fsva

A m genes by n arrays matrix of expression data from the database/training data

dbdat

The model matrix for the terms included in the analysis for the training data

The surrogate variable object created by running sva on dbdat using mod.

(optional) A set of test samples to be adjusted using the training database

newdat

If method ="fast" then the SVD is calculated using an online approach, this may introduce slight bias. If method="exact" the exact SVD is calculated, but will be slower

method


This function performs frozen surrogate variable analysis as described in Parker, Corrada Bravo and Leek 2013.
The approach uses a training database to create surrogate variables which are then used
to remove batch effects both from the training database and a new data set for prediction purposes.
For inferential analysis see <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

fsva function

This function performs frozen surrogate variable analysis as described in Parker, Corrada Bravo and Leek 2013.
The approach uses a training database to create surrogate variables which are then used
to remove batch effects both from the training database and a new data set for prediction purposes.
For inferential analysis see <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>.

fsva: A function for performing frozen surrogate variable analysis as proposed in Parker, Corrada Bravo and Leek 2013

Description

Usage

Arguments

Value