r i
. r names(resAnchor)
{#r resAnchor
}The results were then visualized with the plotEnrichment()
function:
plts_verdsplit <- plotEnrichment(object, pred_POS_only = FALSE, label_POS_only = FALSE, splitby = "prediction") resRmd <- lapply( names(plts_verdsplit), function(db) { knitr::knit_child(text = c( '#### `r db`', '', '```r', 'plts_verdsplit[[db]]', '```', '' ), envir = environment(), quiet = TRUE) } ) cat(unlist(resRmd), sep = '\n')
Note: If < 200 peaks in user-supplied sample, r knitr::asis_output("\u25C7")
will be missing from plots.
Additional Details
Annotations were derived from a variety of sources and accessed using RLHub (unless custom annotations were supplied by the user). Detailed explanations of each database and type can be found here{target="_blank"}. The valr
{target="_blank"} R package was implemented to test the enrichment of these features within the supplied ranges for r object@metadata$sampleName
. For additional detail, please refer to the RLSeq::featureEnrich
documentation (link{target="_blank"}).
featRes <- rlresult(object, resultName = "featureEnrichment") spec_now <- paste0( "featureEnrichment_", gsub(object@metadata$sampleName, pattern = " ", replacement = "_") ) featRes %>% DT::datatable( options = list( dom = "Bfrtip", scrollX = TRUE, pageLength = 6, buttons = list( extend = 'collection', buttons = list( list(extend='csv', filename=spec_now), list(extend='excel', filename=spec_now) ), text = 'Download' ) ), colnames = c( "Database", "Annotation", "# tested peaks", "# total peaks", "# tested annotation ranges", "# total annotation ranges", "Mean relative distance to feature", "Mean relative distance to feature (shuffled)", "Pval of relative distance", "Fisher Statistic", "Fisher Statistic (shuffled)", "Fisher pval", "Fisher pval (shuffled)" ) )
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