heatmapPlot: Heatmap Plot of Enriched Terms

In eegc: Engineering Evaluation by Gene Categorization (eegc)

Description

This function plot the significantly enriched terms in a heatmap by calling pheatmap package.

Usage

heatmapPlot(enrichresult, GO = FALSE, terms = NULL, padjust = TRUE, pvalue = 0.05,
top= NA, filter = FALSE, main = NA, annotation = NULL, annotation_col = NULL,
annotated_row = FALSE, annotation_row = NULL,annotation_colors = NA,
display_numbers = FALSE, annotation_legend = FALSE,...)

Arguments

enrichresult	a list of data frames with enrichment results, alternatively output by functionEnrich or enrichment.
GO	logical to determine whether the terms are Gene Ontology(GO) terms enriched by functionEnrich.
terms	a character vector to specify the terms chosen to be listed in the heatmap, selected from the enrichment result, or a data frame with terms and corresponding term annotations used for annotation_row.
padjust	logical to determine whether the significantly enriched terms were selected by adjusted p.value rather than the p.value, default to TRUE.
pvalue	a cutoff value to select the significantly enriched terms.
top	a number to specify the most significantly enriched terms to be drawn in each category, default to NA without specifying.
filter	logical to specify whether the enriched terms need to be filered with the ones which are significant among the first four categories.
main	a character of main title on the heatmap plot.
annotation, annotation_row, annotation_col, annotation_colors, annotation_legend, ...	see details in pheatmap.
annotated_row	logical to determine whether the the rows are annotated by annotation_row, default to FALSE. When it's TRUE, annotation_row should be specified or using annotations in a data frame of terms.
display_numbers	logical to determine whether the gene counts number is shown on the heatmap.

Value

heatmap plot and the terms with p.values for the heatmap

Examples

# plot the enrichment results by the five gene categories
data(goenrich)
heatmaptable = heatmapPlot(goenrich, GO = TRUE, top = 5, filter = FALSE,
                           main = "Gene ontology enrichment",
                           display_numbers =  FALSE)

eegc documentation built on Nov. 8, 2020, 5:27 p.m.