| build_grn {WGCNA} | R Documentation |
build_grn(x, TF, opts,
bicor = NULL,
modules = NULL);
performance notes: when the bicor store is present, the candidate edges and p-values are read from the cached correlation matrix and the WGCNA module map is resolved through a three-level cache (explicit modules argument => {storeFile}.modules sidecar with fingerprint validation => fresh blockwise run with automatic cache write-back), so repeated calls with different thresholds do not re-run either the correlation computation or the WGCNA module detection.
the assembled GRNBuildResult: the prior regulatory network split by the WGCNA modules (each module holds a PriorNetwork of RegulatoryEdge edges carrying TF, target gene, regulation type, confidence and evidence tags), together with the build summary statistics. Use result.ToPriorNetwork() / result.ToPriorNetwork(minConfidence) to merge it into a single network for the downstream DBN or GNN modeling.
let opts = new("GRN_opts", minAbsCorrelation = 0.3, enableGpu = TRUE)
|> string_links(string_db = "K:\hsa_grn\string-db");
let grn = WGCNA::build_grn(hsa, TF$Ensembl, opts, bicor = bicor);