network.plot | R Documentation |
This function plots a network of SNPs with potential multi-SNP effects.
network.plot(
graphical.score.list,
preprocessed.list,
n.top.scoring.pairs = NULL,
node.shape = "circle",
repulse.rad = 1000,
node.size = 25,
graph.area = 100,
vertex.label.cex = 0.5,
edge.width.cex = 12,
plot = TRUE,
edge.color.ramp = c("lightblue", "blue"),
node.color.ramp = c("white", "red"),
plot.legend = TRUE,
high.ld.threshold = 0.1,
plot.margins = c(2, 1, 2, 1),
legend.title.cex = 1.75,
legend.axis.cex = 1.75,
...
)
graphical.score.list |
The list returned by function
|
preprocessed.list |
The initial list produced by function
|
n.top.scoring.pairs |
An integer indicating the number of top scoring SNP-pairs to plot. Defaults to, NULL, which plots all pairs. For large networks, plotting a subset of the top scoring pairs can improve the appearance of the graph. |
node.shape |
The desired node shape. See
|
repulse.rad |
A scalar affecting the graph shape. Decrease to reduce overlapping nodes, increase to move nodes closer together. |
node.size |
A scalar affecting the size of the graph nodes. Increase to increase size. |
graph.area |
A scalar affecting the size of the graph area. Increase to increase graph area. |
vertex.label.cex |
A scalar controlling the size of the vertex label. Increase to increase size. |
edge.width.cex |
A scalar controlling the width of the graph edges. Increase to make edges wider. |
plot |
A logical indicating whether the network should be plotted. If set to false, this function will return an igraph object to be used for manual plotting. |
edge.color.ramp |
A character vector of colors. The coloring of the
network edges will be shown on a gradient, with the lower scoring edge
weights closer to the first color specified in |
node.color.ramp |
A character vector of colors. The coloring of the
network nodes will be shown on a gradient, with the lower scoring nodes
closer to the first color specified in |
plot.legend |
A boolean indicating whether a legend should be plotted. Defaults to TRUE. |
high.ld.threshold |
A numeric value between 0 and 1, indicating the r^2
threshold in complements (or unaffected siblings)
above which a pair of SNPs in the same LD block
(as specified in |
plot.margins |
A vector of length 4 passed to |
legend.title.cex |
A numeric value controlling the size of the legend titles. Defaults to 1.75. Increase to increase font size, decrease to decrease font size. |
legend.axis.cex |
A numeric value controlling the size of the legend axis labels. Defaults to 1.75. Increase to increase font size, decrease to decrease font size. |
... |
Additional arguments to be passed to |
An igraph object, if plot
is set to FALSE.
data(case)
data(dad)
data(mom)
case <- as.matrix(case)
dad <- as.matrix(dad)
mom <- as.matrix(mom)
data(snp.annotations)
set.seed(1400)
# preprocess data
target.snps <- c(1:3, 30:32, 60:62, 85)
pp.list <- preprocess.genetic.data(case[, target.snps],
father.genetic.data = dad[ , target.snps],
mother.genetic.data = mom[ , target.snps],
ld.block.vec = c(3, 3, 3, 1))
## run GA for observed data
#observed data chromosome size 2
run.gadgets(pp.list, n.chromosomes = 5, chromosome.size = 2,
results.dir = 'tmp_2',
cluster.type = 'interactive',
registryargs = list(file.dir = 'tmp_reg', seed = 1500),
generations = 2, n.islands = 2, island.cluster.size = 1,
n.migrations = 0)
combined.res2 <- combine.islands('tmp_2', snp.annotations[ target.snps, ],
pp.list, 2)
unlink('tmp_reg', recursive = TRUE)
#observed data chromosome size 3
run.gadgets(pp.list, n.chromosomes = 5, chromosome.size = 3,
results.dir = 'tmp_3',
cluster.type = 'interactive',
registryargs = list(file.dir = 'tmp_reg', seed = 1500),
generations = 2, n.islands = 2, island.cluster.size = 1,
n.migrations = 0)
combined.res3 <- combine.islands('tmp_3', snp.annotations[ target.snps, ],
pp.list, 2)
unlink('tmp_reg', recursive = TRUE)
## create list of results
final.results <- list(combined.res2[1:3, ], combined.res3[1:3, ])
## compute edge scores
set.seed(20)
graphical.list <- compute.graphical.scores(final.results, pp.list,
pval.thresh = 0.5)
## plot
set.seed(10)
network.plot(graphical.list, pp.list)
lapply(c("tmp_2", "tmp_3"), unlink, recursive = TRUE)
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