network.plot: A function to plot a network of SNPs with potential multi-SNP...

In mnodzenski/epistasisGA: An R package to identify multi-snp effects in nuclear family studies using the GADGETS method

A function to plot a network of SNPs with potential multi-SNP effects.

Description

This function plots a network of SNPs with potential multi-SNP effects.

Usage

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,
  ...
)

Arguments

graphical.score.list	The list returned by function compute.graphical.scores, or a subset of it, if there are too many returned SNP-pairs to plot without the figure becoming too crowded. By default, the SNPs will be labeled with their RSIDs, listed in columns 3 and 4. Users can create custom labels by changing the values in these two columns.
preprocessed.list	The initial list produced by function preprocess.genetic.data.
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 names(igraph:::.igraph.shapes) for available shapes. Defaults to circle. If both maternal and child SNPs are to be plotted, this argument should be a vector of length 2, whose first element is the desired child SNP shape, and second SNP is the desired mother SNP shape.
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 edge.color.ramp, and higher scoring weights closer to the last color specified. By default, the low scoring edges are light blue, and high scoring edges are dark blue.
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 node.color.ramp, and higher scoring nodes closer to the last color specified. By default, the low scoring nodes are whiter, and high scoring edges are redder.
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 preprocessed.list) should be considered in high LD. Connections between these high LD SNPs will be dashed instead of solid lines. Defaults to 0.1. If both maternal and child SNPs are among the input variants in preprocessed.list, dashed lines can only appear between SNPs of the same type, i.e., between two maternal SNPs, or between two child SNPs.
plot.margins	A vector of length 4 passed to par(mar = ). Defaults to c(2, 1, 2, 1).
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 plot.igraph.

Value

An igraph object, if plot is set to FALSE.

Examples

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)

mnodzenski/epistasisGA documentation built on Jan. 17, 2023, 7:07 p.m.