RLMM: A Genotype Calling Algorithm for Affymetrix SNP Arrays
Version 1.38.0

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.

Browse man pages Browse package API and functions Browse package files

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)
Version1.38.0
URL http://www.stat.berkeley.edu/users/nrabbee/RLMM
Package repositoryView on Bioconductor
InstallationInstall the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("RLMM")

Man pages

Classify: Classification of SNPs based on theta estimates
create_Thetafile: Calculating Parameter Estimates
normalize_Rawfiles: Normalize PM Intensity values
plot_theta: Allele Summary Plot

Functions

Classify Man page Source code
create_Thetafile Man page Source code
normalize_Rawfiles Man page Source code
plot_theta Man page Source code

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
RLMM documentation built on May 20, 2017, 9:55 p.m.