Package: sva 3.53.0

Jeffrey T. Leek

sva: Surrogate Variable Analysis

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

Authors:Jeffrey T. Leek <[email protected]>, W. Evan Johnson <[email protected]>, Hilary S. Parker <[email protected]>, Elana J. Fertig <[email protected]>, Andrew E. Jaffe <[email protected]>, Yuqing Zhang <[email protected]>, John D. Storey <[email protected]>, Leonardo Collado Torres <[email protected]>

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sva.pdf |sva.html✨
sva/json (API)

# Install 'sva' in R:
install.packages('sva', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On BioConductor:sva-3.53.0(bioc 3.20)sva-3.52.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

17 exports 7.14 score 62 dependencies 50 dependents 511 mentions

Last updated 2 months agofrom:7e792c3264

Exports:ComBatComBat_seqempirical.controlsf.pvaluefstatsfsvairwsva.buildnum.svpsvaqsvaread.degradation.matrixssvasvasva_networksva.checksvaseqtwostepsva.build

Dependencies:annotateAnnotationDbiaskpassBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobcachemclicodetoolscpp11crayoncurlDBIedgeRfastmapformatRfutile.loggerfutile.optionsgenefilterGenomeInfoDbGenomeInfoDbDatagluehttrIRangesjsonliteKEGGRESTlambda.rlatticelifecyclelimmalocfitMatrixMatrixGenericsmatrixStatsmemoisemgcvmimenlmeopensslpkgconfigplogrpngR6RcpprlangRSQLiteS4VectorssnowstatmodsurvivalsysUCSC.utilsvctrsXMLxtableXVectorzlibbioc

sva tutorial

Rendered fromsva.Rnwusingutils::Sweaveon Jul 03 2024.

Last update: 2020-03-22
Started: 2014-06-05

Readme and manuals

Help Manual

Help pageTopics
Adjust for batch effects using an empirical Bayes frameworkComBat
Adjust for batch effects using an empirical Bayes framework in RNA-seq raw countsComBat_seq
A function for estimating the probability that each gene is an empirical controlempirical.controls
A function for quickly calculating f statistic p-values for use in svaf.pvalue
A function for quickly calculating f statistics for use in svafstats
A function for performing frozen surrogate variable analysis as proposed in Parker, Corrada Bravo and Leek 2013fsva
A function for estimating surrogate variables by estimating empirical control probesirwsva.build
A function for calculating the number of surrogate variables to estimate in a modelnum.sv
A function for estimating surrogate variables with the two step approach of Leek and Storey 2007psva
A function for computing quality surrogate variables (qSVs)qsva
A function for reading in coverage data from degradation-susceptible regionsread.degradation.matrix
A function for estimating surrogate variables using a supervised approachssva
sva: a package for removing artifacts from microarray and sequencing datasva
A function to adjust gene expression data before network inferencesva_network
A function for post-hoc checking of an sva object to check for degenerate cases.sva.check
A function for estimating surrogate variables for count based RNA-seq data.svaseq
A function for estimating surrogate variables with the two step approach of Leek and Storey 2007twostepsva.build