draw.targetNet: Target Network Structure Plot for One Driver

In jyyulab/NetBID: Network-based Bayesian Inference of Drivers, version 2

Target Network Structure Plot for One Driver

Description

draw.targetNet draws a network structure to display the target genes of one selected driver. Edges of positively-regulated target genes are orange, edges of negatively-regulated target genes are green. The width of the edges shows the strength of regulation.

Usage

draw.targetNet(
  source_label = "",
  source_z = NULL,
  edge_score = NULL,
  label_cex = 0.7,
  source_cex = 1,
  pdf_file = NULL,
  arrow_direction = "out",
  n_layer = 1,
  alphabetical_order = FALSE
)

Arguments

source_label	character, the label of selected one driver.
source_z	numeric, the Z-statistic of the selected driver. The color shade of driver's node in the network is decided by this Z-statistic. If NULL, the driver node will be colored grey. Default is NULL.
edge_score	a named vector of numerics, indicating the correlation between the driver and its target genes. The range of the numeric value is from -1 to 1. Positive value means it is positively-regulated by driver and vice versa. The names of the vector are gene names.
label_cex	numeric, giving the amount by which the text of target gene names should be magnified relative to the default. Default is 0.7.
source_cex	numeric, giving the amount by which the text of driver name should be magnified relative to the default. Default is 1.
pdf_file	character, the file path to save as PDF file. If NULL, no PDF file will be saved. Default is NULL.
arrow_direction	character, users can choose between "in" and "out". Default is "out".
n_layer	integer, number of circle layers to display. Default is 1.
alphabetical_order	logical, if TRUE, the targe gene names will be sorted alphabetically. If FALSE, will be sorted by statistics. Default is FALSE.

Value

Return a logical value. If TRUE, the plot has been created successfully.

Examples

source_label <- 'test1'
source_z <- 1.96
edge_score <- (sample(1:200,size=100,replace=TRUE)-100)/100
names(edge_score) <- paste0('G',1:100)
draw.targetNet(source_label=source_label,source_z=source_z,
               edge_score=edge_score)
draw.targetNet(source_label=source_label,source_z=source_z,
               edge_score=edge_score,n_layer=2)
draw.targetNet(source_label=source_label,source_z=source_z,
               edge_score=edge_score,
               arrow_direction='in',
               source_cex=2)
## Not run: 
source_label <- 'test1'
source_z <- 1.96
edge_score <- (sample(1:200,size=100,replace=TRUE)-100)/100
names(edge_score) <- paste0('G',1:100)
analysis.par <- list()
analysis.par$out.dir.PLOT <- getwd()
draw.targetNet(source_label=source_label,source_z=source_z,
               edge_score=edge_score,
               pdf_file=sprintf('%s/targetNet.pdf',
               analysis.par$out.dir.PLOT))

## End(Not run)

jyyulab/NetBID documentation built on Dec. 23, 2024, 6:34 a.m.