[BioC] Using limma to identify differentially expressed genes

[David Westergaard]

Hello,

I've been trying to use limma to identify genes from the following
data: http://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-21340 -
It's a simple control vs. disease experiment


# SDRF downloaded from above page
SDRF <- read.table(file="E-GEOD-21340.sdrf.txt",header=TRUE,stringsAsFactors=FALSE,sep="\t")

# Looking to compare Family-history negativer versus Diabetis
controls <- SDRF[grep("Control, Family History
Neg",SDRF$Comment..Sample_source_name.),]
disease <- SDRF[grep("^DM",SDRF$Characteristics.DiseaseState.),]
Batch <- rbind(controls,disease)

# Read in CEL files
mixture.batch <- ReadAffy(filenames=Batch$Array.Data.File)

# Preprocess data
mixture.processed <- expresso(mixture.batch, bgcorrect.method = "rma",
normalize.method = "quantiles", pmcorrect.method = "pmonly",
summary.method = "medianpolish")

# Get data in matrix
signals <- exprs(mixture.prepared)
cl <- ifelse(colnames(signals) %in% disease$Array.Data.File,1,0)

# Do design matrix and fit
design <- model.matrix(~factor(cl))
fit <- lmFit(signals,design)
fit <- eBayes(fit)
topTable(fit2,coef=2)

Which yields the following:
               ID  logFC AveExpr     t P.Value adj.P.Val     B
7513    208004_at -0.323    5.43 -4.65 0.00191     0.999 -3.10
11225 211829_s_at  0.340    5.07  4.36 0.00278     0.999 -3.17
5950    206424_at -0.907    6.65 -4.15 0.00363     0.999 -3.23
1354  201826_s_at -0.447    8.37 -4.13 0.00374     0.999 -3.24
19782   220418_at  0.392    5.43  4.02 0.00431     0.999 -3.27
8889  209396_s_at  1.899    7.47  4.01 0.00437     0.999 -3.28
5005    205478_at -0.931    9.22 -3.94 0.00481     0.999 -3.30
9469  209983_s_at  0.412    5.72  3.92 0.00492     0.999 -3.31
2936    203409_at  0.506    6.93  3.87 0.00531     0.999 -3.32
5054  205527_s_at  0.331    6.80  3.84 0.00549     0.999 -3.33

I'm abit puzzled over the adjusted P-values. Can it really be true
that ALL of the adjusted P-values are 0.999, or did I make a rookie
mistake somewhere?

Best Regards,
David Westergaard