sva.id: Estimate the number of significant eigenvectors to include in...

Description Usage Arguments Details Value Author(s) References See Also

View source: R/eigenR2.R

Description

Estimate the number of significant eigenvectors from the residuals of a high-dimensional data set given the model fit.

Usage

1
  sva.id(dat, mod, B=20, eigen.sig=0.10, seed=NULL)

Arguments

dat

A m response variables by n samples matrix of data

mod

A n by k model matrix corresponding to the primary model fit, if mod is NULL, we estimate the number of significant eigenvectors in the expression data. (see model.matrix)

B

The number of null iterations to perform.

eigen.sig

The significance cutoff for eigenvectors.

seed

A numeric seed for reproducible results. Optional.

Details

Note that this function is a modified function from the package sva by Leek JT and Storey JD. The model matrix should include a column for an intercept. sva.id estimates the number of surrogate variables to include in the analysis as described in Leek and Storey (2007).

Value

A list containing:

n.sv

The number of significant surrogate variables

p.sv

The p-values for each eigenvector

Author(s)

Jeff T. Leek [email protected] and John D. Storey [email protected]

References

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

See Also

eigenR2


StoreyLab/eigenR2 documentation built on May 10, 2017, 3:33 p.m.