xOBOcode: Function to create codes annotating nodes in an igraph object

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

Description

xOBOcode is supposed to create codes annotating nodes in an igraph object. It returns two ggplot2 objects, one for visualing the network with nodes lablelled by codes, the other for listing code meaning in a table

Usage

xOBOcode(
g,
node.level = "term_distance",
node.level.value = 2,
node.label.size = 2,
node.label.color = "darkblue",
node.label.alpha = 0.8,
node.label.padding = 0,
node.label.arrow = 0.01,
node.label.force = 0,
node.shape = 19,
node.xcoord = NULL,
node.ycoord = NULL,
node.color = NULL,
node.color.title = NULL,
colormap = "grey-grey",
ncolors = 64,
zlim = NULL,
node.size.range = 4,
title = "",
edge.size = 0.5,
edge.color = "black",
edge.color.alpha = 0.4,
edge.curve = 0.1,
edge.arrow = 2,
edge.arrow.gap = 0.02,
node.table = "term_name",
node.table.wrap = 50,
table.base.size = 7,
table.row.space = 2,
table.nrow = 55,
table.ncol = NULL,
root.code = "RT"
)

Arguments

g	an object of class "igraph"
node.level	a character specifying which node attribute defining the node level. By default, it is 'term_distance'
node.level.value	a positive integer specifying the level value as major branches. By default, it is 2
node.label.size	a character specifying which node attribute used for node label size
node.label.color	a character specifying which node attribute used for the node label color
node.label.alpha	the 0-1 value specifying transparency of node labelling
node.label.padding	the padding around the labeled node
node.label.arrow	the arrow pointing to the labeled node
node.label.force	the repelling force between overlapping labels
node.shape	an integer specifying node shape
node.xcoord	a vector specifying x coordinates. If NULL, it will be created using igraph::layout_with_kk
node.ycoord	a vector specifying y coordinates. If NULL, it will be created using igraph::layout_with_kk
node.color	a character specifying which node attribute used for node coloring
node.color.title	a character specifying the title for node coloring
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
ncolors	the number of colors specified over the colormap
zlim	the minimum and maximum values for which colors should be plotted
node.size.range	the range of actual node size
title	a character specifying the title for the plot
edge.size	a numeric value specifying the edge size. By default, it is 0.5
edge.color	a character specifying which edge attribute defining the the edge colors
edge.color.alpha	the 0-1 value specifying transparency of edge colors
edge.curve	a numeric value specifying the edge curve. 0 for the straight line
edge.arrow	a numeric value specifying the edge arrow. By default, it is 2
edge.arrow.gap	a gap between the arrow and the node
node.table	a character specifying which node attribute for coding. By default, it is 'term_name'
node.table.wrap	a positive integer specifying wrap width of coded node labelling
table.base.size	a positive integer specifying font size in the table
table.row.space	a positive numeric value specifying amplying horizental space for a row with wrapped text
table.nrow	a positive integer specifying the number of rows in the table
table.ncol	NULL or a positive integer specifying the number of columns per page. If NULL, it will be 3 or less
root.code	a character specifying the root code. By default, it is 'RT'

Value

a list with 3 components, two ggplot objects (code and table) and an igraph object (ig appended with node attributes 'node.code' and 'node.table')

Note

none

See Also

xGGnetwork

Examples

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

# load REACTOME
# 1a) restricted to Immune System ('R-HSA-168256') or Signal Transduction ('R-HSA-162582')
g <- xRDataLoader(RData.customised='ig.REACTOME',
RData.location=RData.location)
neighs.out <- igraph::neighborhood(g, order=vcount(g),
nodes="R-HSA-168256", mode="out")
vids <- V(g)[unique(unlist(neighs.out))]$name
ig <- igraph::induced.subgraph(g, vids=vids)

# 1b) visualise the graph with nodes coded
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=2,
node.shape=19, node.size.range=4, edge.color.alpha=0.2)
pdf('xOBOcode.pdf', useDingbats=FALSE, width=8, height=8)
print(ls_gp$code + coord_equal(ratio=1))
print(ls_gp$table)
dev.off()

# 1c) visualise the graph with nodes coded and colored by information content (IC)
V(ig)$IC <- -1*log10(V(ig)$nAnno/max(V(ig)$nAnno))
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=2,
node.shape=19, node.size.range=4, node.color='IC',
node.color.title='IC', colormap='white-cyan-darkcyan')

V(ig)$term_anno <- log10(V(ig)$nAnno)
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=2,
node.shape=19, node.size.range=4, node.color='term_anno',
node.color.title='# genes\n(log10)', colormap='white-cyan-darkcyan',
zlim=c(1,4))


# load EF (annotating GWAS reported genes)
# 2a) restricted to disease ('EFO:0000408') and annotation (>=10)
# 2a) restricted to immune system disease ('EFO:0000540') and annotation (>=10)
g <- xRDataLoader(RData.customised='ig.EF',
RData.location=RData.location)
neighs.out <- igraph::neighborhood(g, order=vcount(g),
nodes="EFO:0000540", mode="out")
nodeClan <- V(g)[unique(unlist(neighs.out))]$name
anno <- xRDataLoader(RData.customised='org.Hs.egEF',
RData.location=RData.location)
vec <- sapply(anno$gs, length)
nodeAnno <- names(vec[vec>=10])
neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno,
mode="in")
nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
vids <- intersect(nodeClan, nodeAnno)
ig <- igraph::induced.subgraph(g, vids=vids)
V(ig)$anno <- anno$gs[V(ig)$name]
# 2b) visualise the graph with nodes coded
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=4,
node.shape=19, node.size.range=4, edge.color.alpha=0.2)
pdf('xOBOcode.pdf', useDingbats=FALSE, width=8, height=8)
print(ls_gp$code + coord_equal(ratio=1))
print(ls_gp$table)
dev.off()
# 2c) ## GWAS genes for immune system disease ('EFO:0000540')
anno <- xRDataLoader(RData.customised='org.Hs.egEF',
RData.location=RData.location)
genes <- anno$gs[['EFO:0000540']]
# 2d) ## GWAS SNPs for immune system disease ('EFO:0000540')
annotation <- xRDataLoader(RData.customised='GWAS2EF',
RData.location=RData.location)
dag <- xDAGpropagate(g, annotation, path.mode="all_paths",
propagation="min")
snps <- unlist(V(dag)[V(dag)$name=='EFO:0000540']$anno)
# 2e) ## ChEMBL targets for immune system disease ('EFO:0000540')
annotation <- xRDataLoader(RData.customised='Target2EF',
RData.location=RData.location)
dag <- xDAGpropagate(g, annotation, path.mode="all_paths",
propagation="max")
targets <- unlist(V(dag)[V(dag)$name=='EFO:0000540']$anno)


# load GOBP
# 3a) restricted to immune system process ('GO:0002376') and annotation (>=10)
g <- xRDataLoader(RData.customised='ig.GOBP',
RData.location=RData.location)
neighs.out <- igraph::neighborhood(g, order=vcount(g),
nodes="GO:0002376", mode="out")
nodeClan <- V(g)[unique(unlist(neighs.out))]$name
anno <- xRDataLoader(RData.customised='org.Hs.egGOBP',
RData.location=RData.location)
vec <- sapply(anno$gs, length)
nodeAnno <- names(vec[vec>=10])
neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno,
mode="in")
nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
vids <- intersect(nodeClan, nodeAnno)
ig <- igraph::induced.subgraph(g, vids=vids)
V(ig)$anno <- anno$gs[V(ig)$name]
# 3b) visualise the graph with nodes coded
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=1,
node.shape=19, node.size.range=4, edge.color.alpha=0.2)
pdf('xOBOcode.pdf', useDingbats=FALSE, width=8, height=8)
print(ls_gp$code + coord_equal(ratio=1))
print(ls_gp$table)
dev.off()


# load GOMF
# 4a) restricted to molecular function ('GO:0003674') and annotation (>=50)
g <- xRDataLoader(RData.customised='ig.GOMF',
RData.location=RData.location)
neighs.out <- igraph::neighborhood(g, order=vcount(g),
nodes="GO:0003674", mode="out")
nodeClan <- V(g)[unique(unlist(neighs.out))]$name
anno <- xRDataLoader(RData.customised='org.Hs.egGOMF',
RData.location=RData.location)
vec <- sapply(anno$gs, length)
nodeAnno <- names(vec[vec>=50])
neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno,
mode="in")
nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
vids <- intersect(nodeClan, nodeAnno)
ig <- igraph::induced.subgraph(g, vids=vids)
V(ig)$anno <- anno$gs[V(ig)$name]
# 4b) visualise the graph with nodes coded
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=1,
node.shape=19, node.size.range=4, edge.color.alpha=0.2)
pdf('xOBOcode.pdf', useDingbats=FALSE, width=8, height=8)
print(ls_gp$code + coord_equal(ratio=1))
print(ls_gp$table)
dev.off()


# load HPPA
# 5a) restricted to Abnormality of the immune system ('HP:0002715') and annotation (>=50)
g <- xRDataLoader(RData.customised='ig.HPPA',
RData.location=RData.location)
neighs.out <- igraph::neighborhood(g, order=vcount(g),
nodes="HP:0002715", mode="out")
nodeClan <- V(g)[unique(unlist(neighs.out))]$name
anno <- xRDataLoader(RData.customised='org.Hs.egHPPA',
RData.location=RData.location)
vec <- sapply(anno$gs, length)
nodeAnno <- names(vec[vec>=50])
neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno,
mode="in")
nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
vids <- intersect(nodeClan, nodeAnno)
ig <- igraph::induced.subgraph(g, vids=vids)
V(ig)$anno <- anno$gs[V(ig)$name]
# 5b) visualise the graph with nodes coded
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=1,
node.shape=19, node.size.range=4, edge.color.alpha=0.2)
pdf('xOBOcode.pdf', useDingbats=FALSE, width=8, height=8)
print(ls_gp$code + coord_equal(ratio=1))
print(ls_gp$table)
dev.off()


# load DO
# 6a) restricted to immune system disease ('DOID:2914') and annotation (>=10)
g <- xRDataLoader(RData.customised='ig.DO',
RData.location=RData.location)
neighs.out <- igraph::neighborhood(g, order=vcount(g),
nodes="DOID:2914", mode="out")
nodeClan <- V(g)[unique(unlist(neighs.out))]$name
anno <- xRDataLoader(RData.customised='org.Hs.egDO',
RData.location=RData.location)
vec <- sapply(anno$gs, length)
nodeAnno <- names(vec[vec>=10])
neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno,
mode="in")
nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
vids <- intersect(nodeClan, nodeAnno)
ig <- igraph::induced.subgraph(g, vids=vids)
V(ig)$anno <- anno$gs[V(ig)$name]
# 6b) visualise the graph with nodes coded
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=2,
node.shape=19, node.size.range=4, edge.color.alpha=0.2)
pdf('xOBOcode.pdf', useDingbats=FALSE, width=8, height=8)
print(ls_gp$code + coord_equal(ratio=1))
print(ls_gp$table)
dev.off()


# load MP
# 7a) restricted to immune system phenotype ('MP:0005387') and annotation (>=50)
# 7a) restricted to abnormal immune system physiology ('MP:0001790') and annotation (>=50)
g <- xRDataLoader(RData.customised='ig.MP',
RData.location=RData.location)
neighs.out <- igraph::neighborhood(g, order=vcount(g),
nodes="MP:0001790", mode="out")
nodeClan <- V(g)[unique(unlist(neighs.out))]$name
anno <- xRDataLoader(RData.customised='org.Hs.egMP',
RData.location=RData.location)
vec <- sapply(anno$gs, length)
nodeAnno <- names(vec[vec>=50])
neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno,
mode="in")
nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
vids <- intersect(nodeClan, nodeAnno)
ig <- igraph::induced.subgraph(g, vids=vids)
V(ig)$anno <- anno$gs[V(ig)$name]
# 7b) visualise the graph with nodes coded
ls_gp <- xOBOcode(g=ig, node.level='term_distance', node.level.value=3,
node.shape=19, node.size.range=4, edge.color.alpha=0.2)
pdf('xOBOcode.pdf', useDingbats=FALSE, width=8, height=8)
print(ls_gp$code + coord_equal(ratio=1))
print(ls_gp$table)
dev.off()

## End(Not run)

XGR documentation built on Jan. 8, 2020, 5:06 p.m.