Package: Ravages 1.2.0

Ozvan Bocher

Ravages: Rare Variant Analysis and Genetic Simulations

Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 <doi:10.1371/journal.pgen.1009923>). Ravages also enables to perform genetic simulations (Bocher et al. 2023 <doi:10.1002/gepi.22529>).

Authors:Ozvan Bocher [aut, cre], Hervé Perdry [aut], Gaelle Marenne [aut]

Ravages_1.2.0.tar.gz
Ravages_1.2.0.tar.gz(r-4.7-arm64)Ravages_1.2.0.tar.gz(r-4.7-x86_64)Ravages_1.2.0.tar.gz(r-4.6-arm64)Ravages_1.2.0.tar.gz(r-4.6-x86_64)
Ravages_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Ravages/json (API)

# Install 'Ravages' in R:
install.packages('Ravages', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.30 score 2 scripts 223 downloads 34 exports 50 dependencies

Last updated from:23f7cac283. Checks:4 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING261
linux-devel-x86_64WARNING255
source / vignettesOK394
linux-release-arm64WARNING258
linux-release-x86_64WARNING269
wasm-releaseOK215

Exports:adjustedCADD.annotationadjustedCADD.annotation.indelsadjustedCADD.annotation.SNVsbed.matrix.split.genomic.regionburdenburden.continuousburden.continuous.subscoresburden.mlogitburden.mlogit.subscoresburden.subscoresburden.weighted.matrixCASTfilter.adjustedCADDfilter.rare.variantsgenotypic.freqGRR.matrixJaccardmultinomial.asso.freqNullObject.parametersRAVA.FIRSTrbm.GRRrbm.GRR.powerrbm.haplos.freqsrbm.haplos.powerrbm.haplos.thresholdsset.CADDregionsset.genomic.regionset.genomic.region.subregionSKATSKAT.bootstrapSKAT.continuousSKAT.permutationsSKAT.theoreticalWSS

Dependencies:bedrBHbriocallrclicrayoncurldata.tabledescdfidxdiffobjevaluateformatRFormulafsfutile.loggerfutile.optionsgastongluejsonlitelambda.rlatticelifecyclelmtestmagrittrMASSmlogitpkgbuildpkgloadpraiseprocessxpsR.methodsS3R.ooR.utilsR6rbibutilsRcppRcppEigenRcppParallelRdpackrlangrprojrootstatmodtestthatVennDiagramwaldowithryamlzoo

Package Ravages (RAre Variant Analysis and GEnetic Simulation), Simulations
Introduction | Global parameters of Ravages | Simulations based on allelic frequencies and GRR | Calculation of frequencies in each group of individuals | Simulation of genotypes | Simulations based on haplotypes | Power calculation
Rendered fromRavages_Simulations_vignette.Rmdusingknitr::rmarkdown

Last update: 2026-02-27
Started: 2020-11-11

Package Ravages (RAre Variant Analysis and GEnetic Simulation)
Introduction | Global parameters of Ravages | Example of analysis using LCT data | Defining genomic regions | Rare variant selection | Rare variant association tests | Genetic score for burden tests | CAST | WSS | Other genetic scores | Regressions | SKAT | RAVA-FIRST (RAre Variant Analysis using Functionally-InfoRmed STeps) | Data management
Rendered fromRavages_vignette.Rmdusingknitr::rmarkdown

Last update: 2026-02-27
Started: 2020-11-11

Readme and manuals

Help Manual

Help pageTopics
SNVs and Indels annotation with adjusted CADD scoresadjustedCADD.annotation
Indels annotation with adjusted CADD scoresadjustedCADD.annotation.indels
SNVs annotation with adjusted CADD scoresadjustedCADD.annotation.SNVs
Bed matrix for variants associated to multiple genomic regionsbed.matrix.split.genomic.region
Linear, logistic or multinomial regression on a genetic scoreburden
Linear regression on a genetic scoreburden.continuous
Linear regression on a multiple genetic scores within a genomic regionburden.continuous.subscores
Logistic or multinomial regression on a genetic scoreburden.mlogit
Logistic or multinomial regression on a multiple genetic scores within a genomic regionburden.mlogit.subscores
Linear, logistic or multinomial regression on a multiple genetic scores within a genomic regionburden.subscores
Score matrix for burden testsburden.weighted.matrix
Cohort Allelic Sum TestCAST
Variant filtering based on frequency and median adjusted CADD by CADD regionsfilter.adjustedCADD
Rare variants filteringfilter.rare.variants
Genes positionsgenes.b37 genes.b38 genes.positions
Genotypic frequencies calculation for data simulationsgenotypic.freq
GnomADgenes datasetGnomADgenes
GRR matrix for genetic data simulationGRR.matrix
Jaccard indexJaccard
Kryukov data setKryukov
LCT genotypes matrixLCT.EUR.b37 LCT.EUR.b37.bed LCT.EUR.b37.fam LCT.EUR.b38 LCT.EUR.b38.bed LCT.EUR.b38.fam
LCT haplotypes data setLCT.hap LCT.haplotypes LCT.sample LCT.snps
Single variant association test with categorical phenotypemultinomial.asso.freq
Null Model for SKAT and burden testsNullObject.parameters
RAVA-FIRST: RAre Variant Association using Functionally-InfoRmed STepsRAVA.FIRST
Simulation of genetic data using GRR valuesrbm.GRR
Power of RVAT based on simulations and theoretical calculations (CAST) with GRRrbm.GRR.power
Simulation of genetic data based on haplotypic frequenciesrbm.haplos.freqs
Power of RVAT based on simulations with haplotypesrbm.haplos.power
Simulation of genetic data based on haplotypes and a libaility modelrbm.haplos.thresholds
Variants annotation based on 'CADD regions' and genomic categoriesset.CADDregions
Variants annotation based on gene positionsset.genomic.region
Variants annotation based on regions and subregions positionsset.genomic.region.subregion
SKAT testSKAT
Multi group SKAT test using bootstrap samplingSKAT.bootstrap
Multi group SKAT test using Liu et al. approximationSKAT.continuous
Multi group SKAT test using bootstrap samplingSKAT.permutations
Multi group SKAT test using Liu et al. approximationSKAT.theoretical
Exemple of functional categoriessubregions.LCT
WSS genetic scoreWSS