LMGene (version 2.28.0)

rowaov: Gene by gene ANOVA function

Description

Computes the mean squares and degrees of freedom for gene-by-gene ANOVAs.

Usage

rowaov(eS, model=NULL)

Arguments

eS

An ExpressionSet object. Any transformation and normalization of eS should be done prior to use in rowaov.

model

Model used for comparison. See details and LMGene.

Value

resmat

A matrix of MSEs and degrees of freedom for all model factors and all genes. The first rows of resmat contain MSE's for each effect in model, ending with the residual MSE. The remaining rows contain degrees of freedom for each effect in the model, ending with the residual d.f. Each column corresponds to a gene.

Details

If you have data in a matrix and information about experimental design factors, then you can use neweS to convert the data into an ExpressionSet object. Please see neweS for more detail.

The model argument is an optional character string, constructed like the right-hand side of a formula for lm. It specifies which of the variables in the ExpressionSet will be used in the model and whether interaction terms will be included. If model=NULL, it uses all variables from the ExpressionSet without interactions. Be careful of using interaction terms with factors; this often leads to overfitting, which will yield an error.

References

David M. Rocke (2004), Design and analysis of experiments with high throughput biological assay data, Seminars in Cell & Developmental Biology, 15, 703--713.

http://dmrocke.ucdavis.edu

See Also

genediff, LMGene

Examples

library(Biobase)
library(LMGene)

#data
data(sample.mat)
data(vlist)

raw.eS <- neweS(sample.mat, vlist)

# glog transform data
trans.eS <- transeS(raw.eS, lambda = 727, alpha = 56)

# Perform gene-by-gene anova
resmat <- rowaov(trans.eS)
resmat[,1:3]