create_rse: Create a recount3 RangedSummarizedExperiment gene or exon...

In LieberInstitute/recount3: Explore and download data from the recount3 project

Create a recount3 RangedSummarizedExperiment gene or exon object

Description

Once you have identified a project you want to work with, you can use this function to construct a recount3 RangedSummarizedExperiment-class (RSE) object at the gene or exon expression feature level. This function will retrieve the data, cache it, then assemble the RSE object.

Usage

create_rse(
  project_info,
  type = c("gene", "exon", "jxn"),
  annotation = annotation_options(project_info$organism),
  bfc = recount3_cache(),
  jxn_format = c("ALL", "UNIQUE"),
  recount3_url = getOption("recount3_url", "http://duffel.rail.bio/recount3"),
  verbose = getOption("recount3_verbose", TRUE)
)

Arguments

project_info	A data.frame() with one row that contains the information for the project you are interested in. You can find which project to work on using available_projects().
type	A character(1) specifying whether you want to access gene, exon, or exon-exon junction counts.
annotation	A character(1) specifying which annotation you want to download. Only used when type is either gene or exon.
bfc	A BiocFileCache-class object where the files will be cached to, typically created by recount3_cache().
jxn_format	A character(1) specifying whether the exon-exon junction files are derived from all the reads (ALL) or only the uniquely mapping read counts (UNIQUE). Note that UNIQUE is only available for some projects: GTEx and TCGA for human.
recount3_url	A character(1) specifying the home URL for recount3 or a local directory where you have mirrored recount3. Defaults to the load balancer http://duffel.rail.bio/recount3, but can also be https://recount-opendata.s3.amazonaws.com/recount3/release from https://registry.opendata.aws/recount/ or SciServer datascope from IDIES at JHU https://sciserver.org/public-data/recount3/data. You can set the R option recount3_url (for example in your .Rprofile) if you have a favorite mirror.
verbose	A logical(1) indicating whether to show messages with updates.

Value

A RangedSummarizedExperiment-class object.

Examples

## Find all available human projects
human_projects <- available_projects()

## Find the project you are interested in
proj_info <- subset(
    human_projects,
    project == "SRP009615" & project_type == "data_sources"
)

## Create a RSE object at the gene level
rse_gene_SRP009615 <- create_rse(proj_info)

## Explore the resulting RSE gene object
rse_gene_SRP009615

## Information about how this RSE object was made
metadata(rse_gene_SRP009615)

## Number of genes by number of samples
dim(rse_gene_SRP009615)

## Information about the genes
rowRanges(rse_gene_SRP009615)

## Sample metadata
colnames(colData(rse_gene_SRP009615))

## Check how much memory this RSE object uses
pryr::object_size(rse_gene_SRP009615)

## Create an RSE object using gencode_v29 instead of gencode_v26
rse_gene_SRP009615_gencode_v29 <- create_rse(
    proj_info,
    annotation = "gencode_v29",
    verbose = FALSE
)
rowRanges(rse_gene_SRP009615_gencode_v29)

## Create an RSE object using FANTOM6_CAT instead of gencode_v26
rse_gene_SRP009615_fantom6_cat <- create_rse(
    proj_info,
    annotation = "fantom6_cat"
)
rowRanges(rse_gene_SRP009615_fantom6_cat)

## Create an RSE object using RefSeq instead of gencode_v26
rse_gene_SRP009615_refseq <- create_rse(
    proj_info,
    annotation = "refseq"
)
rowRanges(rse_gene_SRP009615_refseq)

## Create an RSE object using ERCC instead of gencode_v26
rse_gene_SRP009615_ercc <- create_rse(
    proj_info,
    annotation = "ercc"
)
rowRanges(rse_gene_SRP009615_ercc)

## Create an RSE object using SIRV instead of gencode_v26
rse_gene_SRP009615_sirv <- create_rse(
    proj_info,
    annotation = "sirv"
)
rowRanges(rse_gene_SRP009615_sirv)

## Obtain a list of RSE objects for all gene annotations
rses_gene <- lapply(annotation_options(), function(x) {
    create_rse(proj_info, type = "gene", annotation = x)
})
names(rses_gene) <- annotation_options()
rses_gene

## Create a RSE object at the exon level
rse_exon_SRP009615 <- create_rse(
    proj_info,
    type = "exon"
)

## Explore the resulting RSE exon object
rse_exon_SRP009615

dim(rse_exon_SRP009615)
rowRanges(rse_exon_SRP009615)
pryr::object_size(rse_exon_SRP009615)

## Create a RSE object at the exon-exon junction level
rse_jxn_SRP009615 <- create_rse(
    proj_info,
    type = "jxn"
)

## Explore the resulting RSE exon-exon junctions object
rse_jxn_SRP009615

dim(rse_jxn_SRP009615)
rowRanges(rse_jxn_SRP009615)
pryr::object_size(rse_jxn_SRP009615)

## Obtain a list of RSE objects for all exon annotations
## Not run: 
rses_exon <- lapply(annotation_options(), function(x) {
    create_rse(proj_info, type = "exon", annotation = x, verbose = FALSE)
})
names(rses_exon) <- annotation_options()

## End(Not run)

LieberInstitute/recount3 documentation built on Dec. 11, 2024, 8:35 p.m.