sva2: The adjusted sva code using irwsva.build2

In ttScreening: Genome-Wide DNA Methylation Sites Screening by Use of Training and Testing Samples

Description

This function is the modified SVA function in which it uses the irwsva.build2 function rather than the irwsva.build function to build the surrogate variables. Thus, only a single line has been altered from the original SVA() function.

Usage

sva2(dat, mod, mod0 = NULL, n.sv = NULL, method = c("irw", "two-step"), 
	vfilter = NULL, B = 5, numSVmethod = "be")

Arguments

dat	An m by n (m cpg sites by n subjects) matrix of methylation data.
mod	A n by k model matrix corresponding to the primary model fit (see model.matrix).
mod0	A n by k0 model matrix corresponding to the null model to be compared to mod.
n.sv	Optional. The number of surrogate variables to estimate, can be estimated using the num.sv function.
method	Choose between the iteratively re-weighted or two-step surrogate variable estimation algorithms.
vfilter	The number of most variable CpG sites to use when building SVs, must be between 100 and m.
B	The number of iterations of the algorithm to perform.
numSVmethod	The method for determining the number of surrogate variables to use.

Details

See http://www.bioconductor.org/packages/release/bioc/manuals/sva/man/sva.pdf

Value

sv	A n by n.sv matrix where each column is a distinct surrogate variable.
pprob.gam	A vector with the posterior probability estimates that each row is affected by dependence.
pprob.b	A vector with the posterior probabiliity estimates that each row is affected by the variables in mod, but not in mod0.
n.sv	The number of suggorate variables estimated.

Note

sva Vignette http://www.biostat.jhsph.edu/~jleek/sva/

Author(s)

Original sva: Jeffrey T. Leek <[e-mail]>, John Storey [e-mail]

References

Original sva: Leek JT and Storey JD. (2008) A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences, 105: 18718-18723.

Leek JT and Storey JD. (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genetics, 3: e161.

ttScreening documentation built on May 2, 2019, 2:51 p.m.