Description Usage Arguments Details Value Author(s) References See Also Examples
As described in the recount workflow, the counts provided by the recount2 project are base-pair counts. You can scale them using scale_counts or compute the read counts using the area under coverage information (AUC). We use the AUC because Rail-RNA soft clips some reads.
1 | read_counts(rse, use_paired_end = TRUE, round = FALSE)
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rse |
A RangedSummarizedExperiment-class object as downloaded with download_study. |
use_paired_end |
A logical vector. When |
round |
Whether to round the counts to integers or not. |
Check the recount workflow for details about the counts provided by the recount2 project. Note that this function should not be used after scale_counts or it will return non-sensical results.
Returns a RangedSummarizedExperiment-class object with the read counts.
Leonardo Collado-Torres
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.
scale_counts
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Difference between read counts and reads downloaded by Rail-RNA
colSums(assays(read_counts(rse_gene_SRP009615))$counts) / 1e6 -
colData(rse_gene_SRP009615)$reads_downloaded / 1e6
## Paired-end read counts vs fragment counts (single-end counts)
download_study("DRP000499")
load("DRP000499/rse_gene.Rdata")
colSums(assays(read_counts(rse_gene, use_paired_end = FALSE))$counts) /
colSums(assays(read_counts(rse_gene))$counts)
## Difference between paired-end read counts vs paired-end reads downloaded
colSums(assays(read_counts(rse_gene))$counts) / 1e6 -
colData(rse_gene)$reads_downloaded / 1e6 / 2
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