scale_counts: Scale the raw counts provided by the recount project
In recount: Explore and download data from the recount project

Description Usage Arguments Details Value Author(s) See Also Examples

In preparation for a differential expression analysis, you will have to choose how to scale the raw counts provided by the recount project. Note that the raw counts are the sum of the base level coverage so you have to take into account the read length or simply the total coverage for the given sample (default option). You might want to do some further scaling to take into account the gene or exon lengths. If you prefer to calculate read counts without scaling check the function read_counts.

scale_counts(
  rse,
  by = "auc",
  targetSize = 4e+07,
  L = 100,
  factor_only = FALSE,
  round = TRUE
)

`rse`	A RangedSummarizedExperiment-class object as downloaded with download_study.
`by`	Either `auc` or `mapped_reads`. If set to `auc` it will scale the counts by the total coverage of the sample. That is, the area under the curve (AUC) of the coverage. If set to `mapped_reads` it will scale the counts by the number of mapped reads, whether the library was paired-end or not, and the desired read length (`L`).
`targetSize`	The target library size in number of single end reads.
`L`	The target read length. Only used when `by = 'mapped_reads'` since it cancels out in the calculation when using `by = 'auc'`.
`factor_only`	Whether to only return the numeric scaling factor or to return a RangedSummarizedExperiment-class object with the counts scaled. If set to `TRUE`, you have to multiply the sample counts by this scaling factor.
`round`	Whether to round the counts to integers or not.

Rail-RNA http://rail.bio uses soft clipping when aligning which is why we recommed using by = 'auc'.

If the reads are from a paired-end library, then the avg_read_length is the average fragment length. This is taken into account when using by = 'mapped_reads'.

If factor_only = TRUE it returns a numeric vector with the scaling factor for each sample. If factor_only = FALSE it returns a RangedSummarizedExperiment-class object with the counts already scaled.

Leonardo Collado-Torres

download_study, read_counts

## Load an example rse_gene object
rse_gene <- rse_gene_SRP009615

## Scale counts
rse <- scale_counts(rse_gene)

## Find the project used as an example
project_info <- abstract_search("GSE32465")

## See some summary information for this project
project_info

## Use the following code to re-download this file
## Not run: 
## Download
download_study(project_info$project)

## Load file
load(file.path(project_info$project, "rse_gene.Rdata"))
identical(rse_gene, rse_gene_SRP009615)

## End(Not run)