miRNAtap (version 1.6.0)

aggregateRanks: Aggreagate ranks from multiple sources with various methods

Description

This function performs aggregation phase of target prediction for getPredictedTargets. Consensus ranking is derived from multiple individual rankings. Available methods include minimum, maximum and geometric mean with further tuning parameters which promote true positives at the top of the final ranking

Usage

aggregateRanks(ranks, n_valid_srcs, min_src, method = "geom", promote = TRUE)

Arguments

ranks

data.frame with ordered scores

n_valid_srcs

number of valid sources in the dataset

min_src

minimum acceptable number fo sources

method

'min','max', or 'geom', default 'geom'

promote

add weights to improve accuracy of the method, default TRUE

Value

data.frame object with ranks per source and aggregate ranks

Examples

data = data.frame(GeneID=c("15364", "56520", "57781", "58180", "18035"),
                source1scores=c(0.9,0.5,0.3,NA,NA),
                source2scores=c(0.7,NA,0.8,0.6,0.5),
                source3scores=c(0.5,NA,0.3,0.1,0.2))
data #dataframe with scores
aggregateRanks(data, n_valid_srcs=3, min_src=2, method='geom')
#note how gene 56520 is eliminated as it appeared in fewer than 2 sources