pvclustModule {PANR} | R Documentation

## Search enriched functional gene modules by pvclust

### Description

The function employs the R package `pvclust` to search significant functional gene modules using hierarchical clustering with bootstrap resampling.

### Usage

    pvclustModule(object, nboot=1000, metric="cosine", hclustMethod="average",
    filter=TRUE, verbose=TRUE, ...)

### Arguments

`object` | an object of S4 class `PAN`.
`nboot` | a numeric value giving the number of bootstraps for `pvclust`.
`metric` | a character value specifying which distance metric to use for the hierarchical clustering: 'correlation', 'cosine', 'abscor' or those allowed by the argument 'method' in `dist`.
`hclustMethod` | the agglomerative method used in hierarchical clustering: 'average', 'ward', 'single', 'complete', 'mcquitty', 'median' or 'centroid' (see the argument `method` in `hclust` for more details).
`filter` | a logical value specifying whether or not to filter out screening data of genes without significant associations with all the other genes.
`verbose` | a logical value to switch on (if `TRUE`) or off if `FALSE` detailed run-time message.
`...` | all the other arguments accepted by the function `pvclust`.

### Details

This function performs hierarchical clustering with bootstrap resampling to quantify significance of gene clusters (modules) based on the package `pvclust`.

### Value

This function will return an object of class `PAN` with inferred gene modules (`modules$clusters`) and corresponding p-values (`modules$pval`) updated in slot '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.

R. Suzuki and H. Shimodaira. Pvclust: an r package for assessing the uncertainty in hierarchical clustering. Bioinformatics, 22(12):1540, 2006.

### Examples

    ## Not run:
    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)
    data(Bakal2007Cluster, package="PANR")
    pan<-buildPAN(pan, engine="igraph", para=list(nodeColor=nodeColor,
    hideNeg=TRUE), verbose=TRUE)
    ##need pvclust to search modules
    library(pvclust)
    pan<-pvclustModule(pan, nboot=1000, metric="cosine",
    hclustMethod="average", filter=TRUE, verbose=TRUE, r=c(5:12/7))

    ## End(Not run)

[Package PANR version 1.26.0 Index]