POTestWrapper: Wrapper function to test for equality of variance by using...

In diffMeanVar: Detecting Gene Probes with Different Means or Variances Between Two Groups

Description

Wrapper function to test for equality of variance by using Phipson and Oshlack's (2014) methods

Usage

POTestWrapper(
  es, 
  grpVar = "group", 
  type = "AD",
  esFlag = "es", 
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  nTop = 20, 
  probeID.var = "ProbeID", 
  gene.var = "Symbol", 
  chr.var = "Chromosome", 
  applier = lapply, 
  verbose = FALSE)

Arguments

es	An ExpressionSet object storing gene expression/DNA methylation data, phenotype data, and feature annotation.
grpVar	character string. The name of the phenotype variable indicating arrays' group membership. 0 means control and 1 means case.
type	character string indicating if AD method or SQ method would be used.
esFlag	character string. Indicating if es is an ExpressionSet object or MethylSet object. The program will use exprs function to extract gene expression data or use betas function to extract methylation data.
pvalAdjMethod	character string. Indicating which p-value adjustment will be used to control for multiple testing.
alpha	numeric. Cutoff for p-value or adjusted p-value to determine if a probe is diferentially variable.
nTop	integer. Specifying the number of top probes to be displayed if verbose=TRUE
probeID.var	character string. Feature annotation variable indicating probe ID.
gene.var	character string. Feature annotation variable indicating gene symbol.
chr.var	character string. Feature annotation variable indicating chromosome number.
applier	function name to do apply operation.
verbose	logical. indicating if intermediate results should be output to screen.

Value

A list of 2 elements. The first element frame is unsorted data frame; the second element frame.s is a sorted data frame object storing the analysis results and containing the following columns: probe (probe id), stat (test statistic), pval (raw p-value), p.adj (adjusted p-value), gene (gene symbol), chr (chromosome number), and pos (position of a probe in unsorted data frame).

The data frame is sorted based on the descending order of the absolute value of the test statistic.

Author(s)

Xuan Li <[e-mail]>, Weiliang Qiu <[e-mail]>, Yuejiao Fu <[e-mail]>, Xiaogang Wang <[e-mail]>

References

Phipson B, Oshlack A. DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging. Genome Biology 2014, 15:465.

Examples

    # generate simulated data set from t distribution
    set.seed(1234567)
    es.sim = genSimData.tDistr(nCpGs = 100, nCases = 20, nControls = 20,
      df0 = 10, ncp0 = 0, df1 = 6, ncp1 = 2.393, testPara = "var",
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)
    
    res.POTestWrapper = POTestWrapper(
      es = es.sim,
      grpVar = "memSubj",
      type = "SQ",
      esFlag = "es", 
      pvalAdjMethod = "fdr",
      alpha = 0.05,
      nTop = 20,
      probeID.var = "probe",
      gene.var = "gene",
      chr.var = "chr",
      applier=lapply,
      verbose=TRUE)

diffMeanVar documentation built on May 2, 2019, 2:54 a.m.