GeneSelector (version 2.22.0)

GeneSelector: Select promising candidate genes

Description

Given GeneRankings or AggregatedRankings obtained from several ranking procedures, the aim is to find is a unifying output. A threshold equal to the maximum rank/list position which is still relevant for the question of interest may be provided by the user, or the threshold can adaptively be determined via significance analysis in multiple testing procedures. Then, all genes are checked whether their ranks fall below this threshold consistenly in all ranking procedures used. If this holds, then the gene is selected. A final order of the genes is defined by the following criteria

1.

A user-defined ranking of the used ranking procedures, i.e. the user decides which statistic he or she considers most important.

2.

'Selection', i.e. falling below the threshold.

3.

The obtained ranks. The rank from the most important ranking procedure is considered, then that from the second most important, and so on.

Usage

GeneSelector(Rlist, ind = NULL, indstatistic = 1:length(Rlist),  threshold = c("user", "BH", "qvalue", "Bonferroni", "Holm",  "Hochberg", "SidakSS", "SidakSD", "BY"),  maxrank = NULL, maxpval = 0.05)

Arguments

Rlist

A list of objects of class RepeatedRanking or AggregatedRanking, all based on the same data.

ind

Indices of genes to be considered. Defaults to all.

indstatistic

An index vector defining the importance of the elements of Rlist. For instance, if RList consists of five elements, then indstatistic=c(2,4,1,3,5) would give most importance to the second element.

threshold

Determination of the threshold (s. description). Can be either "user", in which case the threshold is specified via maxrank, or an acronym for one of the following multiple testing procedures (s. help file for mt.rawp2adjp in the package multtest for detailed information and references):

"BH"

Benjamini-Hochberg procedure.

"qvalue"

The q-value of Storey and Tibshirani (2003): "Statistical significance for genomewide studies". PNAS of the USA, 100, 9440-9445.

"Bonferroni"

Bonferroni procedure.

"Holm"

Holm procedure.

"SidakSS"

Sidak single-step procedure.

"SidakSD"

Sidak step-down procedure.

"BY"

Benjamini-Yekutieli procedure.

In the latter case, the p-values of the element of Rlist attributed most importance (s. indstatistic) are adjusted and the number of p-values falling below maxpval is used as threshold rank. If the most important statistic provides no p-values, then those of the second most are used (if available), and so on.

maxrank

A positive integer specifying a user-defined threshold.

maxpval

Specified if threshold is not user.

Value

GeneSelectorOutput.

See Also

GeneRanking, AggregatedRanking

Examples

## Load toy gene expression data
data(toydata)
### class labels
yy <- toydata[1,]
### gene expression
xx <- toydata[-1,]
### Get Rankings from five different statistics
ordinaryT <- RankingTstat(xx, yy, type="unpaired")
baldilongT <- RankingBaldiLong(xx, yy, type="unpaired")
samT <- RankingSam(xx, yy, type="unpaired")
wilc <- RankingWilcoxon(xx, yy, type="unpaired")
wilcebam <- RankingWilcEbam(xx, yy, type="unpaired")
### form a list
LL <- list(ordinaryT, baldilongT, samT, wilc, wilcebam)
### order statistics (assign importance)
ordstat <-  c(3,4,2,1,5)
### start GeneSelector, threshold set to rank 50
gk50 <- GeneSelector(LL, indstatistic=ordstat, maxrank=50)
### start GeneSelector, using adaptive threshold based on p-values,
### here using the multiple testing procedure of Hochberg
gkpval <- GeneSelector(LL, indstatistic=ordstat, threshold = "BH", maxpval=0.05)
### show results
show(gkpval)
str(gkpval)
toplist(gkpval)
### which genes have been selected ?
SelectedGenes(gkpval)
### Detailed information about gene 4
plot(gkpval, which=4)