runRSA {staRank} | R Documentation |
Performs RSA analysis from within R. It does exactly the same as the version from BinZhou 2007 but data are parsed from within R.
runRSA(t, opts)
t |
a data.frame in RSA format (can be created with the function dataFormatRSA). |
opts |
the options for the RSA ranking. This is a list of: LB: lower_bound (defaults = 0), UB: upper bound (defaults = 1) reverse: boolean (if TRUE: reverse hit picking, higher scores are better, default = FALSE). |
a matrix containing the RSA analysis results. The gene wise LogP-value is considered for further ranking analysis.
# generate dataset d<-replicate(4,sample(1:10,10,replace=FALSE)) rownames(d)<-letters[1:10] # rank aggregation on the dataset using two base methods aggregRank(d, method='mean') aggregRank(d, method='median') # calculate summary statistic from the data summaryStats(d, method='mean') summaryStats(d, method='RSA') # calculating replicate scores from different summary statistics scores<-getSampleScores(d,'mean',decreasing=FALSE,bootstrap=TRUE) scores<-getSampleScores(d,'mwtest',decreasing=FALSE,bootstrap=TRUE) # perform RSA analysis # get RSA format of data rsaData<-dataFormatRSA(d) # set RSA options opts<-list(LB=min(d),UB=max(d),reverse=FALSE) # run the RSA analysis r<-runRSA(rsaData,opts) # directly obtain the per gene RSA ranking from the data r<-uniqueRSARanking(rsaData,opts) # get stable Ranking, stable setsizes and the Pi matrix for default settings # and stability threshold of 0.9 s<-getStability(d,0.9) # run default stability ranking s<-stabilityRanking(d) # using an accessor function on the RankSummary object stabRank(s) # summarize a RankSummary object summary(s) # generate a rank matrix from a RankSummary object getRankmatrix(s)