How do I extract normalized signal values for Affy SNP 6.0 chip using oligo or crlmm?
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Entering edit mode
3.8 years ago
shawn.w.foley ★ 1.3k

Taking publicly available Affy SNP6.0 data, I am trying to find the normalized signal for each probe. I've used both the "oligo" and "crlmm" from Bioconductor, and these generate a SnpSuperSet variable where I can then use calls(x) to find the genotype (AA=1, AB=2, or BB=3) or I can use confs(x) to find the p-value for this call. My code and output is below.

Rather than the calls themselves, I'd like to go one step back, and extract a matrix of signal intensity, so that I can go in and make the genotype calls myself. The publication states that there can be as little as 40% tumor DNA in the sample, therefore I'm concerned that crlmm is making erroneous calls (since there is an overrepresentation of "normal" tissue in the sample).

Thank you for the help!

library(oligo)
celFiles <- list.celfiles(celDirectory,full.names=T)
crlmm(celFiles,outDir)
x <- getCrlmmSummaries(outDir)

print(x)
SnpSuperSet (storageMode: lockedEnvironment)
assayData: 906600 features, 17 samples
element names: alleleA, alleleB, call, callProbability, F
protocolData: none
phenoData
rowNames: 1 2 ... 17 (17 total)
varLabels: crlmmSNR
featureData: none
experimentData: use 'experimentData(object)'
Annotation: pd.genomewidesnp.6

calls (x)
A B C D E
SNP_A-1780270 3 3 2 2 3
SNP_A-1780271 1 1 2 1 1
SNP_A-1780272 3 3 2 3 3
SNP_A-1780274 2 2 2 2 2
SNP_A-1780277 1 1 2 1 2
SNP_A-1780278 3 3 3 3 3

confs (x)
A            B            C            D            E
SNP_A-1780270 0.0009991370 0.0009989767 0.0009974529 0.0009988142 0.0009991313
SNP_A-1780271 0.0009699487 0.0009970648 0.0009924274 0.0009945284 0.0009670124
SNP_A-1780272 0.0009991341 0.0009950610 0.0009926793 0.0009992551 0.0009990913
SNP_A-1780274 0.0009923162 0.0009886114 0.0009978304 0.0009943256 0.0009975597
SNP_A-1780277 0.0009973942 0.0009951006 0.0009981321 0.0008798894 0.0009976221
SNP_A-1780278 0.0009932014 0.0009901400 0.0009991727 0.0009992094 0.0009988690

SNP bioconductor oligos crlmm • 1.2k views
1
Entering edit mode
3.8 years ago
shawn.w.foley ★ 1.3k

After more searching I found the answer to my question, I'm posting it here in case someone needs to do a similar analysis in the future. The answer to this question is absent from the crlmm genotyping vignette, however if you look through the full vignette here, you can find the appropriate analysis. The snprma function takes CEL files and preprocesses them. Then you can simply take the normalized intensities for A and B and compare them.

library(crlmm)
library(genomewidesnp6Crlmm)
celFiles <- list.celfiles(celDirectory,full.names=T)
snpData <- snprma(celFiles)

head(snpData$A) [,1] [,2] [1,] 1631 641 [2,] 252 1021 [3,] 891 833 [4,] 1825 572 [5,] 2005 1901 [6,] 1206 1623 head(snpData$B)
[,1] [,2]
[1,] 1430 2843
[2,]  843  292
[3,] 3585 3552
[4,] 1373 1925
[5,]  286  241
[6,]  951  270

[1] "SNP_A-2131660" "SNP_A-1967418" "SNP_A-1969580" "SNP_A-4263484"
[5] "SNP_A-1978185" "SNP_A-4264431"