getStability

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Description

Performes stability selection on sample rankings with a given stability threshold. Selection probabilities and stability ranking are calculated.

Usage

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  getStability(sr, thr, Pi = FALSE, verbose = FALSE)

Arguments

sr

sample rankings in the form of a matrix where each row corresponds to one element and each column gives one ranking.

thr

the threshold for the stability selection, indicating above which frequency in the samples an element is considered stable.

Pi

boolean indicating if the Pi matrix should be returned (can be very large, default=FALSE).

verbose

boolean indicating whether status updates should be printed

Value

a list containing:

stabRank

the stable ranking.

Pi

the frequency matrix with all values per gene and per cutoff.

stableSetSize

a table with the number of stable genes per cutoff.

Examples

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# 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)