num.sv2: Adjusted num.sv which estimates the number of important...

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

Description

This function is directly modified from the orginal num.sv() in the sva package. This function has the tolerance level in the fast.svd() function set back to its orginal default instead of 0.

Usage

num.sv2(dat, mod, method = c("be", "leek"), vfilter = NULL, 
B = 20, sv.sig = 0.1, seed = NULL)

Arguments

dat	A m genes by n arrays matrix of expression data.
mod	A n by k model matrix corresponding to the primary model fit (see model.matrix).
method	The method to use for estimating surrogate variables, for now there is only one option (based ib Buja and Eyuboglu 1992).
vfilter	The number of most variable genes to use when building SVs, must be between 100 and m.
B	The number of null iterations to perform. Only used when method="be".
sv.sig	The significance cutoff for eigengenes. Only used when method="be".
seed	A numeric seed for reproducible results. Optional, only used when method="be".

Details

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

Value

n.sv	The number of significant surrogate variables

Note

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

Author(s)

Original num.sv: 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.