snprma {crlmm} | R Documentation |
SNPRMA will preprocess SNP chips. The preprocessing consists of quantile normalization to a known target distribution and summarization to the SNP-Allele level.
snprma(filenames, mixtureSampleSize = 10^5, fitMixture = FALSE, eps = 0.1, verbose = TRUE, seed = 1, cdfName, sns) snprma2(filenames, mixtureSampleSize = 10^5, fitMixture = FALSE, eps = 0.1, verbose = TRUE, seed = 1, cdfName, sns)
filenames |
'character' vector with file names. |
mixtureSampleSize |
Sample size to be use when fitting the mixture model. |
fitMixture |
'logical'. Fit the mixture model? |
eps |
Stop criteria. |
verbose |
'logical'. |
seed |
Seed to be used when sampling. |
cdfName |
cdfName: 'GenomeWideSnp\_5', 'GenomeWideSnp\_6' |
sns |
Sample names. |
'snprma2' allows one to genotype very large datasets (via ff package) and also permits the use of clusters or multiple cores (via snow package) to speed up preprocessing.
A |
Summarized intensities for Allele A |
B |
Summarized intensities for Allele B |
sns |
Sample names |
gns |
SNP names |
SNR |
Signal-to-noise ratio |
SKW |
Skewness |
mixtureParams |
Parameters from mixture model |
cdfName |
Name of the CDF |
if (require(genomewidesnp6Crlmm) & require(hapmapsnp6) & require(oligoClasses)){ path <- system.file("celFiles", package="hapmapsnp6") ## the filenames with full path... ## very useful when genotyping samples not in the working directory cels <- list.celfiles(path, full.names=TRUE) snprmaOutput <- snprma(cels) snprmaOutput[["A"]][1:10,] snprmaOutput[["B"]][1:10,] } ## Not run: ## HPC Example library(ff) library(snow) library(crlmm) ## genotype 50K SNPs at a time ocProbesets(50000) ## setup cluster - 8 cores on the machine setCluster(8, "SOCK") path <- system.file("celFiles", package="hapmapsnp6") cels <- list.celfiles(path, full.names=TRUE) snprmaOutput <- snprma2(cels) ## End(Not run)