peaky_prepare_from_chicago: Wrapper that prepares CHiCAGO output for Peaky analysis
In cqgd/pky: Capture Hi-C calling algorithm

Description Usage Arguments Value Details Examples

Reads a CHiCAGO .rds object (example at https://osf.io/eaqz6/) and prepares it for post-hoc analysis with Peaky. This setup fine-maps chromatin interactions based on CHiCAGO scores, instead of based on the adjusted readcounts that Peaky's own model (see interpret_peaky_fs for a full pipeline) would generate from raw Capture Hi-C or Capture-C counts. The next step of this pipeline is to process the generated files with peaky_run.

peaky_prepare_from_chicago(
  chicago_rds_path,
  peaky_output_dir,
  chicago_max_dist = 1e+06,
  chicago_bait_subset = NA,
  subsample_size = 10000
)

`chicago_rds_path`	Path to the .rds file produced by CHiCAGO.
`peaky_output_dir`	Directory to store Peaky's intermediate files and results in. Will be created if it doesn't exist.
`chicago_max_dist`	Maximum distance putative interactions may span if they are to be extracted and analyzed.
`chicago_bait_subset`	Path to a file specifying baitIDs to extract from the CHiCAGO object. This file just needs one column name: baitID. By default, all bais will be extracted.
`subsample_size`	Number of putative interactions to build a null model from that relates CHiCAGO scores to count data. Used for all distance bins. See also `model_bin_fs`.

List containing the output directory where baits are stored and their individual paths.

This function exports CHiCAGO-made bins (analogous to bin_interactions_fs in Peaky's standard pipeline), uses a modified version of model_bin_fs where only CHiCAGO scores are used, and ultimately calls split_baits_fs.

base = system.file("extdata",package="peaky")
chicago_rds_path = paste0(base,"/chicago_output.rds")
peaky_output_dir = paste0(base,"/peaky_from_chicago")
## Not run: 
peaky_prepare_from_chicago(chicago_rds_path, peaky_output_dir, subsample_size=NA) 
#Big dataset? Consider subsample_size=10e3 for speed.

for(i in 1:3){ peaky_run(peaky_output_dir,i) }
#Tip: run this in parallel on a cluster by scheduling an array job and passing its elements to i.

peaky_wrapup(peaky_output_dir)

## End(Not run)