genSimData.BayesNormal: Generating simulated data set from conditional normal...

Description Usage Arguments Details Value Author(s) References Examples

View source: R/genSimData.BayesNormal.R

Description

Generating simulated data set from conditional normal distributions.

Usage

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genSimData.BayesNormal(
  nCpGs, 
  nCases, 
  nControls,
  mu.n = -2,
  mu.c = 2,
  d0 = 20, 
  s02 = 0.64,
  s02.c = 1.5,
  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.

mu.n

numeric. mean of the conditional normal distribution for controls. See details.

mu.c

numeric. mean of the conditional normal distribution for cases. See details.

d0

integer. degree of freedom for scale-inverse chi squared distribution. See details.

s02

numeric. scaling parameter for scale-inverse chi squared distribution for controls. See details.

s02.c

numeric. scaling parameter for scale-inverse chi squared distribution for cases. See details.

testPara

character string. indicating if the test is for testing equal mean, equal variance, or 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 chi squared distribution for controls. mean1 and var1 are the mean and variance of the chi squared distribution for cases.

applier

function name to do apply operation.

Details

Based on Phipson and Oshlack's (2014) simulation algorithm. For each CpG site, variance of the DNA methylation was first sampled from an scaled inverse chi-squared distribution with degree of freedom d_0 and scaling parameter s_0^2: σ^2_i ~ scale-inv χ^2(d_0, s_0^2). M value for each CpG was then sampled from a normal distribution with mean μ_n and variance equal to the simulated variance σ^2_i. For cases, the variance was first generated from σ^2_{i,c} ~ scale-inv χ^2(d_0, s_{0,c}^2). M value for each CpG was then sampled from a normal distribution with mean μ_c and variance equal to the simulated variance σ^2_{i,c}.

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)

Weiliang Qiu <[email protected]>, Brandon Guo <[email protected]>, Christopher Anderson <[email protected]>, Barbara Klanderman <[email protected]>, Vincent Carey <[email protected]>, Benjamin Raby <[email protected]>

References

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

Examples

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    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

iCheck documentation built on Nov. 1, 2018, 4:23 a.m.