Description Usage Arguments Value Parallel processing
View source: R/overlap_score_summary_grid.R
Calculate and Summarize TopDom Overlap Scores Across Chromosomes, Bin Sizes, and Fractions
1 2 3 4 5 6 7 8 9 10 11 12 |
dataset |
(character string) The name of the data set. |
chromosomes |
(character vector) Chromosomes to process. |
bin_sizes |
(numeric vector) The set of bin sizes (in bps) to process. |
rhos, reference_rhos |
(numeric vector) The set of fractions (in (0,0.5]) to process. |
window_size |
(integer) The TopDom windows size.
Argument passed as |
nsamples |
(integer) Number of random samples for produce. |
weights |
(character string) A character string specifying how overlap
scores across domains should be weighted.
Argument passed as is to |
domain_length |
(optional; character string or numeric vector of length two)
If specified, controls how to filter out too short or too long TopDom domains.
Argument passed as is to |
verbose |
(logical) If |
A three-dimensional character array of pathname names where the first
dimension specify chromosomes
, the second bin_sizes
, and the third rhos
(fractions).
The future framework is used to parallelize in three layers:
across (chromosome, bin size, fraction)
overlap_scores_partitions()
:
across a single chromosome (already subsetted above)
across nsamples
random samples
An example of a future::plan()
setup for parallelization on the
local machine is:
plan(list( chr_bin_rho = sequential, ## across (chr, bin_size, rho) mono_chr = sequential, ## always a single chromosome samples = multisession ## across 1:nsamples ))
Another is,
plan(list( chr_bin_rho = multisession, ## across (chr, bin_size, rho) mono_chr = sequential, ## always a single chromosome samples = sequential ## across 1:nsamples ))
For parallelization on a HPC cluster via a scheduler,
hpc_scheduler <- tweak(future.batchtools::batchtools_torque, resources = list(nodes="1:ppn=8", vmem="32gb")) plan(list( chr_bin_rho = hpc_scheduler, mono_chr = sequential, samples = multisession ))
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