Package: GWASbyCluster 0.1.7

Li Xing

GWASbyCluster: Identifying Significant SNPs in Genome Wide Association Studies (GWAS) via Clustering

Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) <doi:10.1038/s41598-019-50229-6>.

Authors:Yan Xu, Li Xing, Jessica Su, Xuekui Zhang<[email protected]>, Weiliang Qiu <[email protected]>

GWASbyCluster_0.1.7.tar.gz
GWASbyCluster_0.1.7.zip(r-4.7-any)GWASbyCluster_0.1.7.zip(r-4.6-any)GWASbyCluster_0.1.7.zip(r-4.5-any)
GWASbyCluster_0.1.7.tgz(r-4.6-any)GWASbyCluster_0.1.7.tgz(r-4.5-any)
GWASbyCluster_0.1.7.tar.gz(r-4.7-any)GWASbyCluster_0.1.7.tar.gz(r-4.6-any)
GWASbyCluster_0.1.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
GWASbyCluster/json (API)

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:
  • esSim - An ExpressionSet Object Storing Simulated Genotype Data
  • esSimDiffPriors - An ExpressionSet Object Storing Simulated Genotype Data

On CRAN:

Conda:

archivedpackagesr-universe

1.70 score 5 stars 286 downloads 4 exports 10 dependencies

Last updated from:a20d6c41b5 (on package/GWASbyCluster). Checks:7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develNOTE122
source / vignettesOK204
linux-releaseNOTE160
macos-releaseNOTE132
macos-oldrelNOTE115
windows-develNOTE109
windows-releaseNOTE83
windows-oldrelNOTE68
wasm-releaseOK85

Exports:estMemSNPsestMemSNPs.oneSetHyperParasimGenoFuncsimGenoFuncDiffPriors

Dependencies:BiobaseBiocGenericsgenericslatticelimmaMatrixrootSolvesnpStatsstatmodsurvival