rowaov {LMGene}R Documentation

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 AArray data. must be an ExpressionSet object and the log-transformation and the normalization of exprs(eS) are recommended.
model Model used for comparison. See details and LMGene.

Details

The argument eS must be an ExpressionSet object from the Biobase package. If you have a data in a matrix and information about the considered factors, then you can use neweS to convert the data into an ExpressionSet object. Please see neweS in 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.

Value

resmat A matrix of MSE and DF of all factors for all genes.

Author(s)

David Rocke and Geun-Cheol Lee

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://www.idav.ucdavis.edu/~dmrocke/

See Also

genediff, mlm2lm

Examples

#library
library(Biobase)
library(LMGene)

#data
data(sample.mat)
data(vlist)
LoggedSmpd0 <- neweS(lnorm(log(sample.mat)),vlist)

resmat <- rowaov(LoggedSmpd0)
resmat[,1:3]

[Package LMGene version 1.12.0 Index]