Description Usage Arguments Details Value Examples
View source: R/snprma-functions.R
SNPRMA will preprocess SNP chips. The preprocessing consists of quantile normalization to a known target distribution and summarization to the SNP-Allele level.
1 2 |
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 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | 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)
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