xCircos: Function to visualise a network as a circos plot

In Pi: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level

Description

xCircos is used to visualise a network as a circos plot. The network must be a 'igraph' object. The degree of similarity between SNPs (or genes) is visualised by the colour of links. This function can be used either to visualise the most similar links or to plot links involving an input SNP (or gene).

Usage

xCircos(
g,
entity = c("SNP", "Gene", "Both"),
top_num = 50,
colormap = c("yr", "bwr", "jet", "gbr", "wyr", "br", "rainbow", "wb",
"lightyellow-orange"),
rescale = TRUE,
nodes.query = NULL,
ideogram = TRUE,
chr.exclude = "auto",
entity.label.cex = 0.7,
entity.label.side = c("in", "out"),
entity.label.track = 1,
entity.label.query = NULL,
GR.SNP = c("dbSNP_GWAS", "dbSNP_Common", "dbSNP_Single"),
GR.Gene = c("UCSC_knownGene", "UCSC_knownCanonical"),
verbose = TRUE,
RData.location = "http://galahad.well.ox.ac.uk/bigdata",
guid = NULL
)

Arguments

g	an object of class "igraph". For example, it stores semantic similarity results with nodes for genes/SNPs and edges for pair-wise semantic similarity between them
entity	the entity of similarity analysis for which results are being plotted. It can be either "SNP" or "Gene"
top_num	the top number of similarity edges to be plotted
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. "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
rescale	logical to indicate whether the edge values are rescaled to the range [0,1]. By default, it sets to true
nodes.query	nodes in query for which edges attached to them will be displayed. By default, it sets to NULL meaning no such restriction
ideogram	logical to indicate whether chromosome banding is plotted
chr.exclude	a character vector of chromosomes to exclude from the plot, e.g. c("chrX", "chrY"). By defautl, it is 'auto' meaning those chromosomes without data will be excluded. If NULL, no chromosome is excluded
entity.label.cex	the font size of genes/SNPs labels. Default is 0.8
entity.label.side	the position of genes/SNPs labels relative to chromosome ideogram. It can be "out" (by default) or "in"
entity.label.track	an integer specifying the plot track for genes/SNPs labels. Default is 1
entity.label.query	which genes/SNPs in query will be labelled. By default, it sets to NULL meaning all will be displayed. If labels in query can not be found, then all will be displayed
GR.SNP	the genomic regions of SNPs. By default, it is 'dbSNP_GWAS', that is, SNPs from dbSNP (version 146) restricted to GWAS SNPs and their LD SNPs (hg19). It can be 'dbSNP_Common', that is, Common SNPs from dbSNP (version 146) plus GWAS SNPs and their LD SNPs (hg19). Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to dbSNP IDs. Then, tell "GR.SNP" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly
GR.Gene	the genomic regions of genes. By default, it is 'UCSC_knownGene', that is, UCSC known genes (together with genomic locations) based on human genome assembly hg19. It can be 'UCSC_knownCanonical', that is, UCSC known canonical genes (together with genomic locations) based on human genome assembly hg19. Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to Gene Symbols. Then, tell "GR.Gene" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly
verbose	logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display
RData.location	the characters to tell the location of built-in RData files. See xRDataLoader for details
guid	a valid (5-character) Global Unique IDentifier for an OSF project. See xRDataLoader for details

Value

a circos plot with edge weights between input snps/genes represented by the colour of the links

Note

none

See Also

xRDataLoader

Examples

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

# provide genes and SNPs reported in GWAS studies
ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase',
RData.location=RData.location)

# 1) SNP-based similarity analysis using GWAS Catalog traits (mapped to EF)
## Get lead SNPs reported in AS GWAS
example.snps <- names(ImmunoBase$AS$variants)
SNP.g <- xSocialiserSNPs(example.snps, include.LD=NA,
RData.location=RData.location)
# Circos plot of the EF-based SNP similarity network
#out.file <- "SNP_Circos.pdf"
#pdf(file=out.file, height=12, width=12, compress=TRUE)
xCircos(g=SNP.g, entity="SNP", RData.location=RData.location)
#dev.off()
# Circos plot involving nodes 'rs6871626'
xCircos(g=SNP.g, entity="SNP", nodes.query="rs6871626",
RData.location=RData.location)

# 2) Gene-based similarity analysis using Disease Ontology (DO)
## Get genes within 10kb away from AS GWAS lead SNPs
example.genes <- names(which(ImmunoBase$AS$genes_variants<=10000))
gene.g <- xSocialiserGenes(example.genes, ontology="DO",
RData.location=RData.location)
# Circos plot of the DO-based gene similarity network
#out.file <- "Gene_Circos.pdf"
#pdf(file=out.file, height=12, width=12, compress=TRUE)
xCircos(g=gene.g, entity="Gene", chr.exclude="chrY",
RData.location=RData.location)
#dev.off()

# 3) Advanced usages: Gene-SNP pairs from trans-eQTL mapping
JKscience_TS3A <- xRDataLoader(RData.customised='JKscience_TS3A',
RData.location=RData.location)
## extract the significant trans-eQTL in IFN
ind <- -1*log10(JKscience_TS3A$IFN_fdr)
ind <- which(!is.na(ind) & ind>2)
relations <- JKscience_TS3A[ind, c("Symbol","variant","IFN_fdr")]
relations <- data.frame(from=relations$Symbol, to=relations$variant,
weight=-log10(relations$IFN_fdr))
ig_Gene2SNP <- igraph::graph.data.frame(d=relations, directed=TRUE)
# Circos plot of the eQTL (Gene-SNP) network
#out.file <- "eQTL_Circos.pdf"
#pdf(file=out.file, height=12, width=12, compress=TRUE)
xCircos(g=ig_Gene2SNP, entity="Both", top_num=NULL,
nodes.query=c("GAD1","TNFRSF1B"), chr.exclude=NULL,
entity.label.side="out", RData.location=RData.location)
#dev.off()

## End(Not run)

Pi documentation built on Nov. 26, 2020, 2:01 a.m.