Hi everyone,

I am new to microarray data and I am having a really hard time finding a good work flow so far. My experiment is with human transcriptome data. I have 4 time points and I am trying to find DEG between them. This is just a test run with two times points. I have no idea what to do and so far my script hasn't worked so well. This is what I have so far. Thanks in advance. My volcano plot also looks really weird. 

source("http://bioconductor.org/biocLite.R")
biocLite("oligo")
biocLite("limma")
biocLite("pd.hugene.2.1.st")
library(genefilter)
library(limma)
library(oligo)
library(pd.hugene.2.0.st)

#directory for the data
mydir <- "C:\\Users\\hakim\\Desktop\\Bioinformatics_Thesis_Concordia_Microarry_Data\\Control"

#setting seed for reproducibility
set.seed(1)
#listing the files from directory using special CEL file read function
celList <- list.celfiles(mydir, full.names=TRUE)
#reading data from cellist and setting annotation package to approiate one for this microarray
rawData <- read.celfiles(celList, pkgname='pd.hugene.2.0.st')

#normalizing the data using RMA algorithm
normData <- rma(rawData)
#checking boxplot of raw data
par(mar=c(10,4.5,2,1))
boxplot(rawData,las=3)
#checking boxplot of normalized data
boxplot(normData,las=3)

#the annotation package
biocLite("hugene20sttranscriptcluster.db")

library(hugene20sttranscriptcluster.db)

#retreaving feature data

featureData(normData) <- getNetAffx(normData, "transcript")

design = cbind(control = 1, controlvstreatment = c((rep.int(0,13)),rep.int(1,13)))
fit<- lmFit(normData, design)
normfit <-eBayes(fit)

omg <- topTable(normfit, coef="controlvstreatment", adjust="BH")

write.table(omg,file="finally.txt",sep= "/" )

results <- decideTests(fit)
vennDiagram(results)

volcanoplot(normfit)

For starters, it makes no sense to do the control contrast. You're effectively testing for whether the intercept is equal to zero for each gene, i.e., whether gene expression is equal to unity. This is a nonsensical contrast as you wouldn't expect that they would have expression values equal to 1; as a result, every gene will probably have massively positive log-fold changes. This shows up in your comment as having all genes with positive log-fold changes in your summary and on your volcano plot. You should only interpret the controlvstreatment column of the decideTests output, and you should set coef=2 in your volcanoplot call.