emapplot: emapplot
In GuangchuangYu/enrichplot: Visualization of Functional Enrichment Result

Description Usage Arguments Details Value Author(s) Examples

Enrichment Map for enrichment result of over-representation test or gene set enrichment analysis

emapplot(x, showCategory = 30, ...)

## S4 method for signature 'enrichResult'
emapplot(x, showCategory = 30, ...)

## S4 method for signature 'gseaResult'
emapplot(x, showCategory = 30, ...)

## S4 method for signature 'compareClusterResult'
emapplot(x, showCategory = 30, ...)

emapplot.enrichResult(
  x,
  showCategory = 30,
  layout = NULL,
  coords = NULL,
  color = "p.adjust",
  min_edge = 0.2,
  cex_label_category = 1,
  cex_category = 1,
  cex_line = 1,
  shadowtext = TRUE,
  label_style = "shadowtext",
  repel = FALSE,
  node_label = "category",
  with_edge = TRUE,
  group_category = FALSE,
  group_legend = FALSE,
  cex_label_group = 1,
  nWords = 4,
  label_format = 30,
  clusterFunction = stats::kmeans,
  nCluster = NULL,
  ...
)

emapplot.compareClusterResult(
  x,
  showCategory = 30,
  layout = NULL,
  coords = NULL,
  split = NULL,
  pie = "equal",
  legend_n = 5,
  cex_category = 1,
  cex_line = 1,
  min_edge = 0.2,
  cex_label_category = 1,
  shadowtext = TRUE,
  with_edge = TRUE,
  group_category = FALSE,
  label_format = 30,
  group_legend = FALSE,
  node_label = "category",
  label_style = "shadowtext",
  repel = FALSE,
  cex_label_group = 1,
  nWords = 4,
  clusterFunction = stats::kmeans,
  nCluster = NULL,
  cex_pie2axis = 1,
  ...
)

`x`	Enrichment result.
`showCategory`	A number or a vector of terms. If it is a number, the first n terms will be displayed. If it is a vector of terms, the selected terms will be displayed.
`...`	additional parameters additional parameters can refer the following parameters. `force` Force of repulsion between overlapping text labels. Defaults to 1. `nudge_x, nudge_y` Horizontal and vertical adjustments to nudge the starting position of each text label. `direction` "both", "x", or "y" – direction in which to adjust position of labels. `ellipse_style` style of ellipse, one of "ggforce" an "polygon". `ellipse_pro` numeric indicating confidence value for the ellipses, it can be used only when ellipse_style = "polygon". `alpha` the transparency of ellipse fill. `type` The type of ellipse. The default "t" assumes a multivariate t-distribution, and "norm" assumes a multivariate normal distribution. "euclid" draws a circle with the radius equal to level, representing the euclidean distance from the center.
`layout`	Layout of the map, e.g. 'star', 'circle', 'gem', 'dh', 'graphopt', 'grid', 'mds', 'randomly', 'fr', 'kk', 'drl' or 'lgl'.
`coords`	a data.frame with two columns: 'x' for X-axis coordinate and 'y' for Y-axis coordinate.
`color`	Variable that used to color enriched terms, e.g. 'pvalue', 'p.adjust' or 'qvalue'.
`min_edge`	The minimum similarity threshold for whether two nodes are connected, should between 0 and 1, default value is 0.2.
`cex_label_category`	Scale of category node label size.
`cex_category`	Number indicating the amount by which plotting category nodes should be scaled relative to the default.
`cex_line`	Scale of line width
`shadowtext`	a logical value, whether to use shadow font.
`label_style`	style of group label, one of "shadowtext" and "ggforce".
`repel`	whether to correct the position of the label. Defaults to FALSE.
`node_label`	Select which labels to be displayed, one of 'category', 'group', 'all' and 'none'.
`with_edge`	Logical, if TRUE (the default), draw the edges of the network diagram.
`group_category`	a logical, if TRUE(the default), group the category.
`group_legend`	Logical, if TRUE, the grouping legend will be displayed. The default is FALSE.
`cex_label_group`	Numeric, scale of group labels size, the default value is 1.
`nWords`	Numeric, the number of words in the cluster tags, the default value is 4.
`label_format`	a numeric value sets wrap length, alternatively a custom function to format axis labels.
`clusterFunction`	function of Clustering method, such as stats::kmeans(the default), cluster::clara, cluster::fanny or cluster::pam.
`nCluster`	Numeric, the number of clusters, the default value is square root of the number of nodes.
`split`	separate result by 'category' variable
`pie`	proportion of clusters in the pie chart, one of 'equal' (default) and 'Count'
`legend_n`	number of circle in legend
`cex_pie2axis`	It is used to adjust the relative size of the pie chart on the coordinate axis, the default value is 1.

This function visualizes gene sets as a network (i.e. enrichment map). Mutually overlapping gene sets tend to cluster together, making it easier for interpretation. When the similarity between terms meets a certain threshold (default is 0.2, adjusted by parameter 'min_edge'), there will be edges between terms. The stronger the similarity, the shorter and thicker the edges. The similarity between terms is obtained by function 'pairwise_termsim', the details of similarity calculation can be found in its documentation: pairwise_termsim.

ggplot object

Guangchuang Yu

## Not run: 
    library(DOSE)
    data(geneList)
    de <- names(geneList)[1:100]
    x <- enrichDO(de)
    x2 <- pairwise_termsim(x)
    emapplot(x2)
    # use `layout` to change the layout of map
    emapplot(x2, layout = "star")
    # use `showCategory` to  select the displayed terms. It can be a number of a vector of terms.
    emapplot(x2, showCategory = 10)
    categorys <- c("pre-malignant neoplasm", "intestinal disease",
                   "breast ductal carcinoma", "non-small cell lung carcinoma")
    emapplot(x2, showCategory = categorys)

    # It can also graph compareClusterResult
    data(gcSample)
    library(clusterProfiler)
    library(DOSE)
    library(org.Hs.eg.db)
    data(gcSample)
    xx <- compareCluster(gcSample, fun="enrichGO", OrgDb="org.Hs.eg.db")
    xx2 <- pairwise_termsim(xx)
    emapplot(xx2)

## End(Not run)