Package: ramwas 1.37.0

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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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ramwas/json (API)

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

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

On BioConductor:ramwas-1.37.0(bioc 3.24)ramwas-1.36.0(bioc 3.23)

dnamethylationsequencingqualitycontrolcoveragepreprocessingnormalizationbatcheffectprincipalcomponentdifferentialmethylationvisualization

6.11 score 10 stars 91 scripts 55 exports 89 dependencies

Last updated from:df845e91c1. Checks:1 ERROR, 11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksERROR242
linux-devel-arm64NOTE317
linux-devel-x86_64NOTE394
source / vignettesOK458
linux-release-arm64NOTE330
linux-release-x86_64NOTE397
macos-release-arm64NOTE188
macos-release-x86_64NOTE392
macos-oldrel-arm64NOTE181
macos-oldrel-x86_64NOTE422
windows-develNOTE483
windows-releaseNOTE444
windows-oldrelNOTE438
wasm-releaseOK206

Exports:cachedRDSloadcolSumsSqestimateFragmentSizeDistributionfindBestNpvsgetCpGsetALLgetCpGsetCGgetDataByLocationgetLocationsgetMWASgetMWASandLocationsgetMWASrangeinjectSNPsMAFinsilicoFASTQisAbsolutePathmadeBEDmadeBEDgraphmadeBEDgraphRangemadeBEDrangemakefullpathmanPlotFastmanPlotPreparemat2colsorthonormalizeCovariatesparameterDumpparameterPreprocessparametersFromFilepipelineProcessBamplotCVcorsplotFragmentSizeDistributionEstimateplotPCvaluesplotPCvectorsplotPredictionplotROCprocessCommandLinepvalue2qvalueqcmeanqqPlotFastqqPlotPrepareramwas0createArtificialDataramwas1scanBamsramwas2collectqcramwas3normalizedCoverageramwas4PCAramwas5MWASramwas6annotateTopFindingsramwas7ArunMWASesramwas7BrunElasticNetramwas7CplotByNCpGsramwas7riskScoreCVramwasAnnotateLocationsramwasParametersramwasSNPsrowSumsSqsubsetCoverageDirByLocationtestPhenotype

Dependencies:abindAnnotationDbiaskpassBHBiobaseBiocFileCacheBiocGenericsBiocParallelbiomaRtBiostringsbitbit64bitopsblobcachemcigarilloclicodetoolscpp11crayoncurlDBIdbplyrDelayedArraydigestdplyrfastmapfilelockfilematrixforeachformatRfutile.loggerfutile.optionsgenericsGenomicAlignmentsGenomicRangesglmnetgluehmshttrhttr2IRangesiteratorsjsonliteKEGGRESTKernSmoothlambda.rlatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslpillarpkgconfigpngprettyunitsprogresspurrrR6rappdirsRcppRcppEigenRhtslibrlangRsamtoolsRSQLiteS4ArraysS4VectorsSeqinfoshapesnowSparseArraystringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselectutf8vctrswithrxml2XVector

Analyzing Illumina Methylation Array Data in RaMWAS
Using RaMWAS with Illumina HumanMethylation450K or MethylationEPIC arrays | Required packages | Download example (public) data | Loading IDAT files | Probe and sample level quality control | Probes with SNPs and in cross-reactive regions | Probes with low bead count | Probes and samples with low detection p-values | Exclusion of low quality samples and probes | Obtain methylation estimates and save in RaMWAS format | Covariates for analysis | Principal components analysis (PCA) on control probes | Cell type composition | Median methylated and unmethylated intensity | Phenotypic covariates from the sample sheet | Running RaMWAS on the data | Set up parameters and covariates | Covariate pruning | MWAS without covariates (high inflation factor) | MWAS with all covariates (moderate inflation factor) | Further steps of RaMWAS pipeline
Rendered fromRW5a_matrix.Rmdusingknitr::rmarkdown

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

Analyzing Data from Other Methylation Platforms or Data Types
Using RaMWAS with other methylation platforms or data types | Import data from other sources | Principal Component Analysis (PCA) | PCA with batch regressed out | Association testing | Further steps of RaMWAS pipeline | Cleanup | Version information
Rendered fromRW5c_matrix.Rmdusingknitr::rmarkdown

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

RaMWAS Overview
Introduction | Getting started | Installation | Loading package and viewing documentation | RaMWAS steps | Scan BAM files and calculate QC indices | Summarize QC measures | Calculate CpG score matrix | Principal component analysis | Methylome-wide association study (MWAS) | Annotation of top results | Methylation risk score | CpG-SNP analysis | Directory names | Version information | References
Rendered fromRW1_intro.Rmdusingknitr::rmarkdown

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

CpG sets
Downloadable CpG sets | Constructing a custom CpG set | Constructing a CpG set for a reference genome | In silico alignment experiment
Rendered fromRW2_CpG_sets.Rmdusingknitr::rmarkdown

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

RaMWAS Parameters
Initializing RaMWAS parameters | Explanation of all parameters | Parameters pointing to directories | Parameters pointing to files | BAM names | BAM to sample matching | CpG locations | File with covariates | Multithreading | Read filtering | Coverage matrix | PCA and MWAS | Annotation of top findings | Methylation risk score | Choosing the number of folds cvnfolds in the cross validation | Joint analysis with genotype data
Rendered fromRW6_param.Rmdusingknitr::rmarkdown

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

Joint Analysis of Methylation and Genotype Data
Statistical model for Joint Analysis of Methylation and Genotype Data | Input data | Create data matrices for CpG-SNP analysis | SNP-CpG analysis
Rendered fromRW4_SNPs.Rmdusingknitr::rmarkdown

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

BAM Quality Control Measures
Loading and saving RaMWAS objects | QC text summary | Quality control measures | The number of BAM files | Total number of reads in the BAM file(s) | Number of reads aligned to the reference genome | Number of reads that passed minimum score filter | Number of reads after removal of duplicate reads | Number of recorded reads aligned to each strand | Distribution of the alignment scores | Distribution of the length of the aligned part of the reads | Distribution of edit distance | Number of reads away from CpGs | Average CpG score for CpGs and non-CpGs | Average CpG score vs. CpG density | Coverage around isolated CpGs | Fraction of reads from chrX and chrY
Rendered fromRW3_BAM_QCs.Rmdusingknitr::rmarkdown

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

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