emapplot {enrichplot} | R Documentation |
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.
|
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)