XGR (version 1.1.5)

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

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')

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()
# }