manhattan_hicexp {multiHiCcompare} | R Documentation

## Manhattan plot function for results of multiHiCcompare

### Description

Manhattan plot function for results of multiHiCcompare

### Usage

    manhattan_hicexp(
      hicexp,
      method = "standard",
      return_df = FALSE,
      alpha = 0.05,
      plot.chr = NA
    )

### Arguments

`hicexp` | A hicexp object that has had differences detected
`method` | string denoting the p-value method to use for plotting. Options are "standard", "fisher", "stouffer", "addCLT", and "count". "standard" plots a manhattan plot using all individual p-values (very slow, use with caution). "fisher" or "stouffer" methods use the Fisher's method or the Stouffer-Liptak method, respectively, for combining p-values for each region which are then plotted on the -log10(p-value) Y-axis. "addCLT" combines p-values using the BLMA package's addCLT function. "count" summarizes the number of times a region was detected as significant (see "alpha" parameter), plotted on Y-axis. The higher the dots are, the more sighificant/more frequent a region was detected as significantly differentially interacting.
`return_df` | Logical, should the data.frame used to generate the plot be returned?
`alpha` | The adjusted p-value cutoff to be used for calling an interaction significant. This is only used if method = 'count'. Defaults to 0.05.
`plot.chr` | A numeric value indicating a specific chromosome number to subset the plot to. Defaults to NA indicating that all chromosomes will be plotted.

### Details

This function is used to create a manhattan plot for the significance of all genomic regions in the dataset. These correspond to the rows (or columns) of the upper triangle of the full Hi-C matrix. Each genomic region of the Hi-C dataset has multiple interactions it is involved in and the significance of all of these can be visualized with `method = "standard"`. Alternatively the p-values for all these interactions can be combined using either Fisher's method or the Stouffer-Liptak method of combining p-values. Additionally the "count" option will plot based on the number of times each region was found to be involved in a signficantly different interaction. The manhattan plot can be used to identify "hotspot" regions of the genome where major differences seem to be located based on the results of a multiHiCcompare analysis.

### Value

A manhattan plot and optionally the data.frame used to generate the manhattan plot.

### Examples

    data("hicexp_diff")
    manhattan_hicexp(hicexp_diff, method = "fisher")

[Package multiHiCcompare version 1.8.0 Index]