compute_scale_factors {recount3} | R Documentation |
This function computes the count scaling factors used by
transform_counts()
. This function is similar to
recount::scale_counts(factor_only = TRUE)
, but it is more general.
compute_scale_factors( x, by = c("auc", "mapped_reads"), targetSize = 4e+07, L = 100, auc = "recount_qc.bc_auc.all_reads_all_bases", avg_mapped_read_length = "recount_qc.star.average_mapped_length", mapped_reads = "recount_qc.star.all_mapped_reads", paired_end = is_paired_end(x, avg_mapped_read_length) )
x |
Either a
RangedSummarizedExperiment-class
created by |
by |
Either |
targetSize |
A |
L |
A |
auc |
A |
avg_mapped_read_length |
A |
mapped_reads |
A |
paired_end |
A |
A numeric()
with the sample scale factors that are used by
transform_counts()
.
Other count transformation functions:
compute_read_counts()
,
is_paired_end()
,
transform_counts()
## Download the metadata for SRP009615, a single-end study SRP009615_meta <- read_metadata( metadata_files = file_retrieve( locate_url( "SRP009615", "data_sources/sra", ) ) ) ## Compute the scaling factors compute_scale_factors(SRP009615_meta, by = "auc") compute_scale_factors(SRP009615_meta, by = "mapped_reads") ## Download the metadata for DRP000499, a paired-end study DRP000499_meta <- read_metadata( metadata_files = file_retrieve( locate_url( "DRP000499", "data_sources/sra", ) ) ) ## Compute the scaling factors compute_scale_factors(DRP000499_meta, by = "auc") compute_scale_factors(DRP000499_meta, by = "mapped_reads") ## You can compare the factors against those from recount::scale_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_factors <- recount::scale_counts( recount::rse_gene_SRP009615, by = "auc", factor_only = TRUE ) recount3_factors <- compute_scale_factors(SRP009615_meta, by = "auc") recount_factors <- data.frame( recount2 = recount2_factors[order(names(recount2_factors))], recount3 = recount3_factors[order(names(recount3_factors))] ) plot(recount2 ~ recount3, data = recount_factors) abline(a = 0, b = 1, col = "purple", lwd = 2, lty = 2)