imputeLCMD: generate.ExpressionData – R documentation

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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

Author(s)

Cosmin Lazar

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]]
  
## Not run: 
hist(exprsData[,1])

## End(Not run)

## 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))
  }

imputeLCMD

A collection of methods for left-censored missing data imputation

v2.0

GPL (>= 2)

Authors

Cosmin Lazar

Initial release

2015-01-18