treeplot {enrichplot}

R Documentation

treeplot

Description

Functional grouping tree diagram for enrichment result of over-representation test or gene set enrichment analysis.

Usage

treeplot(x, showCategory = 30, color = "p.adjust", label_format = 30, ...)

## S4 method for signature 'enrichResult'
treeplot(x, showCategory = 30, color = "p.adjust", label_format = 30, ...)

## S4 method for signature 'gseaResult'
treeplot(x, showCategory = 30, color = "p.adjust", label_format = 30, ...)

## S4 method for signature 'compareClusterResult'
treeplot(x, showCategory = 5, color = "p.adjust", label_format = 30, ...)

treeplot.enrichResult(
  x,
  showCategory = 30,
  color = "p.adjust",
  nWords = 4,
  nCluster = 5,
  cex_category = 1,
  label_format = 30,
  fontsize = 4,
  offset = 1,
  offset_tiplab = 1,
  hclust_method = "ward.D",
  group_color = NULL,
  extend = 0.3,
  hilight = TRUE,
  hexpand = 0.1,
  align = "both",
  ...
)

treeplot.compareClusterResult(
  x,
  showCategory = 5,
  color = "p.adjust",
  nWords = 4,
  nCluster = 5,
  cex_category = 1,
  split = NULL,
  label_format = 30,
  fontsize = 4,
  offset = 1,
  pie = "equal",
  legend_n = 3,
  offset_tiplab = 1,
  hclust_method = "ward.D",
  group_color = NULL,
  extend = 0.3,
  hilight = TRUE,
  geneClusterPanel = "heatMap",
  hexpand = 0.1,
  align = "both",
  ...
)

Arguments

x	enrichment result.
showCategory	number of enriched terms to display
color	variable that used to color enriched terms, e.g. pvalue, p.adjust or qvalue
label_format	a numeric value sets wrap length, alternatively a custom function to format axis labels.
...	additional parameters
nWords	The number of words in the cluster tags.
nCluster	The number of clusters, the default value is 5.
cex_category	Number indicating the amount by which plotting category. nodes should be scaled relative to the default. displayed completely, the user can increase this value.
fontsize	The size of text, default is 4.
offset	numeric, distance bar and tree, offset of bar and text from the clade, default is 1, meaning 1 * 1.2 * x_range_of_tree plus distance_between_tree_and_tiplab (1 * (1.2 * x_range_of_tree + distance_between_tree_and_tiplab)).
offset_tiplab	tiplab offset, the bigger the number, the farther the distance between the node and the branch. The default is 1, when geneClusterPanel = "pie", meaning 1 * max_radius_of_the_pies; when geneClusterPanel = "heatMap", meaning 1 * 0.16 * column_number_of_heatMap * x_range_of_tree; when geneClusterPanel = "dotplot", meaning 1 * 0.09 * column_number_of_dotplot * x_range_of_tree.
hclust_method	Method of hclust. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).
group_color	A vector of group colors, the length of the vector should be the same as nCluster.
extend	Numeric, extend the length of bar, default is 0.3.
hilight	Logical value, if TRUE(default), add ggtree::geom_hilight() layer.
hexpand	expand x limits by amount of xrange * hexpand.
align	control the align direction of the edge of high light rectangular. Options is 'none', 'left', 'right', 'both (default)'.
split	Separate result by 'category' variable.
pie	Used only when geneClusterPanel = "pie", proportion of clusters in the pie chart, one of 'equal' (default) and 'Count'.
legend_n	Number of circle in legend, the default value is 3.
geneClusterPanel	one of "heatMap"(default), "dotplot", "pie".

Details

This function visualizes gene sets as a tree. Gene sets with high similarity tend to cluster together, making it easier for interpretation.

Value

ggplot object

Examples

## Not run: 
    library(clusterProfiler)
    library(org.Hs.eg.db)
    library(enrichplot)
    library(GOSemSim)
    library(ggplot2)
    library(DOSE)
    data(geneList)
    gene <- names(geneList)[abs(geneList) > 2]
    ego <- enrichGO(gene  = gene,
        universe      = names(geneList),
        OrgDb         = org.Hs.eg.db,
        ont           = "BP",
        pAdjustMethod = "BH",
        pvalueCutoff  = 0.01,
        qvalueCutoff  = 0.05,
        readable      = TRUE)
    d <- godata('org.Hs.eg.db', ont="BP")
    ego2 <- pairwise_termsim(ego, method = "Wang", semData = d)
    treeplot(ego2, showCategory = 30)
    # use `hilight = FALSE` to remove ggtree::geom_hilight() layer.
    treeplot(ego2, showCategory = 30, hilight = FALSE)
    # use `offset` parameter to adjust the distance of bar and tree.
    treeplot(ego2, showCategory = 30, hilight = FALSE, offset = 1.5)
    # use `offset_tiplab` parameter to adjust the distance of nodes and branches.
    treeplot(ego2, showCategory = 30, hilight = FALSE, offset_tiplab = 1.5)
    keep <- rownames(ego2@termsim)[c(1:10, 16:20)]
    keep
    treeplot(ego2, showCategory = keep)
    treeplot(ego2, showCategory = 20, 
        group_color = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442"))
    # It can also graph compareClusterResult
    data(gcSample)
    xx <- compareCluster(gcSample, fun="enrichKEGG",
                         organism="hsa", pvalueCutoff=0.05)
    xx <- pairwise_termsim(xx)                     
    treeplot(xx)                     
    
    # use `geneClusterPanel` to change the gene cluster panel.
    treeplot(xx, geneClusterPanel = "dotplot")  
    
    treeplot(xx, geneClusterPanel = "pie")  
   
## End(Not run)

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