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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:Christoph Hafemeister [aut], Saket Choudhary [aut, cre], Rahul Satija [ctb]

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sctransform.pdf |sctransform.html✨
sctransform/json (API)
NEWS

# Install 'sctransform' in R:

install.packages('sctransform', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/satijalab/sctransform/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

pbmc - Peripheral Blood Mononuclear Cells
umify_data - Transformation functions for umify

8.64 score 1 stars 70 packages 1.5k scripts 27k downloads 34 mentions 13 exports 46 dependencies

Last updated 1 years agofrom:de74747424. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 12 2024
R-4.5-linux-x86_64	OK	Nov 12 2024

Exports:correct correct_counts diff_mean_test diff_mean_test_conserved generate get_model_var get_residual_var get_residuals plot_model plot_model_pars smooth_via_pca umify vst

Dependencies:cli codetools colorspace digest dplyr fansi farver future future.apply generics ggplot2 globals glue gridExtra gtable isoband labeling lattice lifecycle listenv magrittr MASS Matrix matrixStats mgcv munsell nlme parallelly pillar pkgconfig plyr R6 RColorBrewer Rcpp RcppArmadillo reshape2 rlang scales stringi stringr tibble tidyselect utf8 vctrs viridisLite withr

Help page	Topics
Compare gene expression between two groups	compare_expression
Correct data by setting all latent factors to their median values and reversing the regression model	correct
Correct data by setting all latent factors to their median values and reversing the regression model	correct_counts
Non-parametric differential expression test for sparse non-negative data	diff_mean_test
Find differentially expressed genes that are conserved across samples	diff_mean_test_conserved
Generate data from regularized models.	generate
Return average variance under negative binomial model	get_model_var
Get median of non zero UMIs from a count matrix	get_nz_median2
Return variance of residuals of regularized models	get_residual_var
Return Pearson or deviance residuals of regularized models	get_residuals
Identify outliers	is_outlier
Convert a given matrix to dgCMatrix	make.sparse
Peripheral Blood Mononuclear Cells (PBMCs)	pbmc
Plot observed UMI counts and model	plot_model
Plot estimated and fitted model parameters	plot_model_pars
Robust scale using median and mad	robust_scale
Robust scale using median and mad per bin	robust_scale_binned
Geometric mean per row	row_gmean
Variance per row	row_var
Smooth data by PCA	smooth_via_pca
Quantile normalization of cell-level data to match typical UMI count data	umify
Transformation functions for umify	umify_data
Variance stabilizing transformation for UMI count data	vst

Package: sctransform 0.4.1

sctransform: Variance Stabilizing Transformations for Single Cell UMI Data

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