I have an Affymetrix single channel DNA microarray dataset where normal and diseased tissue are taken from the same organ from 5 persons. So I have 10 CEL files with me. Now I want to analyse the differential gene expression between the normal and diseased tissues. I think I should use Limma Paired samples design matrix and compute paired moderated t test. Limma user guide. 9.4.1 pg no: 42 and 43. 

Can anyone please confirm whether my approach is correct or Should I follow different approach ?

My phenodata file looks like this 

My code 

library(affy)

library(limma)

# Read all CEL FIles and put into an affybatch 
affy.data = ReadAffy()

#Importing the phenotype data
pData(affy.data) = read.table("phenodata.txt",  header=TRUE, row.names=1, as.is=TRUE)

#Visualise the phenotype data
pData(affy.data)

# Normalize the data
eset = rma(affy.data)

# DIFFERENTALLY GENE EXPRESSION ANALYSIS

library(limma)
pData(eset)
Subject <- factor(eset$Subject)

Tissue <- factor(eset$Tissue, levels = c("Normal", "Diseased"))

design <- model.matrix(~Subject+Tissue)
design

fit <- lmFit(eset, design)
eBayesfit <- eBayes(fit)

View(eBayesfit)
topTable(eBayesfit, coef="TissueDiseased")

Looks fine to me. You should probably do some filtering on abundance, though. See the relevant section of the user's guide and case studies for examples. 

P.S. limma should be a separate tag, otherwise the maintainers don't get notified.