An example of how counts are typically filtered using the package functions.
library(CIMseq.data) library(printr)
Load the counts data. This would usually be done with the read.table function but here we use a small test dataset that represents typical input of HTSeq generated counts.
data(testingCounts, package = "CIMseq.data")
Move the gene names to rownames.
testingCounts <- moveGenesToRownames(testingCounts) testingCounts
Remove the .htseq suffix from colnames.
testingCounts <- removeHTSEQsuffix(testingCounts) testingCounts
Label samples as singlets or multiplets.
testingCounts <- labelSingletsAndMultiplets(testingCounts, LETTERS[1:5]) testingCounts
Detect non-genes reported by HTSeq.
nonGenes <- detectNonGenes(testingCounts) nonGenes testingCounts <- testingCounts[!nonGenes, ] testingCounts
Detect ERCC reads.
ercc <- detectERCCreads(testingCounts) ercc testingERCC <- testingCounts[ercc, ] testingCounts <- testingCounts[!ercc, ] testingERCC testingCounts
Detect low quality genes.
lqg <- detectLowQualityGenes(testingCounts, 18) lqg testingCounts <- testingCounts[lqg, ] testingCounts
Detect low quality cells (samples).
Low quality cell detection can also be done with detectLowQualityCells.ERCCfrac
which uses ERCC reads to detect cells with a high fraction ERCC.
lqc.totalCounts <- detectLowQualityCells.totalCounts(testingCounts, mincount = 25) lqc.housekeeping <- detectLowQualityCells.housekeeping(testingCounts, geneName = "ACTB", quantileCut = 0.01) lqc <- lqc.totalCounts & lqc.housekeeping testingCounts <- testingCounts[, lqc] testingCounts
Convert counts to a matrix.
testingCounts <- convertCountsToMatrix(testingCounts) testingCounts
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