FPKM is short for "Fragments Per Kilobase of transcript per Million fragments in library". When calculating RiboSeq data FPKM over ORFs, use ORFs as 'grl'. When calculating RNASeq data FPKM, use full transcripts as 'grl'. It is equal to RPKM given that you do not have paired end reads.
fpkm(grl, reads, pseudoCount = 0, librarySize = "full", weight = 1L)
an integer, by default is 0, set it to 1 if you want to avoid NA and inf values.
either numeric value or character vector. Default ("full"), number of alignments in library (reads). If you just have a subset, you can give the value by librarySize = length(wholeLib), if you want lib size to be only number of reads overlapping grl, do: librarySize = "overlapping" sum(countOverlaps(reads, grl) > 0), if reads has 3 hits in grl, and reads has 2 hits, librarySize will be 2, not 5. You can also get the inverse overlap, if you want lib size to be total number of overlaps, do: librarySize = "DESeq" This is standard fpkm way of DESeq2::fpkm(robust = FALSE) sum(countOverlaps(grl, reads)) if grl has 3 reads and grl has 2 reads, librarySize is 5, not 2.
a vector (default: 1L, if 1L it is identical to countOverlaps()), if single number (!= 1), it applies for all, if more than one must be equal size of 'reads'. else it must be the string name of a defined meta column in subject "reads", that gives number of times a read was found. GRanges("chr1", 1, "+", score = 5), would mean "score" column tells that this alignment region was found 5 times.
Note also that you must consider if you will use the whole read library or just the reads overlapping 'grl' for library size. A normal question here is, does it make sense to include rRNA in library size ? If you only want overlapping grl, do: librarySize = "overlapping"
a numeric vector with the fpkm values
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ORF <- GRanges(seqnames = "1", ranges = IRanges(start = c(1, 10, 20), end = c(5, 15, 25)), strand = "+") grl <- GRangesList(tx1_1 = ORF) RFP <- GRanges("1", IRanges(25, 25),"+") fpkm(grl, RFP) # With weights (10 reads at position 25) RFP <- GRanges("1", IRanges(25, 25),"+", score = 10) fpkm(grl, RFP, weight = "score")
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