Extension of the RangedSummarizedExperiment to store the output of different methods intended for the same purpose in a given dataset. For example, a differential expression analysis could be done using limma-voom, edgeR and DESeq2. The SummarizedBenchmark class provides a framework that is useful to store, benckmark and compare results.

Slots

performanceMetrics

A SimpleList of the same length as the number of assays containing performance functions to be compared with the ground truths.

BenchDesign

A BenchDesign originally used to generate the results in the object.

Examples

## loading the example data from iCOBRA library(iCOBRA)
#> #> Attaching package: ‘iCOBRA’
#> The following objects are masked from ‘package:GenomicRanges’: #> #> score, score<-
#> The following objects are masked from ‘package:BiocGenerics’: #> #> score, score<-
data(cobradata_example) ## a bit of data wrangling and reformatting assays <- list( qvalue=cobradata_example@padj, logFC=cobradata_example@score ) assays[["qvalue"]]$DESeq2 <- p.adjust(cobradata_example@pval$DESeq2, method="BH") groundTruth <- DataFrame( cobradata_example@truth[,c("status", "logFC")] ) colnames(groundTruth) <- names( assays ) colData <- DataFrame( method=colnames(assays[[1]]) ) groundTruth <- groundTruth[rownames(assays[[1]]),] ## constructing a SummarizedBenchmark object sb <- SummarizedBenchmark( assays=assays, colData=colData, groundTruth=groundTruth )