select.sig.gene: select differentially expressed genes based on p value and/or...

In AffyExpress: Affymetrix Quality Assessment and Analysis Tool

Description

select differentially expressed genes based on p value and/or fold change

Usage

select.sig.gene(top.table, p.value =0.05, m.value =0)  

Arguments

top.table	an data frame returned from the regress function
p.value	p value, the default value is 0.05
m.value	fold change cut-off value, default value is 0

Value

A dataframe which is the similar to the one returned from regress function. An additional column 'significant' is added to the table from the "regress" function. If p value < p.value and absolute of fold change value >=m.value then signiicant = TRUE, otherwise, significant = FALSE.

Author(s)

Xiwei Wu [e-mail], Xuejun Arthur Li [e-mail]

Examples

data(testData)
normaldata<-pre.process("rma",testData)
## Create design matrix
design<-make.design(pData(normaldata), "group")

## Create contrast matrix - Compare group "A" vs. "C"
contrast<-make.contrast(design, "A", "C")

## Identify differentially expressed gene by using LIMMA method
result<-regress(normaldata, design, contrast, "L")

## Select differentially expressed gene based on p <0.05 and 
## fold change >=log2(1.5)
select<-select.sig.gene(result, p.value=0.05, m.value=log2(1.5))

AffyExpress documentation built on Nov. 8, 2020, 7:50 p.m.