Question: How do I extract normalized signal values for Affy SNP 6.0 chip using oligo or crlmm?
0
gravatar for shawn.w.foley
9 months ago by
shawn.w.foley110
shawn.w.foley110 wrote:

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)
rawData <- read.celfiles(celFiles)
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
      varMetadata: labelDescription
    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
oligos snp crlmm bioconductor • 294 views
ADD COMMENTlink modified 9 months ago • written 9 months ago by shawn.w.foley110
1
gravatar for shawn.w.foley
9 months ago by
shawn.w.foley110
shawn.w.foley110 wrote:

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

head(snpData$gns)
[1] "SNP_A-2131660" "SNP_A-1967418" "SNP_A-1969580" "SNP_A-4263484"
[5] "SNP_A-1978185" "SNP_A-4264431"
ADD COMMENTlink written 9 months ago by shawn.w.foley110
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1358 users visited in the last hour