mwwExtGST: Competitive Regulon Test
In miccec/yaGST: Competitive gene set and regulon tests.

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Run a competitive test to highlight whether a regulon, with postive and negative gene associated with a transcription factor, is highly ranked in a sequence of gene values.

1	mwwExtGST(rankedList, geneSetUp, geneSetDown, minLenGeneSet = 15, moreDetails = FALSE, verbose = TRUE)

`rankedList`	numeric vector of data values where the names are the genes names
`geneSetUp`	a character list of genes having a positive association with the transcription factor.
`geneSetDown`	a character list of genes having a negative association with the transcription factor.
`minLenGeneSet`	minimum dimension of the pooled geneSet
`moreDetails`	a logical indicating whether the output includes the rankedList (necessary to plot the enrichment)
`verbose`	a logical indicating to suppress or not the messages; it's TRUE by default.

The rankedList has to be a named sequence of values where the genes associated with the phenotype are positvive values, while those not associated are negative. This is necessary because the doubledRankedList is set as c(rankedList, -rankedList).

`call`	a character string of the call of the function.
`alternative`	a character string describing the alternative hypothesis.
`originalGeneSetCount`	the length of the pooled positive and negative gene-sets.
`geneSetUp`	the same character list given in input.
`geneSetDown`	the same character list given in input.
`actualGeneSet`	the list of pooled positive and negative genes as comes from the intesection between the gene-set and the ranked-list.
`actualGeneSetCount`	the length of the `actualGeneSet`.
`doubleRankedList`	the doubled ranked-list given in input; this slot is filled whether moreDetails is TRUE. Seeq details
`lengthOfRankedList`	the length of the ranked-list given in input
`statistic`	the value of the Mann-Whitney-Wilcox test statistic.
`nes`	the value of the normalized enrichment score.
`pu`	is the probability unbalance, i.e. the ratio of nes to 1-nes.
`log.pu`	the log2 transformation of the pu.
`p.value`	the p-value for the test.

This function adatpts the enrichment analysis methodology from Lim at el. (2009) to the mwwGST function.

Stefano M. Pagnotta

Lim, W. K., Lyashenko, E. and Califano, A. - Master regulators used as breast cancer metastasis classifier. - Pac Symp Biocomput, 504-515 (2009))

rankedList, mwwGST

  library(yaGST)
  data("rankedList")
  positive_gs <- sample(head(names(rankedList), 10000), 200)
  negative_gs <- sample(tail(names(rankedList), 10000), 200)
  ans <- mwwExtGST(rankedList, positive_gs, negative_gs, moreDetails = TRUE)
  ans
  plot(ans)