compute_read_counts {recount3} | R Documentation |
As described in the recount workflow, the counts provided by the recount2
project are base-pair counts. You can scale them using transform_counts()
or compute the read counts using the area under coverage information (AUC).
compute_read_counts( rse, round = TRUE, avg_mapped_read_length = "recount_qc.star.average_mapped_length" )
rse |
A
RangedSummarizedExperiment-class
created by |
round |
A |
avg_mapped_read_length |
A |
This function is similar to
recount::read_counts(use_paired_end = TRUE, round = TRUE)
but more general
and with a different name to avoid NAMESPACE conflicts. Note that the default
value of round
is different than in recount::read_counts()
. This
was done to match the default value of round
in transform_counts()
.
A matrix()
with the read counts. By default this function uses
the average read length to the QC annotation.
Collado-Torres L, Nellore A and Jaffe AE. recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor version 1; referees: 1 approved, 2 approved with reservations. F1000Research 2017, 6:1558 doi: 10.12688/f1000research.12223.1.
Other count transformation functions:
compute_scale_factors()
,
is_paired_end()
,
transform_counts()
## Create a RSE object at the gene level rse_gene_SRP009615 <- create_rse_manual("SRP009615") colSums(compute_read_counts(rse_gene_SRP009615)) / 1e6 ## Create a RSE object at the gene level rse_gene_DRP000499 <- create_rse_manual("DRP000499") colSums(compute_read_counts(rse_gene_DRP000499)) / 1e6 ## You can compare the read counts against those from recount::read_counts() ## from the recount2 project which used a different RNA-seq aligner ## If needed, install recount, the R/Bioconductor package for recount2: # BiocManager::install("recount") recount2_readsums <- colSums(assay(recount::read_counts( recount::rse_gene_SRP009615 ), "counts")) / 1e6 recount3_readsums <- colSums(compute_read_counts(rse_gene_SRP009615)) / 1e6 recount_readsums <- data.frame( recount2 = recount2_readsums[order(names(recount2_readsums))], recount3 = recount3_readsums[order(names(recount3_readsums))] ) plot(recount2 ~ recount3, data = recount_readsums) abline(a = 0, b = 1, col = "purple", lwd = 2, lty = 2) ## Repeat for DRP000499, a paired-end study recount::download_study("DRP000499", outdir = tempdir()) load(file.path(tempdir(), "rse_gene.Rdata"), verbose = TRUE) recount2_readsums <- colSums(assay(recount::read_counts( rse_gene ), "counts")) / 1e6 recount3_readsums <- colSums(compute_read_counts(rse_gene_DRP000499)) / 1e6 recount_readsums <- data.frame( recount2 = recount2_readsums[order(names(recount2_readsums))], recount3 = recount3_readsums[order(names(recount3_readsums))] ) plot(recount2 ~ recount3, data = recount_readsums) abline(a = 0, b = 1, col = "purple", lwd = 2, lty = 2)