num.sv

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

The model matrix being used to fit the data

One of "be" or "leek" as described in the details section

method

You may choose to filter to the vfilter most variable rows before performing the analysis

vfilter

The number of permutaitons to use if method = "be"

Set a seed when using the permutation approach

seed


This function estimates the number of surrogate variables that should be included
in a differential expression model. The default approach is based on a permutation
procedure originally prooposed by Buja and Eyuboglu 1992. The function also provides
an interface to the asymptotic approach proposed by Leek 2011 Biometrics.


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

num.sv function

This function estimates the number of surrogate variables that should be included
in a differential expression model. The default approach is based on a permutation
procedure originally prooposed by Buja and Eyuboglu 1992. The function also provides
an interface to the asymptotic approach proposed by Leek 2011 Biometrics.

num.sv: A function for calculating the number of surrogate variables to estimate in a model

Description

Usage

Arguments

Value