GeneSelector {GeneSelector} | R Documentation |
GeneRankings
and AggregatedRankings
from
several statistics are unified. According to
a user-defined or adaptively determined threshold via
multiple testing procedures, all genes are checked whether
they fall below this threshold consistenly in all
statistics used. If this criterion is not met, then the
gene is selected.
A final order of the genes is defined by the following criteria
GeneSelector(Rlist, ind = NULL, indstatistic = 1:length(Rlist), threshold = c("user", "BH", "qvalue", "Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BY"), maxrank = NULL, maxpval = 0.05)
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 (typically this is the importance of
the used statistics). For instance, if RList
consists of five elements, then
indstatistic=c(2,4,1,3,5) would give most
importance to the second statistic. |
threshold |
How the threshold is determined. Can be either "user"
(then the threshold is specified via maxrank ) or
a multiple testing procedure (s. AdjustPvalues).
In this case, the p-values of that element of
Rlist attributed most importance
(s. indtstatistic ) 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 the ones of
the second most are used (if available), and so on. |
maxrank |
Specified if threshold="user" . A positive integer
that is regarded as threshold rank. |
maxpval |
Specified if threshold is not user |
An object of class CombinedRanking.
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
GeneRanking, AggregatedRanking
## 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) ### relative distance plot plot(gkpval, top=5) ### Detailed information about gene 4 GeneInfoScreen(gkpval, which=4)