Hi all,
My name is Amy, I am a masters student in Bioinformatics at North Carolina
State University. I am working on a project and I am trying to use the lumi
R package for microarray data analysis. I have shown the sample code here
and have questions about modifying the sample code for my own data.


lumi package in R, example.lumi, the sample data has 8000 features and 4
samples

I have highlighted the code I have questions on in red, my data has 4
different types of samples, each repeated 6 times, so a total of 24 samples
and about 48,000 rows. how should I identify my sampleType in my case? also
what does colnames(design) <- c('100:0', '95:5-100:0') do, which columns
exactly does it take into consideration? Thanks!


so the sample code i'm trying to follow is below:

###################################################

### code chunk number 30: filtering

###################################################

presentCount <- detectionCall(example.lumi)

selDataMatrix <- dataMatrix[presentCount > 0,]

probeList <- rownames(selDataMatrix)





###################################################

### code chunk number 31: Identify differentially expressed genes

###################################################

## Specify the sample type

sampleType <- c('100:0', '95:5', '100:0', '95:5')

if (require(limma)) {

                ##  compare '95:5' and '100:0'

                design <- model.matrix(~ factor(sampleType))

                colnames(design) <- c('100:0', '95:5-100:0')

                fit <- lmFit(selDataMatrix, design)

                fit <- eBayes(fit)

                ## Add gene symbols to gene properties

                if (require(lumiHumanAll.db) & require(annotate)) {

               geneSymbol <- getSYMBOL(probeList, 'lumiHumanAll.db')

               geneName <- sapply(lookUp(probeList, 'lumiHumanAll.db',
'GENENAME'), function(x) x[1])

               fit$genes <- data.frame(ID= probeList,
geneSymbol=geneSymbol, geneName=geneName, stringsAsFactors=FALSE)

          }

                ## print the top 10 genes

                print(topTable(fit, coef='95:5-100:0', adjust='fdr',
number=10))



                ## get significant gene list with FDR adjusted p.values
less than 0.01

                p.adj <-
p.adjust(fit$p.value[,2])

                sigGene.adj <- probeList[ p.adj < 0.01]

                ## without FDR adjustment

                sigGene <- probeList[ fit$p.value[,2] < 0.01]

}

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