PAN-class {PANR} | R Documentation

## An S4 class for inferring a posterior association network

### Description

This S4 class includes methods to infer posterior association networks and enriched modules of functional gene interactions from rich phenotyping screens.

### Objects from the Class

Objects of class `PAN` can be created from `new("PAN", bm1, bm2)` (see the example below for details).

### Slots

`bm1`:

  an object of S4 class `BetaMixture`, which models the first- order similarities between genes (see `BetaMixture`).

`bm2`:

  an object of S4 class `BetaMixture`, which models the second- order similarities between genes (modularity).

`edgeWt`:

  a weighted adjacency matrix computed from the posterior probabilities for gene associations to belong to mixture components (see `edgeWeight`).

`engine`:

  the graphics visualization engine for PAN.

`graph`:

  a weighted adjacency matrix with edge weights satisfying certain constraints specified by the user (see `infer`).

`modules`:

  a list summarizing inferred enriched functional gene modules (see `pvclustModule`.

`iPAN`:

  an igraph object for storing the inferred PAN.

`legend`:

  a list of legends for built PAN graph.

`summary`:

  a list of summary information for available results.

### Methods

An overview of methods (More detailed introduction can be found in help for each specific function.):

`edgeWeight`

  compute edge weights by signal-to-noise ratio, posterior odd or posterior probabilities (more details in `edgeWeight`).

`infer`

  infer a posterior association network given the beta-mixture model(s) fitted to first- and/or second-order similarities (more details in `infer`).

`pvclustModule`

  search significantly enriched functional gene modules by hierarchical clustering with bootstrap resampling based on the package `pvclust` (more details in `pvclustModule`).

`exportPAN`

  export the inferred PAN or modules to file(s) in a variety of formats (more details in `exportPAN`).

`sigModules`

  retrieve significant gene modules that satisfy the given p-value cutoff and module size range (more details in `sigModules`).

`viewNestedModules`

  view a nested structure for gene modules searched by hierarchical clustering (more details in `viewNestedModules`).

`viewPAN`

  view the inferred PAN or modules in `igraph` or `RedeR` (more details in `viewPAN`).

`buildPAN`

  build a PAN graph for visualization in `igraph` or `RedeR` (more details in `viewPAN`).

`viewLegend`

  View the legends for the graph built for PAN.

`summarize`

  summarize results including input data and parameters, inferred graph and modules.

### 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` `infer` `pvclustModule` `exportPAN` `sigModules` `viewPAN` `viewNestedModules` `summarize`

### Examples

    ## Not run:
    data(bm, package="PANR")
    ##create an object of `PAN'
    pan<-new("PAN", bm1=bm1)
    ##infer a PAN
    pan<-infer(pan, para=list(type="SNR", log=TRUE, sign=TRUE, cutoff=log(5)),
    	filter=FALSE, verbose=TRUE)
    ##build a PAN graph for RedeR, hide negative edges
    ##using colors scaled based on the clustering results from Bakal et al. 2007
    data(Bakal2007Cluster)
    pan<-buildPAN(pan, engine="RedeR", para=list(nodeColor=nodeColor, hideNeg=TRUE))
    ##view PAN in RedeR
    library(RedeR)
    viewPAN(pan, what="graph")
    ##print a summary of results
    summarize(pan, "ALL")

    ## End(Not run)

[Package PANR version 1.40.0 Index]