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- GWASTools
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GWASTools")
In most cases, you don't need to download the package archive at all.
GWASTools
Tools for Genome Wide Association Studies
Bioconductor version: 2.14
Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis.
Author: Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp
Maintainer: Stephanie M. Gogarten <sdmorris at u.washington.edu>, Adrienne Stilp <amstilp at u.washington.edu>
Citation (from within R,
enter citation("GWASTools")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GWASTools")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GWASTools")
| R Script | Converting VCF data for use in GWASTools | |
| R Script | Data formats in GWASTools | |
| R Script | GWAS Data Cleaning | |
| R Script | Preparing Affymetrix Data | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GeneticVariability, Microarray, QualityControl, SNP, Software |
| Version | 1.10.1 |
| In Bioconductor since | BioC 2.9 (R-2.14) |
| License | Artistic-2.0 |
| Depends | Biobase, ncdf, gdsfmt, sandwich |
| Imports | methods, DBI, RSQLite, GWASExactHW, DNAcopy, survival, lmtest, quantsmooth |
| Suggests | GWASdata, BiocGenerics, RUnit, SNPRelate, snpStats, VariantAnnotation |
| System Requirements | |
| URL | |
| Depends On Me | GWASdata |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | GWASTools_1.10.1.tar.gz |
| Windows Binary | GWASTools_1.10.1.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | GWASTools_1.10.1.tgz |
| Mac OS X 10.9 (Mavericks) | GWASTools_1.10.1.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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