xA2NetCode: Function to create codes annotating nodes in an igraph object
In A2: Atlas and Analysis of Systems-Biology-Led Pathways

Description Usage Arguments Value Note See Also Examples

View source: R/xA2NetCode.r

xA2NetCode 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

xA2NetCode(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")

`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'

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')

none

xA2NetCode

# Load the library
library(A2)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev/"

## Not run: 
# 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 <- xA2NetCode(g=ig, node.level='term_distance',
node.level.value=2, node.shape=19, node.size.range=4,
edge.color.alpha=0.2)
pdf('xA2NetCode.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 <- xA2NetCode(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 <- xA2NetCode(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 <- xA2NetCode(g=ig, node.level='term_distance',
node.level.value=4, node.shape=19, node.size.range=4,
edge.color.alpha=0.2)
pdf('xA2NetCode.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 <- xA2NetCode(g=ig, node.level='term_distance',
node.level.value=1, node.shape=19, node.size.range=4,
edge.color.alpha=0.2)
pdf('xA2NetCode.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 <- xA2NetCode(g=ig, node.level='term_distance',
node.level.value=1, node.shape=19, node.size.range=4,
edge.color.alpha=0.2)
pdf('xA2NetCode.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 <- xA2NetCode(g=ig, node.level='term_distance',
node.level.value=1, node.shape=19, node.size.range=4,
edge.color.alpha=0.2)
pdf('xA2NetCode.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 <- xA2NetCode(g=ig, node.level='term_distance',
node.level.value=2, node.shape=19, node.size.range=4,
edge.color.alpha=0.2)
pdf('xA2NetCode.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 <- xA2NetCode(g=ig, node.level='term_distance',
node.level.value=3, node.shape=19, node.size.range=4,
edge.color.alpha=0.2)
pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
print(ls_gp$code + coord_equal(ratio=1))
print(ls_gp$table)
dev.off()

## End(Not run)