subsetByComposition {CAGEfightR} | R Documentation |
A convenient wrapper around calcComposition and subset.
subsetByComposition(object, inputAssay = "counts", outputColumn = "composition", unexpressed = 0.1, genes = "geneID", minSamples = 1)
object |
RangedSummarizedExperiment: CAGE data quantified at CTSS, cluster or gene-level. |
inputAssay |
character: Name of assay holding input expression values. |
outputColumn |
character: Name of column in rowRanges to hold composition values. |
unexpressed |
numeric: Composition will be calculated based on features larger than this cutoff. |
genes |
character: Name of column in rowData holding genes (NAs are not allowed.) |
minSamples |
numeric: Only features with composition in more than this number of samples will be kept. |
RangedSummarizedExperiment with composition values added as a column in rowData and features with less composition than minSamples removed.
Other Subsetting functions: subsetByBidirectionality
,
subsetBySupport
Other Calculation functions: calcBidirectionality
,
calcComposition
, calcPooled
,
calcShape
, calcSupport
,
calcTPM
, calcTotalTags
,
subsetByBidirectionality
,
subsetBySupport
data(exampleUnidirectional) # Annotate clusters with geneIDs: library(TxDb.Mmusculus.UCSC.mm9.knownGene) txdb <- TxDb.Mmusculus.UCSC.mm9.knownGene exampleUnidirectional <- assignGeneID(exampleUnidirectional, geneModels=txdb, outputColumn='geneID') exampleUnidirectional <- subset(exampleUnidirectional, !is.na(geneID)) # Keep only clusters more than 10% in more than one sample: calcComposition(exampleUnidirectional) # Keep only clusters more than 5% in more than 2 samples: subsetByComposition(exampleUnidirectional, unexpressed = 0.05, minSamples=2)