Package: ramwas 1.31.0

Andrey A Shabalin

ramwas: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms

A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) <doi:10.1093/bioinformatics/bty069>.

Authors:Andrey A Shabalin [aut, cre], Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut]

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NEWS

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

Peer review:

Bug tracker:https://github.com/andreyshabalin/ramwas/issues

On BioConductor:ramwas-1.31.0(bioc 3.21)ramwas-1.30.0(bioc 3.20)

dnamethylationsequencingqualitycontrolcoveragepreprocessingnormalizationbatcheffectprincipalcomponentdifferentialmethylationvisualization

6.08 score 10 stars 85 scripts 265 downloads 55 exports 93 dependencies

Last updated 2 months agofrom:d24c1d2fce. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 30 2024
R-4.5-win-x86_64NOTENov 30 2024
R-4.5-linux-x86_64NOTENov 30 2024
R-4.4-win-x86_64OKNov 30 2024
R-4.4-mac-x86_64OKNov 30 2024
R-4.4-mac-aarch64OKNov 30 2024
R-4.3-win-x86_64OKNov 30 2024
R-4.3-mac-x86_64OKNov 30 2024
R-4.3-mac-aarch64OKNov 30 2024

Exports:cachedRDSloadcolSumsSqestimateFragmentSizeDistributionfindBestNpvsgetCpGsetALLgetCpGsetCGgetDataByLocationgetLocationsgetMWASgetMWASandLocationsgetMWASrangeinjectSNPsMAFinsilicoFASTQisAbsolutePathmadeBEDmadeBEDgraphmadeBEDgraphRangemadeBEDrangemakefullpathmanPlotFastmanPlotPreparemat2colsorthonormalizeCovariatesparameterDumpparameterPreprocessparametersFromFilepipelineProcessBamplotCVcorsplotFragmentSizeDistributionEstimateplotPCvaluesplotPCvectorsplotPredictionplotROCprocessCommandLinepvalue2qvalueqcmeanqqPlotFastqqPlotPrepareramwas0createArtificialDataramwas1scanBamsramwas2collectqcramwas3normalizedCoverageramwas4PCAramwas5MWASramwas6annotateTopFindingsramwas7ArunMWASesramwas7BrunElasticNetramwas7CplotByNCpGsramwas7riskScoreCVramwasAnnotateLocationsramwasParametersramwasSNPsrowSumsSqsubsetCoverageDirByLocationtestPhenotype

Dependencies:abindAnnotationDbiaskpassBHBiobaseBiocFileCacheBiocGenericsBiocParallelbiomaRtBiostringsbitbit64bitopsblobcachemclicodetoolscpp11crayoncurlDBIdbplyrDelayedArraydigestdplyrfansifastmapfilelockfilematrixforeachformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesglmnetgluehmshttrhttr2IRangesiteratorsjsonliteKEGGRESTKernSmoothlambda.rlatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslpillarpkgconfigplogrpngprettyunitsprogresspurrrR6rappdirsRcppRcppEigenRhtslibrlangRsamtoolsRSQLiteS4ArraysS4VectorsshapesnowSparseArraystringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselectUCSC.utilsutf8vctrswithrxml2XVectorzlibbioc

RaMWAS Overview

Rendered fromRW1_intro.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2018-08-30
Started: 2016-12-12

CpG sets

Rendered fromRW2_CpG_sets.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2018-03-09
Started: 2017-04-19

BAM Quality Control Measures

Rendered fromRW3_BAM_QCs.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2017-12-21
Started: 2016-12-12

Joint Analysis of Methylation and Genotype Data

Rendered fromRW4_SNPs.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2017-12-24
Started: 2017-04-19

Analyzing Illumina Methylation Array Data in RaMWAS

Rendered fromRW5a_matrix.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2019-02-18
Started: 2019-02-18

Analyzing Data from Other Methylation Platforms or Data Types

Rendered fromRW5c_matrix.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2019-02-18
Started: 2019-02-18

RaMWAS Parameters

Rendered fromRW6_param.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2018-03-09
Started: 2017-04-19

Readme and manuals

Help Manual

Help pageTopics
Fast Methylome-wide Association Study Pipeline for Enrichment Platformsramwas-package ramwas
Cached Loading of RDS FilescachedRDSload
Quickly Find N Smallest P-values in a Long VectorfindBestNpvs
Functions for Access to Data, MWAS Results, and Location InformationgetDataByLocation getLocations getMWAS getMWASandLocations getMWASrange
Construct CpG set for a Reference GenomegetCpGsetALL getCpGsetCG
Inject SNPs from VCF Count File into a DNA SequenceinjectSNPsMAF
Construct FASTQ File for In-silico Alignment ExperimentinsilicoFASTQ
Check if Path is Absolute.isAbsolutePath
Export MWAS results in BED format.madeBED madeBEDgraph madeBEDgraphRange madeBEDrange
Combine Path and Filename into Filename with Pathmakefullpath
Fast Manhattan plot for Large Number of P-valuesmanPlotFast manPlotPrepare
Split a Matrix into Column Vectorsmat2cols
Orthonormalize CovariatesorthonormalizeCovariates
Save Parameters in a Text FileparameterDump
Preprocess Pipeline Parameter List.parameterPreprocess
Scan Parameters From a R Code FileparametersFromFile
RaMWAS: High Level Pipeline Functionspipeline pipelineProcessBam ramwas1scanBams ramwas2collectqc ramwas3normalizedCoverage ramwas4PCA ramwas5MWAS ramwas6annotateTopFindings ramwas7ArunMWASes ramwas7BrunElasticNet ramwas7CplotByNCpGs ramwas7riskScoreCV ramwasSNPs
Plotting Functions used in Cross Validation Analysis (Methylation Risk Score).plotCVcors plotPrediction plotROC
Estimate and plot Fragment Size Distribution.estimateFragmentSizeDistribution plotFragmentSizeDistributionEstimate
Plot Principal component (PC) Values (variation explained) and PC vectors (loadings)plotPCvalues plotPCvectors
Scan Parameters From Command LineprocessCommandLine
Calculate Benjamini-Hochberg q-valuespvalue2qvalue
Quality Control Measuresplot.qcCoverageByDensity plot.qcEditDist plot.qcEditDistBF plot.qcHistScore plot.qcHistScoreBF plot.qcIsoDist plot.qcLengthMatched plot.qcLengthMatchedBF qcmean qcmean.NULL qcmean.qcChrX qcmean.qcChrY qcmean.qcCoverageByDensity qcmean.qcEditDist qcmean.qcEditDistBF qcmean.qcFrwrev qcmean.qcHistScore qcmean.qcHistScoreBF qcmean.qcIsoDist qcmean.qcLengthMatched qcmean.qcLengthMatchedBF qcmean.qcNonCpGreads
Fast QQ-plot for Large Number of P-valuesqqPlotFast qqPlotPrepare
Create Artificial Data Setramwas0createArtificialData
Extract Biomart Annotation for a Vector of Locations.ramwasAnnotateLocations
Function for Convenient Filling of the RaMWAS Parameter List.ramwasParameters
Form Row and Column Sums of SquarescolSumsSq rowSumsSq
Class for Accessing Data (Coverage) MatrixrwDataClass rwDataClass-class
Subset a data matrix and locationssubsetCoverageDirByLocation
Test the Phenotype of Interest for Association with Methylation Coverage.testPhenotype