Search enriched functional gene modules by pvclust

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Description

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

Usage

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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

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## 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)