To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("trio")
In most cases, you don't need to download the package archive at all.
trio
Testing of SNPs and SNP Interactions in Case-Parent Trio Studies
Bioconductor version: 2.14
Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data.
Author: Holger Schwender, Qing Li, Christoph Neumann, Philipp Berger, Margaret Taub, Ingo Ruczinski
Maintainer: Holger Schwender <holger.schw at gmx.de>
Citation (from within R,
enter citation("trio")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("trio")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("trio")
| R Script | Trio Logic Regression and genotypic TDT | |
| Reference Manual |
Details
| biocViews | GeneticVariability, Genetics, Microarray, SNP, Software |
| Version | 3.2.1 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | LGPL-2 |
| Depends | R (>= 3.0.1) |
| Imports | |
| Suggests | survival, haplo.stats, mcbiopi, siggenes, splines, LogicReg (>= 1.5.3), logicFS(>= 1.28.1), KernSmooth, VariantAnnotation |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | trio_3.2.1.tar.gz |
| Windows Binary | trio_3.2.1.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | trio_3.2.1.tgz |
| Mac OS X 10.9 (Mavericks) | trio_3.2.1.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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