GLA-methods: Function to calculate GLA estimate

Description

'GLA' is used to calculate the GLA estimate for a gene triplet data.

Arguments

object

An numerical matrix object with three columns or an object of ExpresionSet class with three features.

cut

cut==M +1. M is the number of grip points pre-specifed over the third variable.

dim

An index of the column for the gene to be treated as the third controller variable. Default is dim=3

geneMap

A character vector with three elements representing the mapping between gene names and feature names (optional).

Value

'GLA' returns a numerical value representing the estimated value. A more detailed interpretation of the value is illustrated in the vignette.

Details

The input object can be a numerical matrix with three columns with row representing observations and column representing three variables. It can also be an ExpressionSet object with three features. If input a matrix class data, all three columns of the object representing the variables should have column names. Each variable in the object will be standardized with mean 0 and variance 1 in the function. In addition, the third variable will be quantile normalized within the function. More detail example about the usage of geneMap is demonstrated in the vignette.

References

Yen-Yi Ho, Leslie Cope, Thomas A. Louis, and Giovanni Parmigiani, GENERALIZED LIQUID ASSOCIATION (April 2009). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 183. http://www.bepress.com/jhubiostat/paper183

Examples

Run this code


data<-matrix(rnorm(300), ncol=3)

colnames(data)<-c("Gene1", "Gene2", "Gene3")

GLAest<-GLA(data, cut=4, dim=3)

GLAest