With null distributions estimated using the generateNull()
function, p-values are estimated using a one-tailed test. A minimum p-value
of 1/B can be achieved with B permutations.
getPvals(permuteResult, scoredf, subSamples = NULL)A matrix, null distributions for each sample generated
using the generateNull() function
A dataframe, the scored results of samples under test
generated using the simpleScore() function
A vector of sample labels/indices that will be used to subset the score matrix. All samples will be scored if not provided
Estimated p-values for enrichment of the signature in each sample. A p-value of 1/B indicates that the estimated p-value is less than or equal to 1/B.
ranked <- rankGenes(toy_expr_se)
scoredf <- simpleScore(ranked, upSet = toy_gs_up, downSet = toy_gs_dn)
# find out what backends can be registered on your machine
BiocParallel::registered()
#> $MulticoreParam
#> class: MulticoreParam
#> bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
#> bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
#> bpRNGseed: 1; bptimeout: NA; bpprogressbar: FALSE
#> bpexportglobals: TRUE; bpexportvariables: FALSE; bpforceGC: FALSE
#> bpfallback: TRUE
#> bplogdir: NA
#> bpresultdir: NA
#> cluster type: FORK
#>
#> $SnowParam
#> class: SnowParam
#> bpisup: FALSE; bpnworkers: 4; bptasks: 0; bpjobname: BPJOB
#> bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
#> bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
#> bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE
#> bpfallback: TRUE
#> bplogdir: NA
#> bpresultdir: NA
#> cluster type: SOCK
#>
#> $SerialParam
#> class: SerialParam
#> bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
#> bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
#> bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
#> bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
#> bpfallback: FALSE
#> bplogdir: NA
#> bpresultdir: NA
#>
# the first one is the default backend, and it can be changed explicitly.
# See vignette for more details
permuteResult = generateNull(upSet = toy_gs_up, downSet = toy_gs_dn, ranked,
B =10, seed = 1, useBPPARAM = NULL)
# call the permutation function to generate the empirical scores
# for B times.
pvals <- getPvals(permuteResult,scoredf)