xEnrichLadder: Function to visualise enrichment results using ladder-like...

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

Description

xEnrichLadder is supposed to visualise enrichment results using ladder-like plot in which rows for terms and columns for its members. The members are sorted first by sharings and then by individual terms. It returns an object of class "ggplot".

Usage

xEnrichLadder(eTerm, sortBy = c("fdr", "or", "adjp", "pvalue",
"zscore",
"fc", "nAnno", "nOverlap", "none"), top_num = 5, FDR.cutoff = 0.05,
CI.one = T, colormap = "lightgrey-grey-black", x.rotate = 90,
x.text.size = 6, y.text.size = 6, shape = 22, size = 2,
label = c("concise", "full"), verbose = T, ...)

Arguments

eTerm	an object of class "eTerm"
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
top_num	the number of the top terms (sorted according to FDR or adjusted p-values). If it is 'auto', 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
x.rotate	the angle to rotate the x tick labelings. By default, it is 60
x.text.size	the text size of the x tick labelings. By default, it is 6
y.text.size	the text size of the y tick labelings. By default, it is 6
shape	the number specifying the shape. By default, it is 19
size	the number specifying the shape size. By default, it is 2
label	how to label gene sets (terms). It can be "concise" or "full"
verbose	logical to indicate whether the messages will be displayed in the screen
...	additional graphic parameters for xHeatmap

Value

an object of class "ggplot"

Note

none

See Also

xEnrichViewer, xHeatmap

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/"

data(Haploid_regulators)
## only IRF1 positive regulators
data <- subset(Haploid_regulators, Phenotype=='IRF1' &
MI<0)[,c('Gene')]

# 1) KEGGenvironmental
eTerm <- xEnricherGenes(data, ontology="KEGGenvironmental",
size.range=c(10,2000), min.overlap=5, RData.location=RData.location)
gp_ladder <- xEnrichLadder(eTerm)

# 2) PSG
eTerm <- xEnricherGenes(data,
ontology=c("PSG","Approved","GWAS","CGL")[1], size.range=c(1,20000),
min.overlap=0, RData.location=RData.location)
gp_ladder <- xEnrichLadder(eTerm, sortBy="none", top_num="auto",
FDR.cutoff=1)
gp_ladder+ coord_flip()

# 3) save into the file "xEnrichLadder.pdf"
mat <- xSparseMatrix(gp_ladder$data)
pdf("xEnrichLadder.pdf", width=2+ncol(mat)*0.075,
height=2+nrow(mat)*0.1, compress=T)
print(gp_ladder)
dev.off()

# 4) SIFTS2GOMF
## df_fpocket
SIFTS_fpocket <-
xRDataLoader(RData='SIFTS_fpocket',RData.location=RData.location)
df_fpocket <- as.data.frame(SIFTS_fpocket %>%
dplyr::filter(druggable=='Y') %>% dplyr::group_by(Symbol,PDB_code)
%>% dplyr::summarise(num_pockets=n()) %>%
dplyr::arrange(Symbol,desc(num_pockets),PDB_code))
df_fpocket <- df_fpocket[!duplicated(df_fpocket$Symbol), ]
## mat_fpocket
mat_fpocket <- df_fpocket %>% tidyr::spread(Symbol, num_pockets)
rownames(mat_fpocket) <- mat_fpocket[,1]
mat_fpocket <- mat_fpocket[,-1]
## gp_ladder
set.seed(825)
data <- as.character(sample(unique(df_fpocket$Symbol), 100))
eTerm <- xEnricherGenes(data=data, ontology="SIFTS2GOMF",
RData.location=RData.location)
gp_ladder <- xEnrichLadder(eTerm, sortBy="none", top_num=5,
FDR.cutoff=0.01, x.rotate=90)
#gp_ladder + coord_flip()
## data_matrix
ind <- match(colnames(gp_ladder$matrix), colnames(mat_fpocket))
data_matrix <- mat_fpocket[,ind[!is.na(ind)]]
ind <- which(apply(!is.na(data_matrix), 1, sum)!=0)
data_matrix <- data_matrix[ind,]
ind <- match(data, colnames(data_matrix))
data_matrix <- data_matrix[,ind[!is.na(ind)]]
## gp_pdb
gp_pdb <- xHeatmap(t(data_matrix), reorder="row", colormap="jet.top",
x.rotate=90, shape=19, size=1, x.text.size=6,y.text.size=5,
na.color='transparent', legend.title='# pockets')
#gp_pdb + coord_flip()
## plot_combined
#plot_combined <- cowplot::plot_grid(gp_ladder, gp_pdb, align="h", ncol=1, rel_heights=c(2,3))

## enrichment analysis
SIFTS_fpocket <-
xRDataLoader(RData='SIFTS_fpocket',RData.location=RData.location)
annotation.file <- SIFTS_fpocket[!duplicated(SIFTS_fpocket$Symbol),
c('Symbol','druggable')]
### 100 randomly chosen human genes
org.Hs.eg <- xRDataLoader(RData='org.Hs.eg',
RData.location=RData.location)
set.seed(825)
data <- as.character(sample(org.Hs.eg$gene_info$Symbol, 100))
### optionally, provide the test background (if not provided, all human genes)
background <- as.character(org.Hs.eg$gene_info$Symbol)
### perform enrichment analysis
eTerm <- xEnricherYours(data.file=data,
annotation.file=annotation.file, background.file=background,
size.range=c(10,20000))

## End(Not run)

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