simulateData: Simulate Data for Package Testing and Demonstration Purposes

In a4Core: Automated Affymetrix Array Analysis Core Package

Description

Simulate Data for Package Testing and Demonstration Purposes

Usage

  simulateData(nCols = 40, nRows = 1000, nEffectRows = 5, nNoEffectCols = 5,
      betweenClassDifference = 1, withinClassSd = 0.5)

Arguments

nCols	number of samples; currently this should be an even number
nRows	number of features (genes)
nEffectRows	number of differentially expressed features
nNoEffectCols	number of samples for which the profile of a differentially expressed feature will be set similar to the other class
betweenClassDifference	Average mean difference between the two classes to simulate a certain signal in the features for which an effect was introduced; the default is set to 1
withinClassSd	Within class standard deviation used to add a certain noise level to the features for which an effect was introduced; the default standard deviation is set to 0.5

Value

object of class ExpressionSet with the characteristics specified

Note

The simulation assumes the variances are equal between the two classes. Heterogeneity could easily be introduced in the simulation if this would be requested by the users.

Author(s)

W. Talloen and T. Verbeke

Examples

  someEset <- simulateData(nCols = 40, nRows = 1000, nEffectRows = 5, nNoEffectCols = 5)
  someEset

Example output

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16

ExpressionSet (storageMode: lockedEnvironment)
assayData: 1000 features, 40 samples 
  element names: exprs 
protocolData: none
phenoData
  sampleNames: Sample1 Sample2 ... Sample40 (40 total)
  varLabels: type
  varMetadata: type labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation:  

a4Core documentation built on May 2, 2019, 4:48 p.m.