View source: R/transform_counts.R
compute_read_counts | 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)
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