Usage
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)
Arguments
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.