peaky_prepare_from_chicago.RdReads 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 |
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") if (FALSE) { 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) }