filter_pair_motifs: Filter significant motif interactions

In jhammelman/spatzie: Identification of enriched motif pairs from chromatin interaction data

Filter significant motif interactions

Description

Multiple hypothesis correction applied to filter for significant motif interactions.

Usage

filter_pair_motifs(interaction_data, method = "fdr", threshold = 0.05)

Arguments

interaction_data	an interactionData object of paired genomic regions
method	statistical method for multiple hypothesis correction, defaults to Benjamini-Hochberg ("fdr") (see p.adjust for options)
threshold	p-value threshold for significance cut-off

Value

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.

Author(s)

Jennifer Hammelman

Examples

## 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)

jhammelman/spatzie documentation built on Feb. 8, 2024, 8:50 a.m.