gistfile1.r

 Try to de-convolute the array relationships by looking at the SNP
# probes
snpProbes <- grep('rs', fData(lumiData)$TargetID)
betas <- estimateBeta(lumiData)
betasSNP <- exprs(betas)[snpProbes, ]

labels <- paste(pData(lumiData)$Subject,
                pData(lumiData)$sampleID,
                pData(lumiData)$Tissue,
                sep='_')
colnames(betasSNP) <- labels  

# Select a subet of sample for pair plot
subject <- c('1', '2')
subjectSamples <- pData(lumiData)$Subject %in% subject

# Plot pair plot with the dots in scatter plot colored based on the color channels
png('subject_1_2_SNP.png', width=1200, height=1200)
pairs(betas[snpProbes, subjectSamples], 
      main='Pair plot of M-value before normalization')
dev.off()

# Plot overall sample identities  
pheatmap(cor(betasSNP),
         cellheight=16,
         cellwidth=16,
         file='heatmap.pdf')