filter_pair_motifs {spatzie} | R Documentation

## 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)

[Package spatzie version 1.0.1 Index]