filter_pair_motifs {spatzie} | R Documentation |
Multiple hypothesis correction applied to filter for significant motif interactions.
filter_pair_motifs(interaction_data, method = "fdr", threshold = 0.05)
interaction_data |
an interactionData object of paired genomic regions |
method |
statistical method for multiple hypothesis correction,
defaults to Benjamini-Hochberg ( |
threshold |
p-value threshold for significance cut-off |
an interactionData object where obj$pair_motif_enrich
contains
multiple hypothesis corrected p-values for significance of seeing a
higher co-occurrence than what we get by chance and
obj$pair_motif_enrich_sig
contains only motifs that have at least one
significant interaction.
Jennifer Hammelman
## Not run: genome_id <- "BSgenome.Mmusculus.UCSC.mm9" if (!(genome_id %in% rownames(utils::installed.packages()))) { BiocManager::install(genome_id, update = FALSE, ask = FALSE) } genome <- BSgenome::getBSgenome(genome_id) motifs_file <- system.file("extdata/motifs_subset.txt.gz", package = "spatzie") motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM") yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome) yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4) yy1_pd_score_corr <- anchor_pair_enrich(yy1_pd_interaction, method = "score") yy1_pd_score_corr_adj <- filter_pair_motifs(yy1_pd_score_corr) ## End(Not run) res <- filter_pair_motifs(spatzie::anchor_pair_example_count, threshold = 0.5)