getATACABsignal returns estimated A/B compartments from ATAC-seq data.
Usage
getATACABsignal(
obj,
res = 1000000,
parallel = FALSE,
chr = NULL,
targets = NULL,
cores = 2,
bootstrap = TRUE,
num.bootstraps = 100,
genome = c("hg19", "hg38", "mm9", "mm10"),
other = NULL,
group = FALSE,
boot.parallel = FALSE,
boot.cores = 2
)
getRNAABsignal(
obj,
res = 1000000,
parallel = FALSE,
chr = NULL,
targets = NULL,
cores = 2,
bootstrap = TRUE,
num.bootstraps = 100,
genome = c("hg19", "hg38", "mm9", "mm10"),
other = NULL,
group = FALSE,
boot.parallel = FALSE,
boot.cores = 2
)Arguments
- obj
Input SummarizedExperiment object
- res
Compartment resolution in bp
- parallel
Whether to run samples in parallel
- chr
What chromosome to work on (leave as NULL to run on all chromosomes)
- targets
Samples/cells to shrink towards
- cores
How many cores to use when running samples in parallel
- bootstrap
Whether we should perform bootstrapping of inferred compartments
- num.bootstraps
How many bootstraps to run
- genome
What genome to work on ("hg19", "hg38", "mm9", "mm10")
- other
Another arbitrary genome to compute compartments on
- group
Whether to treat this as a group set of samples
- boot.parallel
Whether to run the bootstrapping in parallel
- boot.cores
How many cores to use for the bootstrapping
Examples
if (requireNamespace("csaw", quietly = TRUE)) {
data("k562_scatac_chr14", package = "compartmap")
atac_compartments <- getATACABsignal(
k562_scatac_chr14,
parallel = FALSE,
chr = "chr14",
bootstrap = FALSE,
genome = "hg19",
group = TRUE
)
}
#> Computing compartments for chr14
#> Number of means fewer than 4. Using Bayes instead of JSE.
#> 108 bins created...
#> Calculating correlations...
#> Done...
#> Calculating eigenvectors.
#> Smoothing eigenvector.
#> Done smoothing.