RLMM: A Genotype Calling Algorithm for Affymetrix SNP Arrays

A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now.

Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("RLMM")
AuthorNusrat Rabbee <nrabbee@post.harvard.edu>, Gary Wong <wongg62@berkeley.edu>
Bioconductor views GeneticVariability Microarray OneChannel SNP
Date of publicationNone
MaintainerNusrat Rabbee <nrabbee@post.harvard.edu>
LicenseLGPL (>= 2)
Version1.36.0
http://www.stat.berkeley.edu/users/nrabbee/RLMM

View on Bioconductor

Files

DESCRIPTION
NAMESPACE
R
R/Classify.R R/create_Thetafile.R R/normalize_Rawfiles.R R/plot_theta.R
build
build/vignette.rds
inst
inst/doc
inst/doc/RLMM.R
inst/doc/RLMM.Rnw
inst/doc/RLMM.pdf
man
man/Classify.Rd man/create_Thetafile.Rd man/normalize_Rawfiles.Rd man/plot_theta.Rd
vignettes
vignettes/RLMM.Rnw
vignettes/Xba.rlmm
vignettes/Xba.theta
vignettes/snps.lst

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