Package: sctransform 0.4.3

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]

sctransform_0.4.3.tar.gz
sctransform_0.4.3.tar.gz(r-4.7-arm64)sctransform_0.4.3.tar.gz(r-4.7-x86_64)sctransform_0.4.3.tar.gz(r-4.6-arm64)sctransform_0.4.3.tar.gz(r-4.6-x86_64)
sctransform_0.4.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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

On CRAN:

Conda:

openblascpp

9.31 score 1 stars 108 packages 2.8k scripts 44k downloads 34 mentions 13 exports 43 dependencies

Last updated from:18ab33de11. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK302
linux-devel-x86_64OK184
source / vignettesOK203
linux-release-arm64OK191
linux-release-x86_64OK189
wasm-releaseOK140

Exports:correctcorrect_countsdiff_mean_testdiff_mean_test_conservedgenerateget_model_varget_residual_varget_residualsplot_modelplot_model_parssmooth_via_pcaumifyvst

Dependencies:clicodetoolscpp11digestdplyrfarverfuturefuture.applygenericsggplot2globalsgluegridExtragtableisobandlabelinglatticelifecyclelistenvmagrittrMASSMatrixmatrixStatsparallellypillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangS7scalesstringistringrtibbletidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Clip matrix values to specified rangeclip_matrix_values
Close progress barclose_progress_bar
Compare gene expression between two groupscompare_expression
Correct data by setting all latent factors to their median values and reversing the regression modelcorrect
Correct data by setting all latent factors to their median values and reversing the regression modelcorrect_counts
Non-parametric differential expression test for sparse non-negative datadiff_mean_test
Find differentially expressed genes that are conserved across samplesdiff_mean_test_conserved
Generate data from regularized models.generate
Extract model formula from model stringget_model_formula
Return average variance under negative binomial modelget_model_var
Get median of non zero UMIs from a count matrixget_nz_median2
Return variance of residuals of regularized modelsget_residual_var
Return Pearson or deviance residuals of regularized modelsget_residuals
Identify outliersis_outlier
Convert a given matrix to dgCMatrixmake.sparse
Peripheral Blood Mononuclear Cells (PBMCs)pbmc
Plot observed UMI counts and modelplot_model
Plot estimated and fitted model parametersplot_model_pars
Prepare regressor data from vst object and cell attributesprepare_regressor_data
Robust scale using median and madrobust_scale
Robust scale using median and mad per binrobust_scale_binned
Geometric mean per rowrow_gmean
Variance per rowrow_var
Setup progress bar for batch processingsetup_progress_bar
Smooth data by PCAsmooth_via_pca
Quantile normalization of cell-level data to match typical UMI count dataumify
Transformation functions for umifyumify_data
Update progress barupdate_progress_bar
Variance stabilizing transformation for UMI count datavst