[R] Help with the lumi R package

From: Minyue Wang <mwang3_at_ncsu.edu>
Date: Fri, 30 Mar 2012 12:15:26 -0400


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]

}

-- 
- Amy W.

-- 
Minyue Wang (Amy)
Graduate student, Bioinformatics
mwang3_at_ncsu.edu
919-5210893

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Received on Fri 30 Mar 2012 - 18:01:45 GMT

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