MLP: This function calculates p-values for each gene set based on...
In MLP: MLP

Description Usage Arguments Value References Examples

This function calculates p-values for each gene set based on row permutations of the gene p values or column permutations of the expression matrix; the p values can be obtained either as individual gene set p values or p values based on smoothing across gene sets of similar size.

MLP(geneSet, geneStatistic, minGenes = 5, maxGenes = 100,
  rowPermutations = TRUE, nPermutations = 100, smoothPValues = TRUE,
  probabilityVector = c(0.5, 0.9, 0.95, 0.99, 0.999, 0.9999, 0.99999),
  df = 9, addGeneSetDescription = TRUE)

`geneSet`	is the input list of gene sets (components) and gene IDs (character vectors). A gene set can, for example, be a GO category with for each category Entrez Gene identifiers; The getGeneSets function can be used to construct the geneSet argument for different pathway sources.
`geneStatistic`	is either a named numeric vector (if rowPermutations is TRUE) or a numeric matrix of pvalues (if rowPermutations is FALSE). The names of the numeric vector or row names of the matrix should represent the gene IDs.
`minGenes`	minimum number of genes in a gene set for it to be considered (lower threshold for gene set size)
`maxGenes`	maximum number of genes in a gene set for it to be considered (upper threshold for gene set size)
`rowPermutations`	logical indicating whether to use row permutations (TRUE; default) or column permutations (FALSE)
`nPermutations`	is the number of simulations. By default 100 permutations are conducted.
`smoothPValues`	logical indicating whether one wants to calculate smoothed cut-off thresholds (TRUE; default) or not (FALSE).
`probabilityVector`	vector of quantiles at which p values for each gene set are desired
`df`	degrees of freedom for the smooth.spline function used in getSmoothedPValues
`addGeneSetDescription`	logical indicating whether a column with the gene set description be added to the output data frame; defaults to TRUE.

data frame with four (or five) columns: totalGeneSetSize, testedGeneSetSize, geneSetStatistic and geneSetPValue and (if addDescription is set to TRUE) geneSetDescription; the rows of the data frame are ordered by ascending geneSetPValue.

Raghavan, Nandini et al. (2007). The high-level similarity of some disparate gene expression measures, Bioinformatics, 23, 22, 3032-3038.

if (require(GO.db)){
  pathExampleGeneSet <- system.file("exampleFiles", "exampleGeneSet.rda", package = "MLP")
  pathExamplePValues <- system.file("exampleFiles", "examplePValues.rda", package = "MLP")
  load(pathExampleGeneSet)
  load(pathExamplePValues)
  head(examplePValues)
  head(exampleGeneSet)
  mlpResult <- MLP(geneSet = exampleGeneSet, geneStatistic = examplePValues)
  head(mlpResult)
}