imputeLCMD (version 1.0)

generate.ExpressionData: Generate Peptide/Protein Expression Data

Description

Generates a matrix of peptide/protein expression data. It is assumed that the expression data is log - transformed. Therefore, for each sample the peptides/proteins intensities are randomly drawn from a Gaussian distribution.

Usage

generate.ExpressionData(nSamples1, nSamples2, meanSamples, sdSamples, 
                        nFeatures, nFeaturesUp, nFeaturesDown, 
                        meanDynRange, sdDynRange, 
                        meanDiffAbund, sdDiffAbund)

Arguments

nSamples1

Number of samples in condition 1.

nSamples2

Number of samples in condition 2.

meanSamples

Mean value of the background noise.

sdSamples

Standard deviation of the background noise.

nFeatures

Number of peptides/proteins.

nFeaturesUp

Number of peptides/proteins up-regulated.

nFeaturesDown

Number of /peptidesproteins down-regulated.

meanDynRange

Mean value of the dynamic range of peptide/protein expressions.

sdDynRange

Standard deviation of the dynamic range of peptide/protein expressions.

meanDiffAbund

Mean value of the up/down-regulation.

sdDiffAbund

Standard deviation of the up/down-regulation.

Value

• A list including elements:
• data
  Peptide/protein expression matrix
• conditions
  Vector indicating the samples in each condition
• labelFeatures
  Vector indicating features which are down/up/no regulated

See Also

pep2prot, generate.RollUpMap, insertMVs

Examples

dataObj = generate.ExpressionData(nSamples1 = 6, nSamples2 = 6,
                          meanSamples = 0, sdSamples = 0.2,
                          nFeatures = 2000, nFeaturesUp = 100, nFeaturesDown = 100,
                          meanDynRange = 20, sdDynRange = 1,
                          meanDiffAbund = 1, sdDiffAbund = 0.2)
exprsData = dataObj[[1]]
  
hist(exprsData[,1])

## The function is currently defined as
function (nSamples1, nSamples2, meanSamples, sdSamples, nFeatures, 
    nFeaturesUp, nFeaturesDown, meanDynRange, sdDynRange, meanDiffAbund, 
    sdDiffAbund) 
{
    nSamples = nSamples1 + nSamples2
    data = matrix(rnorm(nSamples * nFeatures, meanSamples, sdSamples), 
        nFeatures, nSamples)
    means = rnorm(nFeatures, meanDynRange, sdDynRange)
    data = data + means
    conditions = c(rep(1, nSamples1), rep(2, nSamples2))
    DE.coef.up = matrix(rnorm(nFeaturesUp * nSamples1, meanDiffAbund, 
        sdDiffAbund), nFeaturesUp, nSamples1)
    DE.coef.down = matrix(rnorm(nFeaturesDown * nSamples2, meanDiffAbund, 
        sdDiffAbund), nFeaturesDown, nSamples2)
    data[1:nFeaturesUp, conditions == 1] = DE.coef.up + data[1:nFeaturesUp, 
        conditions == 1]
    data[(nFeaturesUp + 1):(nFeaturesUp + nFeaturesDown), conditions == 
        2] = DE.coef.down + data[(nFeaturesUp + 1):(nFeaturesUp + 
        nFeaturesDown), conditions == 2]
    labelFeatures = c(rep(1, nFeaturesUp), rep(2, nFeaturesDown), 
        rep(3, nFeatures - (nFeaturesUp + nFeaturesDown)))
    row.names(data) = 1:nFeatures
    return(list(data, conditions, labelFeatures))
  }