xEnrichGGraph: Function to visualise enrichment results using a ggraph-like...

In XGR: Exploring Genomic Relations for Enhanced Interpretation Through Enrichment, Similarity, Network and Annotation Analysis

Description

xEnrichGGraph is supposed to visualise enrichment results using a ggraph-like lauout.

Usage

xEnrichGGraph(eTerm, ig = NULL, fixed = T, node.color = c("zscore",
"adjp", "or"), colormap = "grey-orange-darkred", zlim = NULL,
node.size = c("adjp", "zscore", "or"), slim = NULL,
node.size.range = c(0.5, 4), node.label.size = 2, leave = T,
ncolumns = NULL, ...)

Arguments

eTerm	an object of class "eTerm" or "ls_eTerm". Alterntively, it can be a data frame having all these columns ('name','adjp','or','zscore'; 'group' optionally)
ig	an object of class "igraph" with node attribute 'name'. Note: the node labels would be the node attribute 'name' unless the node attribute 'label' is explicitely provided. If provided, only those terms within it will be visualised. By default, it is NULL meaning no surch restriction
fixed	logical to indicate whether all terms in ig will be visualised. By default, it is TURE; otherwise only overlapped terms from eTerm will be visualised
node.color	which statistics will be used for node coloring. It can be "or" for the odds ratio, "adjp" for adjusted p value (FDR) and "zscore" for enrichment z-score
colormap	short name for the colormap. It can be one of "jet" (jet colormap), "bwr" (blue-white-red colormap), "gbr" (green-black-red colormap), "wyr" (white-yellow-red colormap), "br" (black-red colormap), "yr" (yellow-red colormap), "wb" (white-black colormap), "rainbow" (rainbow colormap, that is, red-yellow-green-cyan-blue-magenta), and "ggplot2" (emulating ggplot2 default color palette). Alternatively, any hyphen-separated HTML color names, e.g. "lightyellow-orange" (by default), "blue-black-yellow", "royalblue-white-sandybrown", "darkgreen-white-darkviolet". A list of standard color names can be found in http://html-color-codes.info/color-names
zlim	the minimum and maximum values for which colors should be plotted
node.size	which statistics will be used for node size. It can be "or" for the odds ratio, "adjp" for adjusted p value (FDR) and "zscore" for enrichment z-score
slim	the minimum and maximum values for which sizes should be plotted
node.size.range	the range of actual node size
node.label.size	the text size of the node labelings. By default, it is 2. If 0, all labellings will be disabled
leave	the logic specifying whether or not only leaves (nodes/labellings) shown. This can be disenabled if the layout does not support tips
ncolumns	an integer specifying the number of columns for facet_wrap. By defaul, it is NULL (decided on according to the number of groups that will be visualised)
...	additional graphic parameters used in xGGraph

Value

a ggplot2 object appended with 'ig', 'data' which should contain columns 'x','y', 'name' (the same as V(ig)$name), 'label' (if not given in ig, a 'name' varient), 'data_enrichment' (enrichment results), and 'gp_template' with labelling if multiple groups (together with no labelling for the colored plots).

Note

none

See Also

xGGraph

Examples

## Not run: 
# Load the XGR package and specify the location of built-in data
library(XGR)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"

# load the atlas of AA pathways
AA.template <- xRDataLoader("AA.template",
RData.location=RData.location)
# consensus tree
ig <- AA.template$consensus$ig

# enrichment analysis using AA pathways
input <- xRDataLoader('Haploid_regulators_all',
RData.location=RData.location)
data <- subset(input, Phenotype=="AKT")
genes <- data$Gene[data$FDR<0.05]
background <- data$Gene
eTerm <- xEnricherGenes(genes, background=background, ontology="AA",
min.overlap=5, test="fisher", RData.location=RData.location)

# circular visualisation of enriched AA pathways
gp <- xEnrichGGraph(eTerm, ig)

###############################
# advanced use: multiple groups
# enrichment analysis using AA pathways
Haploid <- subset(input, FDR<0.05)
ls_group <- split(x=Haploid$Gene, f=Haploid$Phenotype)
background <- unique(input$Gene)
ls_eTerm <- xEnricherGenesAdv(ls_group, background=background,
ontologies="AA", test="fisher", min.overlap=5,
RData.location=RData.location)

# circular visualisation of enriched AA pathways
gp <- xEnrichGGraph(ls_eTerm, ig)
gp
gp$gp_template

## End(Not run)

XGR documentation built on June 18, 2019, 3:01 p.m.