psva

The transformed data matrix with the variables in rows and samples in columns

A factor variable giving the known batch levels

batch

Other arguments to the <code><a rd-options="" href="/link/sva?package=sva&version=3.20.0" data-mini-rdoc="sva::sva">sva</a></code> function.


This function is the implementation of the two step approach for estimating surrogate
variables proposed by Leek and Storey 2007 PLoS Genetics. This function is primarily
included for backwards compatibility. Newer versions of the sva algorithm are available
through <code><a rd-options="" href="/link/sva?package=sva&version=3.20.0" data-mini-rdoc="sva::sva">sva</a></code>, <code><a rd-options="" href="/link/svaseq?package=sva&version=3.20.0" data-mini-rdoc="sva::svaseq">svaseq</a></code>, with low level functionality available
through <code><a rd-options="" href="/link/irwsva.build?package=sva&version=3.20.0" data-mini-rdoc="sva::irwsva.build">irwsva.build</a></code> and <code><a rd-options="" href="/link/ssva?package=sva&version=3.20.0" data-mini-rdoc="sva::ssva">ssva</a></code>.


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).

Jeffrey T Leek

Surrogate Variable Analysis

psva function

Other arguments to the <code><a href="sva" rd-options="">sva</a></code> function.

This function is the implementation of the two step approach for estimating surrogate
variables proposed by Leek and Storey 2007 PLoS Genetics. This function is primarily
included for backwards compatibility. Newer versions of the sva algorithm are available
through <code><a href="sva" rd-options="">sva</a></code>, <code><a href="svaseq" rd-options="">svaseq</a></code>, with low level functionality available
through <code><a href="irwsva.build" rd-options="">irwsva.build</a></code> and <code><a href="ssva" rd-options="">ssva</a></code>.

psva: A function for estimating surrogate variables with the two step approach of Leek and Storey 2007

Description

Usage

Arguments

Value