infer: Infer a posterior association network

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

Description

The function infers a posterior association network from beta-mixture modelling of functional associations computed from rich phenotyping screens.

Usage

infer(object, para=list(type='SNR', log=TRUE, sign=TRUE, cutoff=0),
filter=FALSE, verbose=TRUE, ...)

Arguments

object	an object of S4 class PAN.
para	a list of parameters to perform inference (see details).
filter	a logical value specifying whether or not to filter out genes without any significant association with all the other genes.
verbose	a logical value to switch on (if TRUE) or off if FALSE detailed run-time message.
...	not in use, only for further extension.

Details

This function employs different edge weights to infer a posterior association network (see edgeWeight for more details). Multiple parameters are provided for the user to specify the network:

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

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

'sign' - a logical value specifying whether a signed graph should be inferred. It is only valid when type='SNR'.

'cutoff' - a numeric value giving the threshold to tell the significance of an edge.

Value

This function will return an object of class PAN with inferred PAN updated in slot 'graph'.

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

edgeWeight

Examples

data(bm, package="PANR")
pan<-new("PAN", bm1=bm1)
pan<-infer(pan, para=list(type="SNR", log=TRUE, sign=TRUE, cutoff=log(5)),
filter=FALSE, verbose=TRUE)

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