edgeWeight {PANR}R Documentation

Compute edge weights for posterior association networks

Description

This is an internal function to compute edge weights before inferring a posterior association network.

Usage

edgeWeight(object, which="bm1", type="SNR", log=TRUE, ...)

Arguments

object

an object of S4 class PAN.

which

a character value specifying which BetaMixture modelling result to use: first-order (if 'bm1') or second-order (if 'bm2').

type

a character value giving the type of edge weight to compute: signal- to-noise ratio (if 'SNR'), posterior probability odd (if 'PPR') or posterior probability (if 'PP').

log

a logical value specifying whether or not to compute logrithms for edge weights.

Details

This function will be called by infer to compute edge weights for posterior association networks. When inferring a signed PAN, signal-to-noise ratios are suggested to use; while inferring a PAN of only positive associations, posterior probability odds or posterior probabilities are preferred.

Value

This function will return a numeric adjacency matrix of edge weights.

Author(s)

Xin Wang xw264@cam.ac.uk

References

Xin Wang, Mauro Castro, Klaas W. Mulder and Florian Markowetz, Posterior association networks and enriched functional gene modules inferred from rich phenotypic perturbation screens, in preparation.

See Also

infer


[Package PANR version 1.40.0 Index]