svapls (version 1.0)

hfp: Function to construct a heatmap of the hidden variation in the gene expression data.

Description

The function hfp produces a plot of the PLS imputed estimate of the hidden variability in the data, derived from the optimal model, corresponding to an user-specified set of genes and subjects/samples.

Usage

hfp(obj, gen, ind, Y)

Arguments

obj

An svpls object.

gen

An user-specified set of genes.

ind

An user-specified set of subjects.

Y

A log transformed gene expression matrix with genes along the rows and subjects/samples along the columns.

Value

• A heatmap of the hidden variability corresponding to the specified set of genes and subjects, attributable to the unknown subject-specific factors in the gene expression data.

References

Sutirtha Chakraborty, Somnath Datta and Susmita Datta. (2012) Surrogate Variable Analysis Using Partial Least Squares in Gene Expression Studies. Bioinformatics.

See Also

heatmap, fitModel, svpls

Examples

## Fitting the optimal ANCOVA model to the data gives:
data(hidden_fac.dat)
fit <- svpls(10,10,hidden_fac.dat,pmax = 5)

## Specifying the set of genes and subjects
genes <- c(1,20,55,70,100,150,250,450)
subjects <- c(1,4,7,10,11,15,17,20)

hfp(fit,genes,subjects,hidden_fac.dat)