LMGene (version 2.28.0)

LMGene: LMGene main function

Description

LMGene calls function genediff to calculate the unadjusted gene-specific and posterior p-values of all genes and then calculates the FDR-adjusted p-values of all genes. Significant genes for each factor in model (based on either the gene-specific or posterior FDR-adjusted p-values) are output.

Usage

LMGene(eS, model = NULL, level = 0.05, posterior = FALSE, 
method = c("MLE", "MOM", "MOMlog"))

Arguments

eS

An ExpressionSet object. Any transformation and normalization of exprs(eS) should be conducted prior to use in LMGene.

model

Specifies model to be used. Default is to use all variables from eS without interactions. See details.

level

Significance level

posterior

If TRUE, the posterior FDR-adjusted p-values are used in listing significant genes for each factor. Default is to use gene-specific FDR-adjusted p-values.

method

Method by which the posterior p-values are calculated. Default is "MLE".

Value

lmres

A list with one component for each factor in model. Each component consists of a character vector with one element per significant gene. If no genes are significant for a given factor, the component for that factor is set to "No significant genes".

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 level argument indicates the False Discovery Rate, e.g. level=0.05 means a 5 percent FDR.

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.

See genediff for details of method.

References

Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society, Series B, 57, 289--300.

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, neweS

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)

# Identify significant genes, using an FDR of 1 percent
LMGene(trans.eS, level = 0.01)