Package: rMVP 1.4.6

Xiaolei Liu

rMVP: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) <doi:10.1038/ng1847>), MLM (Jianming Yu (2006) <doi:10.1038/ng1702>) and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX (Hyunmin Kang (2008) <doi:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <doi:10.1038/nmeth.1681>, R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <doi:10.1371/journal.pone.0107684> and 'SUPER': Qishan Wang and Feng Tian (2014) <doi:10.1371/journal.pone.0107684>), and HE regression (Xiang Zhou (2017) <doi:10.1214/17-AOAS1052>).

Authors:Lilin Yin [aut], Haohao Zhang [aut], Zhenshuang Tang [aut], Jingya Xu [aut], Dong Yin [aut], Zhiwu Zhang [aut], Xiaohui Yuan [aut], Mengjin Zhu [aut], Shuhong Zhao [aut], Xinyun Li [aut], Qishan Wang [ctb], Feng Tian [ctb], Hyunmin Kang [ctb], Xiang Zhou [ctb], Xiaolei Liu [cre, aut, cph]

rMVP_1.4.6.tar.gz
rMVP_1.4.6.tar.gz(r-4.7-arm64)rMVP_1.4.6.tar.gz(r-4.7-x86_64)rMVP_1.4.6.tar.gz(r-4.6-arm64)rMVP_1.4.6.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
rMVP/json (API)

# Install 'rMVP' in R:
install.packages('rMVP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/xiaolei-lab/rmvp/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • pig60K - Genotyped by pig 60k chip

On CRAN:

Conda:

openblascppopenmp

3.15 score 1 stars 56 scripts 1.3k downloads 12 mentions 32 exports 10 dependencies

Last updated from:92fe146c00. Checks:5 OK, 1 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK228
linux-devel-x86_64OK168
source / vignettesOK246
linux-release-arm64OK235
linux-release-x86_64OK189
wasm-releaseFAIL156

Exports:as.big.matrixattach.big.matrixdeepcopyis.big.matrixMVPMVP.BRENT.Vg.VeMVP.DataMVP.Data.Bfile2MVPMVP.Data.Hapmap2MVPMVP.Data.imputeMVP.Data.KinMVP.Data.MapMVP.Data.MVP2BfileMVP.Data.Numeric2MVPMVP.Data.PCMVP.Data.PhenoMVP.Data.VCF2MVPMVP.EMMA.Vg.VeMVP.FarmCPUMVP.FaSTLMM.LLMVP.GLMMVP.HE.Vg.VeMVP.HistMVP.K.VanRadenMVP.MLMMVP.PCAMVP.PCAplotMVP.ReportMVP.Report.DensityMVP.Report.QQplotMVP.Versionnew

Dependencies:BHbigmemorybigmemory.sriMASSRcppRcppArmadilloRcppEigenRcppProgressRhpcBLASctluuid

Readme and manuals

Help Manual

Help pageTopics
MVP, Memory-efficient, Visualization-enhanced, Parallel-acceleratedMVP
MVP.BRENT.Vg.Ve variance component estimation using the BRENT methodMVP.BRENT.Vg.Ve
MVP.Data: To prepare data for MVP packageMVP.Data
MVP.Data.Bfile2MVP: To transform plink binary data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Bfile2MVP
MVP.Data.Hapmap2MVP: To transform Hapmap data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Hapmap2MVP
MVP.Data.impute: To impute the missing genotype Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.impute
KinshipMVP.Data.Kin
MVP.Data.Map: To check map file Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Map
MVP.Data.MVP2Bfile: To transform MVP data to binary format Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.MVP2Bfile
MVP.Data.Numeric2MVP: To transform Numeric data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Numeric2MVP
Principal component analysisMVP.Data.PC
MVP.Data.Pheno: To clean up phenotype file Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Pheno
MVP.Data.VCF2MVP: To transform vcf data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.VCF2MVP
Estimate variance components using EMMAMVP.EMMA.Vg.Ve
Perform GWAS using FarmCPU methodMVP.FarmCPU
Evaluation of the maximum likelihood using FaST-LMM methodMVP.FaSTLMM.LL
To perform GWAS with GLM and MLM model and get the P value of SNPsMVP.GLM
To estimate variance component using HE regressionMVP.HE.Vg.Ve
Phenotype distribution histogramMVP.Hist
Calculate Kinship matrix by VanRaden methodMVP.K.VanRaden
To perform GWAS with GLM and MLM model and get the P value of SNPsMVP.MLM
Principal Component AnalysisMVP.PCA
PCA PlotMVP.PCAplot
MVP.ReportMVP.Report
SNP DensityMVP.Report.Density
QQ PlotMVP.Report.QQplot
Print MVP BannerMVP.Version
Genotyped by pig 60k chippig60K