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Bootstrap the AUC and conduct T-Tests for a collection of signatures.

Source: R/bootstrap.R
bootstrapAUC.Rd

Run bootstrapping of the AUC and derive the p-value for a 2-sample t-test for all signatures tested on a given dataset.

Usage

bootstrapAUC(
  SE_scored,
  annotationColName,
  signatureColNames,
  num.boot = 100,
  pb.show = TRUE
)

Arguments

SE_scored

a SummarizedExperiment object with genes as the row features and signature scores in the colData. There should also be a column of annotation data. Required.

annotationColName

a character string giving the column name in colData that contains the annotation data. Required.

signatureColNames

a vector of column names in the colData that contain the signature score data. Required.

num.boot

integer. The number of times to bootstrap the data. The default is 100.

pb.show

logical for whether to show a progress bar while running code. The default is TRUE.

Value

A list of length 5 returning a vector of p-values for a 2-sample t-test, bootstrapped AUC values, an AUC value for using all scored values for all signatures specified in signatureColNames, and values for the lower and upper bounds of a bootstrapped AUC confidence interval using pROC::roc().

Examples

 # Run signature profiling
 choose_sigs <- list("madeupsig" = c("FCRL3", "OAS2", "IFITM3"))
 prof_indian <- runTBsigProfiler(TB_indian, useAssay = "logcounts",
                                 algorithm = "ssGSEA",
                                 combineSigAndAlgorithm = TRUE,
                                 signatures = choose_sigs,
                                 parallel.sz = 1)
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
#>     union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
#> Parameter update_genes is TRUE. Gene names will be updated.
#> Running ssGSEA
#> Warning: 1 genes with constant values throughout the samples.
#> [1] "Calculating ranks..."
#> [1] "Calculating absolute values from ranks..."
#> [1] "Normalizing..."
 # Bootstrapping
 booted <- bootstrapAUC(SE_scored = prof_indian, annotationColName = "label",
                        signatureColNames = names(choose_sigs), num.boot = 2)
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
 booted
#> $`P-values`
#> [1] 2.458101e-05
#> 
#> $`Boot AUC Values`
#>   madeupsig
#> 1 0.7284211
#> 2 0.8636364
#> 
#> $`Non-Boot AUC Values`
#> [1] 0.8484211
#> 
#> $`pROC Lower`
#> [1] 0.7347
#> 
#> $`pROC Upper`
#> [1] 0.9621
#>