monsterTransitionNetworkPlot: This function uses igraph to plot the transition matrix...

In netZoo/netZooR: Unified methods for the inference and analysis of gene regulatory networks

This function uses igraph to plot the transition matrix (directed graph) as a network. The edges in the network should be read as A 'positively/negatively contributes to' the targeting of B in the target state.

Description

This function uses igraph to plot the transition matrix (directed graph) as a network. The edges in the network should be read as A 'positively/negatively contributes to' the targeting of B in the target state.

Usage

monsterTransitionNetworkPlot(
  monsterObj,
  numEdges = 100,
  numTopTFs = 10,
  rescale = "significance"
)

Arguments

monsterObj	monsterAnalysis Object
numEdges	The number of edges to display
numTopTFs	The number of TFs to display, only when rescale='significance'
rescale	string to specify the order of edges. If set to 'significance', the TFs with the largest dTFI significance (smallest dTFI p-values) will be filtered first before plotting the edges with the largest magnitude in the transition matrix. Otherwise the filtering step will be skipped and the edges with the largest transitions will be plotted. The plotted graph represents the top numEdges edges between the numTopTFs if rescale=='significance' and top numEdges edges otherwise. The edge weight represents the observed transition edges standardized by the null and the node size in the graph is proportional to the p-values of the dTFIs of each TF. When rescale is set to 'significance', the results can be different between two MONSTER runs if the number of permutations is not large enough to sample the null, that is why it is the seed should be set prior to calling MONSTER to get reproducible results. If rescale is set to another value such as 'none', it will produce deterministic results between two identical MONSTER runs.

Value

plot the transition matrix (directed graph) as a network.

Examples

# data(yeast)
# yeast$exp.cc[is.na(yeast$exp.cc)] <- mean(as.matrix(yeast$exp.cc),na.rm=TRUE)
# design <- c(rep(0,20),rep(NA,10),rep(1,20))
# monsterRes <- monster(yeast$exp.cc, design, yeast$motif, nullPerms=100, numMaxCores=4)#' 
data(monsterRes)
monsterTransitionNetworkPlot(monsterRes, rescale='significance')
monsterTransitionNetworkPlot(monsterRes, rescale='none')

netZoo/netZooR documentation built on July 28, 2024, 6 p.m.