svapls (version 1.4)

fitModel: Function to fit an ANCOVA model to the log transformed gene expression data, with a certain specified number of surrogate variables.

Description

This function begins its operation by fitting a standard ANOVA model to the gene expression data, with the gene, variety main effects and their mutual interaction. The residuals from the fit of this model and the original gene expression values are then respectively organized into two matrices E and Y, where each column corresponds to a certain gene. Now E is regressed on Y by Partial Least Squares (PLS) and a specified number of scores are extracted as the estimates of the latent components from their respective column spaces. The scores in the Y-space are used as surrogate variables along with the gene and variety interaction effects with the first score and the usual effects from the standard ANOVA model, in order to fit an ANCOVA model to the data. The function returns the results from this fit.

Usage

fitModel(k1, k2, Y, n.surr)

Arguments

k1

Number of subjects/samples under variety 1.

k2

Number of subjects/samples under variety 2.

Y

The log transformed gene expression data, with genes along the rows and subjects/samples along the columns.

n.surr

The specified number of surrogate variables.

Value

mu.hat

Intercept (general mean effect).

G.hat

Main effects of the genes.

V.hat

Main effects of the varieties.

GV.hat

Gene-Variety interaction effects.

sc

Values of the Surrogate variables (computed only when n.surr>0).

beta.hat

Coefficients of the surrogate variables (computed only when n.surr>0).

GZ1.hat

Interaction effects of the genes with the first surrogate variable (computed only when n.surr>0).

VZ1.hat

Interaction effects of the varieties with the fist surrogate variable (computed only when n.surr>0).

vhat.gvh

Variances of the estimators for the gene-variety interaction effects.

MSE

Mean Squarred Error for the fitted model.

AIC

Value of the Akaike's Information Criterion (AIC) for the fitted model.

References

Sutirtha Chakraborty, Somnath Datta and Susmita Datta. (2012) Surrogate Variable Analysis Using Partial Least Squares in Gene Expression Studies. Bioinformatics. Martens, H., Naes, T. (1989) Multivariate Calibration. Chicestor:Wiley.

See Also

svpls, hfp

Examples

data(hidden_fac.dat)

## Fitting an ANCOVA model with 5 surrogate variables
fit <- fitModel(10,10,hidden_fac.dat,n.surr = 5)
print(fit)