infer.edge.type: Infer regulation direction for each edge

Description

The method infers edge types (up-regulation, down-regulation) for a given nem model. Direct approach: For an edge a->b the method looks, whether b is up- or down-regulated in a knock-down of a. Indirect approach: For an edge a->b the method looks at the fraction of E-genes attached to b (including b itself), which are up- or down-regulated in a knock-down of a. If significantly more genes are down-regulated than up-regulated, the edge a->b is assumed to be an activation. Likewise, if significantly more genes are up-regulated than down-regulated, a->b is assumed to be an inhibition. If there is no significant difference in up- and down-regulated edges, a->b does not have a specified type.

Usage

infer.edge.type(x, logFC, alpha=0.05, adj.method="BY", method=c("direct", "indirect"))

Arguments

nem object

logFC

matrix with fold changes. The rownames of this matrix should correspond to the rownames of the data matrix, which was used to infer the nem model.

alpha

p-value cutoff

adj.method

multiple testing correction method. Default: Benjamini-Yekutieli

method

default: "direct"

Value

Modified nem object. Each edge in the nem graph now has a "weight" and a "label" attribute. The label attribute corresponds to the original value in the adjacency matrix. The weight attribute encodes up- and down-regulation in the following way: value 2 means up-regulation, value -1 down-regulation and value 1 an unknown effect.

Details

Significance in case of the indirect method is calculated using a two-tailed binomial test with null hypothesis p=0.5.

Examples

Run this code

   	data("BoutrosRNAi2002") 
	D <- BoutrosRNAiDiscrete[,9:16]   	
	result = nem(D, control=set.default.parameters(unique(colnames(D)), para=c(0.13,0.05)))
   	resEdgeInf = infer.edge.type(result, BoutrosRNAiLogFC)
  if(require(Rgraphviz))
   	plot.nem(resEdgeInf)