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Last data update: 2014.03.03

R: Fitting a beta-mixture model to association scores

Mulder2012.BMfitting

R Documentation

Fitting a beta-mixture model to association scores

Description

The function fits a three-beta mixture model to functional association scores computed from rich phenotyping screens in Mulder K et al., 2012 and Wang X et al., 2012.

Usage

Mulder2012.BMfitting(pheno, model="global", metric="cosine", nPerm=100)
Mulder2012.BMfitting.extended(pheno, model="stratified", metric="cosine", 
nPerm=20, partition)

Arguments

`pheno`	a numeric matrix of z-scores with rows and columns corresponding to samples and genes, respectively.
`model`	a character value specifying whether the original (if `global`) or extended (if `stratified`) model is used.
`metric`	a character value specifying the metric to compute similarity scores. Currently, 'cosine' and 'correlation' are supported (see `assoScore` for more details).
`nPerm`	a positive numeric or integer value specifying the number of permutations.
`partition`	a numeric of gene partition labels (e.g. c(rep(1, 100), rep(2, 20)) is a valid vector of partition labels for a vector of associations falling into two categories of interaction types 1 and 2).

Details

The two functions implement beta-mixture modelling in the application to epidermal stem cells. Both functions first initiate an object of class BetaMixture with inputted pheno, model and metric (and partition for the stratified model). The ‘x’ component of the beta-mixture model is then fitted to permuted data for nPerm times, and the estimated shape parameters of beta distributions are summarized by taking the median values. After fixing the shape parameters of the ‘x’ component, the function invokes fitBM to fit the global or stratified beta-mixture model.

Value

an object of class BetaMixture with beta-mixture modelling results stored at slot result

Author(s)

Xin Wang xw264@cam.ac.uk

References

Mulder KW, Wang X, Escriu C, et al. (2012). Diverse epigenetic strategies interact to control epidermal differentiation. Nature Cell Biology. doi:10.1038/ncb2520.

Wang X, Castro MA, Mulder KW and Markowetz F (2012), Posterior association networks and enriched functional gene modules inferred from rich phenotypic perturbation screens, PLoS Computational Biology, doi:10.1371/journal.pcbi.1002566.

Examples

## Not run: 
data(Mulder2012, package="Mulder2012")
BMfitting(pheno=Mulder2012, model="global", metric="cosine", nPerm=100)

## End(Not run)