infer {PANR} | R Documentation

## Infer a posterior association network

### 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 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

`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)

[Package PANR version 1.26.0 Index]