edgeWeight: Compute edge weights for posterior association networks

In PANR: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations

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 [e-mail]

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

PANR documentation built on Nov. 8, 2020, 8:15 p.m.