svapls (version 1.1)

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 (SVA-PLS) in Gene Expression Studies. Bioinformatics, 28(6): 799-806.

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)