xEnrichForest: Function to visualise enrichment results using a forest plot

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

Description

xEnrichForest is supposed to visualise enrichment results using a forest plot. A point is colored by the significance level, and a horizontal line for the 95 the wider the CI, the less reliable). It returns an object of class "ggplot".

Usage

xEnrichForest(eTerm, top_num = 10, FDR.cutoff = 0.05, CI.one = T,
colormap = "ggplot2.top", ncolors = 64, zlim = NULL,
barwidth = 0.5, barheight = NULL, wrap.width = NULL,
font.family = "sans", signature = FALSE, drop = F,
sortBy = c("or", "adjp", "fdr", "pvalue", "zscore", "fc", "nAnno",
"nOverlap", "none"))

Arguments

eTerm	an object of class "eTerm" or "ls_eTerm". Alterntively, it can be a data frame having all these columns (named as 'group','ontology','name','adjp','or','CIl','CIu')
top_num	the number of the top terms (sorted according to OR). For the eTerm object, if it is 'auto' (for eTerm), only the significant terms (see below FDR.cutoff) will be displayed
FDR.cutoff	FDR cutoff used to declare the significant terms. By default, it is set to 0.05
CI.one	logical to indicate whether to allow the inclusion of one in CI. By default, it is TURE (allowed)
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), and "rainbow" (rainbow colormap, that is, red-yellow-green-cyan-blue-magenta). Alternatively, any hyphen-separated HTML color names, e.g. "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
ncolors	the number of colors specified over the colormap
zlim	the minimum and maximum z values for which colors should be plotted, defaulting to the range of the -log10(FDR)
barwidth	the width of the colorbar. Default value is 'legend.key.width' or 'legend.key.size' in 'theme' or theme
barheight	the height of the colorbar. Default value is 'legend.key.height' or 'legend.key.size' in 'theme' or theme
wrap.width	a positive integer specifying wrap width of name
font.family	the font family for texts
signature	logical to indicate whether the signature is assigned to the plot caption. By default, it sets TRUE showing which function is used to draw this graph
drop	logical to indicate whether all factor levels not used in the data will automatically be dropped. If FALSE (by default), all factor levels will be shown, regardless of whether or not they appear in the data
sortBy	which statistics will be used for sorting and viewing gene sets (terms). It can be "adjp" or "fdr" for adjusted p value (FDR), "pvalue" for p value, "zscore" for enrichment z-score, "fc" for enrichment fold change, "nAnno" for the number of sets (terms), "nOverlap" for the number in overlaps, "or" for the odds ratio, and "none" for ordering according to ID of terms. It only works when the input is an eTerm object

Value

an object of class "ggplot"

Note

none

See Also

xEnricherGenes, xEnricherSNPs, xEnrichViewer

Examples

## Not run: 
# Load the library
library(XGR)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata/"

# provide the input Genes of interest (eg 100 randomly chosen human genes)
## load human genes
org.Hs.eg <- xRDataLoader(RData='org.Hs.eg',
RData.location=RData.location)
set.seed(825)
data <- as.character(sample(org.Hs.eg$gene_info$Symbol, 100))
data

# optionally, provide the test background (if not provided, all human genes)
#background <- as.character(org.Hs.eg$gene_info$Symbol)

# 1) Gene-based enrichment analysis using REACTOME pathways
# perform enrichment analysis
eTerm <- xEnricherGenes(data, ontology="REACTOME",
RData.location=RData.location)
## forest plot of enrichment results
gp <- xEnrichForest(eTerm, top_num="auto", FDR.cutoff=0.05)

# 2) Gene-based enrichment analysis using ontologies (REACTOME and GOMF)
# perform enrichment analysis
ls_eTerm <- xEnricherGenesAdv(data, ontologies=c("REACTOME","GOMF"),
RData.location=RData.location)
## forest plot of enrichment results
gp <- xEnrichForest(ls_eTerm, FDR.cutoff=0.1)

## End(Not run)

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