To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RLMM")
In most cases, you don't need to download the package archive at all.
RLMM
A Genotype Calling Algorithm for Affymetrix SNP Arrays
Bioconductor version: 2.14
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.
Author: Nusrat Rabbee <nrabbee at post.harvard.edu>, Gary Wong <wongg62 at berkeley.edu>
Maintainer: Nusrat Rabbee <nrabbee at post.harvard.edu>
Citation (from within R,
enter citation("RLMM")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RLMM")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("RLMM")
| R Script | RLMM Doc | |
| Reference Manual |
Details
| biocViews | GeneticVariability, Microarray, OneChannel, SNP, Software |
| Version | 1.26.0 |
| In Bioconductor since | BioC 1.8 (R-2.3) |
| License | LGPL (>= 2) |
| Depends | R (>= 2.1.0) |
| Imports | graphics, grDevices, MASS, stats, utils |
| Suggests | |
| System Requirements | Internal files Xba.CQV, Xba.regions (or other regions file) |
| URL | http://www.stat.berkeley.edu/users/nrabbee/RLMM |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | RLMM_1.26.0.tar.gz |
| Windows Binary | RLMM_1.26.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | RLMM_1.26.0.tgz |
| Mac OS X 10.9 (Mavericks) | RLMM_1.26.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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