grnPlot: Gene Regulatory Network Plot

In Sharonxiaoyuan/eegc: Engineering Evaluation by Gene Categorization (eegc)

Gene Regulatory Network Plot

Description

This function plot the a cell/tissue-specific gene regulatory network with genes in the five categories, alterntively reducing the network size by plotting given specific genes as nodes.

Usage

grnPlot(grn.data, cate.gene, filter = TRUE, nodes = NULL, centrality.score,
col = NULL, main= NULL, vertex.label =NULL, vertex.label.dist = 0, vertex.label.font = 1,
vertex.label.cex = 0.5, vertex.label.color="black", edge.arrow.size = 0.4,
edge.color = "grey", layout ="layout_with_lgl", legend.labels = NULL, ...)

Arguments

grn.data	a data frame with two columns named "TF" and "TG" to specify the genes as transcription regulators (TF) and target genes being regulated (TG).
cate.gene	a list of the five gene categories as nodes in the network, alternatively output by categorizeGene.
filter	logical to specify if the network is reduced to less nodes by a filter, logical to TRUE for a clear visualization.
nodes	a character vector of genes to kept in the network, only applied when filter is TRUE.
centrality.score	a vector or data frame of centrality scores for genes in the network, alternatively calculated by networkAnalyze function.
col	colors of gene vertex in each cate.gene.
main	title of the plot.
vertex.label, vertex.label.dist, vertex.label.font, vertex.label.cex, vertex.label.color	parameters for vertex labels, to specify the labels of vertex, the position of labels on the vertex, font size, cex and color for label, see details in igraph.plotting.
edge.arrow.size, edge.color	parameters for edge to specify the arrow size and color of edge, see details in igraph.plotting.
layout	the layout of network plot, see details in layout.
legend.labels	vector of label names for each cate.gene.
...	other parameters used in igraph.plotting.

Value

a igraph plot for gene regulatory network.

Examples

## Not run: 
  # select genes to shown their regulation with others
  node.genes = c("ZNF641", "BCL6")
  # enlarge the centrality
  centrality.score = degree$centrality*100
  names(centrality.score) = degree$Gene
  par(mar = c(2,2,3,2))
  grnPlot(grn.data = human.grn[[tissue]], cate.gene = cate.gene, filter = TRUE,
         nodes = node.genes, centrality.score = centrality.score,
         main = "Gene regulatory network")

## End(Not run)

Sharonxiaoyuan/eegc documentation built on April 17, 2022, 2:10 a.m.