
author = {Christoph Hafemeister and Rahul Satija},
title = {Normalization and variance stabilization of single-cell
RNA-seq data using regularized negative binomial regression},
journal = {Genome Biology},
year = {2019},
volume = {20},
pages = {296},
doi = {10.1186/s13059-019-1874-1},
url = {https://doi.org/10.1186/s13059-019-1874-1},
}
@Article{,
author = {Saket Choudhary and Rahul Satija},
title = {Comparison and evaluation of statistical error models for
scRNA-seq},
journal = {Genome Biology},
year = {2022},
volume = {23},
pages = {20},
doi = {10.1186/s13059-021-02584-9},
url = {https://doi.org/10.1186/s13059-021-02584-9},
}
Readme and manuals
sctransform
R package for normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
The sctransform package was developed by Christoph Hafemeister in Rahul Satija's lab at the New York Genome Center and described in Hafemeister and Satija, Genome Biology 2019. Recent updates are described in (Choudhary and Satija, Genome Biology, 2022). 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
Installation:
# Install sctransform from CRAN
install.packages("sctransform")
# Or the development version from GitHub:
remotes::install_github("satijalab/sctransform", ref="develop")
Running sctransform:
# Runnning sctransform on a UMI matrix
normalized_data <- sctransform::vst(umi_count_matrix)$y
# v2 regularization
normalized_data <- sctransform::vst(umi_count_matrix, vst.flavor="v2")$y
# Runnning sctransform on a Seurat object
seurat_object <- Seurat::SCTransform(seurat_object)
#v2 regularization
seurat_object <- Seurat::SCTransform(seurat_object, vst.flavor="v2")
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
- Examples of how to perform normalization, feature selection, integration, and differential expression with sctransform v2 regularization
Please use the issue tracker if you encounter a problem
References
-
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology 20, 296 (2019). https://doi.org/10.1186/s13059-019-1874-1. An early version of this work was used in the paper Developmental diversification of cortical inhibitory interneurons, Nature 555, 2018.
-
Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biology 23.1 (2022). https://doi.org/10.1186/s13059-021-02584-9