empirical.controls

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

The model matrix being used to fit the data

The null model being compared when fitting the data

mod0

The number of surogate variables to estimate

n.sv

The number of iterations of the irwsva algorithm to perform

If type is norm then standard irwsva is applied, if type is counts, then the moderated log transform is applied first

type


This function uses the iteratively reweighted surrogate variable analysis approach
to estimate the probability that each gene is an empirical control.


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

empirical.controls function

This function uses the iteratively reweighted surrogate variable analysis approach
to estimate the probability that each gene is an empirical control.

empirical.controls: A function for estimating the probability that each gene is an empirical control

Description

Usage

Arguments

Value