getRPKM: Compute an RPKM matrix based on a RangedSummarizedExperiment...
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Description Usage Arguments Details Value Author(s) See Also Examples

For some analyses you might be interested in transforming the counts into RPKMs which you can do with this function.

1	getRPKM(rse, length_var = "bp_length", mapped_var = NULL)

`rse`	A RangedSummarizedExperiment-class object as downloaded with download_study.
`length_var`	A length 1 character vector with the column name from `rowData(rse)` that has the coding length. For gene level objects from recount this is `bp_length`. If `NULL`, then it will use `width(rowRanges(rse))` which should be used for exon RSEs.
`mapped_var`	A length 1 character vector with the column name from `colData(rse)` that has the number of reads mapped. For recount RSE object this would be `mapped_read_count`. If `NULL` (default) then it will use the column sums of the counts matrix. The results are different because not all mapped reads are mapped to exonic segments of the genome.

For gene RSE objects, you will want to specify the length_var because otherwise you will be adjusting for the total gene length instead of the total exonic sequence length of the gene.

A matrix with the RPKM values.

Andrew Jaffe, Leonardo Collado-Torres

scale_counts

## get RPKM matrix
rpkm <- getRPKM(rse_gene_SRP009615)

## You can also get an RPKM matrix after running scale_counts()
## with similar RPKM values
rpkm2 <- getRPKM(scale_counts(rse_gene_SRP009615))
rpkm3 <- getRPKM(scale_counts(rse_gene_SRP009615, by = "mapped_reads"))

summary(rpkm - rpkm2)
summary(rpkm - rpkm3)
summary(rpkm2 - rpkm3)