genSimData.tDistr: Generating simulated data set from t distributions

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

Description

Generating simulated data set from t distributions.

Usage

genSimData.tDistr(
  nCpGs, 
  nCases, 
  nControls,
  df0 = 10, 
  ncp0 = 0, 
  df1 = 6, 
  ncp1 = 2.393, 
  testPara = "var", 
  outlierFlag = FALSE,
  eps = 0.001, 
  applier = lapply)

Arguments

nCpGs	integer. Number of genes.
nCases	integer. Number of cases.
nControls	integer. Number of controls.
df0	integer. Degree of freedom for group 0 (controls).
ncp0	numeric. Non-centrality parameter for group 0 (controls).
df1	integer. Degree of freedom for group 1 (cases).
ncp1	numeric. Non-centrality parameter for group 1 (cases).
testPara	character string. Indicating if the test is for testing equal mean (testPara="mean"), for testing equal variance (testPara="var"), or for testing both equal mean and equal variance (testPara="both").
outlierFlag	logical. Indicating if outliers would be generated. If outlierFlag=TRUE, then we followed Phipson and Oshlack's (2014) simulation studies to generate one outlier for each CpG site by replacing the DNA methylation level of one diseased subject by the maximum of the DNA methylation levels of all CpG sites.
eps	numeric. If |mean0-mean1|<eps then we regard mean0=mean1. Similarly, if |var0-var1|<eps then we regard var0=var1. mean0 and var0 are the mean and variance of the t distribution for controls. mean1 and var1 are the mean and variance of the t distribution for cases.
applier	function name to do apply operation.

Value

An ExpressionSet object. The phenotype data of the ExpressionSet object contains 2 columns: arrayID (array id) and memSubj (subject membership, i.e., case (memSubj=1) or control (memSubj=0)). The feature data of the ExpressionSet object contains 4 elements: probe (probe id), gene (psuedo gene symbol), chr (psuedo chromosome number), and memGenes (indicating if a gene is differentially expressed (when testPara="mean") or indicating if a gene is differentially variable (when testPara="var") ).

Author(s)

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

References

Ahn S. and Wang T. (2013) A Powerful Statistical Method for Indentifying Differentially Methylated Markers in Complex Diseases. Pacific Symposium on Biocomputing. 69-79.

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

Li X, Qiu W, Morrow J, DeMeo DL, Weiss ST, Fu Y, Wang X. (2015) A Comparative Study of Tests for Homogeneity of Variances with Application to DNA Methylation Data. PLoS ONE 10(12): e0145295. PMID: 26683022

Qiu W, Li X, Morrow J, DeMeo DL, Weiss ST, Wang X, Fu Y. New Score Tests for Equality of Variances in the Application of DNA Methylation Data Analysis [Version 2]. Insights Genet Genomics. (2017) 1: 3.2

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",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)
  

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