XGR (version 1.1.7)

xEnrichCtree: Function to visualise enrichment results using a tree-like circular plot

Description

xEnrichCtree is supposed to visualise enrichment results using a tree-like circular plot.

Usage

xEnrichCtree(
eTerm,
ig,
FDR.cutoff = NULL,
node.color = c("zscore", "adjp", "or", "nOverlap"),
colormap = "brewer.Reds",
zlim = NULL,
node.size = c("adjp", "zscore", "or", "nOverlap"),
slim = NULL,
node.size.range = c(0.5, 4.5),
group.gap = 0.08,
group.color = "lightblue",
group.size = 0.2,
group.label.size = 2,
group.label.color = "black",
legend.direction = c("auto", "horizontal", "vertical"),
leave.label.orientation = c("inwards", "outwards"),
...
)

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','nOverlap'; 'group' optionally)

ig

an object of class "igraph" with node attribute 'name'. It could be a 'phylo' object converted to. Note: the leave labels would be the node attribute 'name' unless the node attribute 'label' is explicitely provided

FDR.cutoff

FDR cutoff used to show the significant terms only. By default, it is set to NULL; useful when nodes sized by FDR

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

group.gap

the gap between group circles. Only works when multiple groups provided

group.color

the color of group circles. Only works when multiple groups provided

group.size

the line width of group circles. Only works when multiple groups provided

group.label.size

the size of group circle labelling. Always a sequential integer located at the top middle. Only works when multiple groups provided

group.label.color

the color of group circle labelling. Only works when multiple groups provided

legend.direction

the legend guide direction. It can be "horizontal" (useful for many groups with lengthy labelling), "vertical" and "auto" ("vertical" when multiple groups provided; otherwise "horizontal")

leave.label.orientation

the leave label orientation. It can be "outwards" and "inwards"

...

additional graphic parameters used in xCtree

Value

a ggplot2 object appended with 'ig', 'data' which should contain columns 'x','y', 'leaf' (T/F), 'name' (the same as V(ig)$name), 'tipid' (tip id), 'label' (if not given in ig, a 'name' varient), 'angle' and 'hjust' (assist in leave label orientation), and 'data_enrichment' (enrichment results for tips)

See Also

xCtree

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 <- xEnrichCtree(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 <- xEnrichCtree(ls_eTerm, ig)
# }