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https://github.com/zhengxwen/snprelate
R package: parallel computing toolset for relatedness and principal component analysis of SNP data (Development version only)
https://github.com/zhengxwen/snprelate
bioinformatics gds-format pca r simd snp
Last synced: 6 days ago
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R package: parallel computing toolset for relatedness and principal component analysis of SNP data (Development version only)
- Host: GitHub
- URL: https://github.com/zhengxwen/snprelate
- Owner: zhengxwen
- Created: 2014-06-08T05:00:50.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2024-11-14T21:51:54.000Z (about 1 month ago)
- Last Synced: 2024-12-14T20:02:33.143Z (13 days ago)
- Topics: bioinformatics, gds-format, pca, r, simd, snp
- Language: C++
- Homepage: http://www.bioconductor.org/packages/SNPRelate
- Size: 18 MB
- Stars: 103
- Watchers: 13
- Forks: 25
- Open Issues: 43
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
Awesome Lists containing this project
README
SNPRelate: Parallel computing toolset for relatedness and principal component analysis of SNP data
====
[GNU General Public License, GPLv3](http://www.gnu.org/copyleft/gpl.html)
[](http://www.bioconductor.org/packages/release/bioc/html/SNPRelate.html)
[](http://www.bioconductor.org/packages/release/bioc/html/SNPRelate.html)
[](https://github.com/zhengxwen/SNPRelate/actions/workflows/r.yml)
## Features
Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed SNPRelate (R package for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized.
The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. The SNP GDS format in this package is also used by the [GWASTools](http://bioconductor.org/packages/GWASTools) package with the support of S4 classes and generic functions. The extended GDS format is implemented in the [SeqArray](https://github.com/zhengxwen/SeqArray) package to support the storage of single nucleotide variation (SNV), insertion/deletion polymorphism (indel) and structural variation calls. It is strongly suggested to use [SeqArray](https://github.com/zhengxwen/SeqArray) for large-scale whole-exome and whole-genome sequencing variant data instead of [SNPRelate](https://github.com/zhengxwen/SNPRelate).
## Bioconductor
Release Version: v1.40.0
[http://www.bioconductor.org/packages/SNPRelate](http://www.bioconductor.org/packages/SNPRelate)
## News
* See [package news](NEWS).
## Tutorials
[http://www.bioconductor.org/packages/release/bioc/vignettes/SNPRelate/inst/doc/SNPRelate.html](http://www.bioconductor.org/packages/release/bioc/vignettes/SNPRelate/inst/doc/SNPRelate.html)
## Citations
Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012). A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. *Bioinformatics*. [DOI: 10.1093/bioinformatics/bts606](http://dx.doi.org/10.1093/bioinformatics/bts606).
Zheng X, Gogarten S, Lawrence M, Stilp A, Conomos M, Weir BS, Laurie C, Levine D (2017). SeqArray -- A storage-efficient high-performance data format for WGS variant calls. *Bioinformatics*. [DOI: 10.1093/bioinformatics/btx145](http://dx.doi.org/10.1093/bioinformatics/btx145).
## Installation
* Bioconductor repository:
```R
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("SNPRelate")
```
* Development version from Github (for developers/testers only):
```R
library("devtools")
install_github("zhengxwen/gdsfmt")
install_github("zhengxwen/SNPRelate")
```
The `install_github()` approach requires that you build from source, i.e. `make` and compilers must be installed on your system -- see the [R FAQ](https://cran.r-project.org/faqs.html) for your operating system; you may also need to install dependencies manually.
## Implementation with Intel Intrinsics
| Functions | No SIMD | SSE2 | AVX | AVX2 | AVX-512 |
|:----------------------|:-------:|:----:|:---:|:----:|:-------:|
| snpgdsDiss [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsDiss.html) | X |
| snpgdsEIGMIX [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsEIGMIX.html) | X | X | X |
| snpgdsGRM [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsGRM.html) | X | X | X | . |
| snpgdsIBDKING [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIBDKING.html) | X | X | | X |
| snpgdsIBDMoM [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIBDMoM.html) | X |
| snpgdsIBS [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIBS.html) | X | X |
| snpgdsIBSNum [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIBSNum.html) | X | X |
| snpgdsIndivBeta [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIndivBeta.html) | X | X | P | X |
| snpgdsPCA [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsPCA.html) | X | X | X |
| snpgdsPCACorr [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsPCACorr.html) | X |
| snpgdsPCASampLoading [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsPCASampLoading.html) | X |
| snpgdsPCASNPLoading [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsPCASNPLoading.html) | X |
| [...](http://rdrr.io/bioc/SNPRelate/man) |
`X: fully supported; .: partially supported; P: POPCNT instruction.`
### Install the package from the source code with the support of Intel SIMD Intrinsics:
You have to customize the package compilation, see: [CRAN: Customizing-package-compilation](https://cran.r-project.org/doc/manuals/r-release/R-admin.html#Customizing-package-compilation)
Change `~/.R/Makevars` to, assuming GNU Compilers (gcc/g++) or Clang compiler (clang++) are installed:
```sh
## for C code
CFLAGS=-g -O3 -march=native -mtune=native
## for C++ code
CXXFLAGS=-g -O3 -march=native -mtune=native
```