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
Usage
Arguments
Value
Note
See Also
Examples
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
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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
Examples
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## 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 June 18, 2019, 3:01 p.m.
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Description Usage Arguments Value Note See Also Examples
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
1 2 3 4 5 6 7 8 9 10 11 | 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
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | ## 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)
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