enrichment_analysis {chromstaR} | R Documentation

## Enrichment analysis

### Description

Plotting functions for enrichment analysis of `multiHMM` or `combinedMultiHMM` objects with any annotation of interest, specified as a `GRanges-class` object.

### Usage

    plotFoldEnrichHeatmap(
      hmm,
      annotations,
      what = "combinations",
      combinations = NULL,
      marks = NULL,
      plot = TRUE,
      logscale = TRUE
    )

    plotEnrichCountHeatmap(
      hmm,
      annotation,
      bp.around.annotation = 10000,
      max.rows = 1000,
      combinations = NULL,
      colorByCombinations = sortByCombinations,
      sortByCombinations = is.null(sortByColumns),
      sortByColumns = NULL
    )

    plotEnrichment(
      hmm,
      annotation,
      bp.around.annotation = 10000,
      region = c("start", "inside", "end"),
      num.intervals = 20,
      what = "combinations",
      combinations = NULL,
      marks = NULL,
      statistic = "fold",
      logscale = TRUE
    )

### Arguments

`hmm` | A `combinedMultiHMM` or `multiHMM` object or a file that contains such an object.
`annotations` | A `list()` with `GRanges-class` objects containing coordinates of multiple annotations The names of the list entries will be used to name the return values.
`what` | One of `c('combinations','peaks','counts','transitions')` specifying on which feature the statistic is calculated.
`combinations` | A vector with combinations for which the enrichment will be calculated. If `NULL` all combinations will be considered.
`marks` | A vector with marks for which the enrichment is plotted. If `NULL` all marks will be considered.
`plot` | A logical indicating whether the plot or an array with the fold enrichment values is returned.
`logscale` | Set to `TRUE` to plot fold enrichment on log-scale. Ignored if `statistic = 'fraction'`.
`annotation` | A `GRanges-class` object with the annotation of interest.
`bp.around.annotation` | An integer specifying the number of basepairs up- and downstream of the annotation for which the enrichment will be calculated.
`max.rows` | An integer specifying the number of randomly subsampled rows that are plotted from the `annotation` object. This is necessary to avoid crashing for heatmaps with too many rows.
`colorByCombinations` | A logical indicating whether or not to color the heatmap by combinations.
`sortByCombinations` | A logical indicating whether or not to sort the heatmap by combinations.
`sortByColumns` | An integer vector specifying the column numbers by which to sort the rows. If `sortByColumns` is specified, will force `sortByCombinations=FALSE`.
`region` | A combination of `c('start','inside','end')` specifying the region of the annotation for which the enrichment will be calculated. Select `'start'` if you have a point-sized annotation like transcription start sites. Select `c('start','inside','end')` if you have long annotations like genes.
`num.intervals` | Number of intervals for enrichment 'inside' of annotation.
`statistic` | The statistic to calculate. Either 'fold' for fold enrichments or 'fraction' for fraction of bins falling into the annotation.

### Value

A `ggplot` object containing the plot or a list() with `ggplot` objects if several plots are returned. For `plotFoldEnrichHeatmap` a named array with fold enrichments if `plot=FALSE`.

### Functions

- `plotFoldEnrichHeatmap`: Compute the fold enrichment of combinatorial states for multiple annotations.

- `plotEnrichCountHeatmap`: Plot read counts around annotation as heatmap.

- `plotEnrichment`: Plot fold enrichment of combinatorial states around and inside of annotation.

### Author(s)

Aaron Taudt

### See Also

`plotting`

### Examples

    ### Get an example multiHMM ###
    file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
                        package="chromstaR")
    model <- get(load(file))

    ### Obtain gene coordinates for rat from biomaRt ###
    library(biomaRt)
    ensembl <- useEnsembl(biomart='ENSEMBL_MART_ENSEMBL', dataset='rnorvegicus_gene_ensembl')
    genes <- getBM(attributes=c('ensembl_gene_id', 'chromosome_name', 'start_position',
                               'end_position', 'strand', 'external_gene_name',
                               'gene_biotype'),
                  mart=ensembl)
    # Transform to GRanges for easier handling
    genes <- GRanges(seqnames=paste0('chr',genes$chromosome_name),
                    ranges=IRanges(start=genes$start, end=genes$end),
                    strand=genes$strand,
                    name=genes$external_gene_name, biotype=genes$gene_biotype)
    # Rename chrMT to chrM
    seqlevels(genes)[seqlevels(genes)=='chrMT'] <- 'chrM'
    print(genes)

    ### Make the enrichment plots ###
    # We expect promoter [H3K4me3] and bivalent-promoter signatures [H3K4me3+H3K27me3]
    # to be enriched at transcription start sites.
       plotEnrichment(hmm = model, annotation = genes, bp.around.annotation = 15000) +
       ggtitle('Fold enrichment around genes') +
       xlab('distance from gene body')

    # Plot enrichment only at TSS. We make use of the fact that TSS is the start of a gene.
       plotEnrichment(model, genes, region = 'start') +
       ggtitle('Fold enrichment around TSS') +
       xlab('distance from TSS in [bp]')
    # Note: If you want to facet the plot because you have many combinatorial states you
    # can do that with
       plotEnrichment(model, genes, region = 'start') +
       facet_wrap(~ combination)

    # Another form of visualization that shows every TSS in a heatmap
    # If transparency is not supported try to plot to pdf() instead.
       tss <- resize(genes, width = 3, fix = 'start')
       plotEnrichCountHeatmap(model, tss) +
       theme(strip.text.x = element_text(size=6))

    # Fold enrichment with different biotypes, showing that protein coding genes are
    # enriched with (bivalent) promoter combinations [H3K4me3] and [H3K4me3+H3K27me3],
    # while rRNA is enriched with the empty [] and repressive combinations [H3K27me3].
       tss <- resize(genes, width = 3, fix = 'start')
       biotypes <- split(tss, tss$biotype)
       plotFoldEnrichHeatmap(model, annotations=biotypes) + coord_flip()

[Package chromstaR version 1.20.2 Index]