Replication Data for: "ramr: an R package for detection of rare aberrantly methylated regions"doi:10.18710/ED8HSDDataverseNO2020-11-302Nikolaienko, Oleksii, 2020, "Replication Data for: "ramr: an R package for detection of rare aberrantly methylated regions"", https://doi.org/10.18710/ED8HSD, DataverseNO, V2, UNF:6:mHk2VYyjhtEzz5muuhFrqw== [fileUNF]Replication Data for: "ramr: an R package for detection of rare aberrantly methylated regions"doi:10.18710/ED8HSDNikolaienko, OleksiiUniversity of BergenRcomb-pramrDataverseNOUniversity of BergenNikolaienko, OleksiiNikolaienko, Oleksii2020-11-24Computer and Information ScienceMedicine, Health and Life SciencesramrDNA methylationComputational BiologyEpigenomicsThis data set contains all the necessary data sets (biologically-relevant simulated data sets, preprocessed public data sets) used to evaluate performance and obtain results using ramr (<a href="https://github.com/BBCG/ramr">https://github.com/BBCG/ramr</a>, <a href="http://www.bioconductor.org/packages/ramr/">http://www.bioconductor.org/packages/ramr/</a>) - a new method for identification of aberrantly methylated regions (AMRs). All the necessary R scripts that were used for preparation, testing and analysis of data sets are also provided. For additional information please check ramr package README.md file, vignettes or reference citation. Please use TREE VIEW to browse files efficientlyAbstract With recent advances in the field of epigenetics, the focus is widening from large and frequent disease- or phenotype-related methylation signatures to rare alterations transmitted mitotically or transgenerationally (constitutional epimutations). Merging evidence indicate that such constitutional alterations, albeit occurring at a low mosaic level, may confer risk of disease later in life. Given their inherently low incidence rate and mosaic nature, there is a need for bioinformatic tools specifically designed to analyse such events. We have developed a method (ramr) to identify aberrantly methylated DNA regions (AMRs). ramr can be applied to methylation data obtained by array or next-generation sequencing techniques to discover AMRs being associated with elevated risk of cancer as well as other diseases. We assessed accuracy and performance metrics of ramr and confirmed its applicability for analysis of large public data sets. Using ramr we identified aberrantly methylated regions that are known or may potentially be associated with development of colorectal cancer and provided functional annotation of AMRs that arise at early developmental stages.program source codemachine-readable datamachine-readable text<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a><a href="http://www.bioconductor.org/packages/devel/bioc/src/contrib/ramr_1.1.2.tar.gz">ramr package code, version 1.1.2</a><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE105018">GSE105018</a>: Whole blood DNA methylation profiles in participants of the Environmental Risk (E-Risk) Longitudinal Twin Study at age 18.<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51032">GSE51032</a>: This Series contains data from 845 participants (188 men and 657 women) in the EPIC-Italy cohort that was produced at the Human Genetics Foundation (HuGeF) in Turin, Italy.<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98149">GSE98149</a>: Reprogramming of H3K9me3-dependent heterochromatin during mammalian early embryo development [ChIP-seq].<a href="https://portal.gdc.cancer.gov/projects/TCGA-COAD">TCGA-COAD</a>: The Cancer Genome Atlas Colon Adenocarcinoma data.Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog, ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions, Bioinformatics, 2021;, btab58610.1093/bioinformatics/btab586Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog, ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions, Bioinformatics, 2021;, btab586ramr: an R package for detection of rare aberrantly methylated regions. Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog. bioRxiv 2020.12.01.40350110.1101/2020.12.01.403501ramr: an R package for detection of rare aberrantly methylated regions. Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog. bioRxiv 2020.12.01.403501https://github.com/BBCG/ramrhttp://www.bioconductor.org/packages/ramr/results-5-1000.results.tab28027text/tab-separated-valuesUNF:6:Jb8LIc18+DBR9OX2xWFcXQ==detailed performance metrics obtained by R/PoC.results.SIMULATED.R.REVISION-SIMULATED-results-5-1000.results.tab45034text/tab-separated-valuesUNF:6:v552E9gD4TbKA1rHJX8rRg==detailed performance metrics obtained by revision/R/REVISION-performance-metrics.R. Decription of columns: 01. lib: library used 02. delta: delta beta of aberrant methylation events 03. cutoff: q-value cutoff parameter for the search 04. user.self: R system.time() output value 05. sys.self: R system.time() output value 06. elapsed: R system.time() output value 07. user.child: R system.time() output value 08. sys.child: R system.time() output value 09. uGTP: number of ground true positive unique AMRs 10. uTP: number of true positive unique AMRs found 11. nGTP: number of ground true positive non-unique AMRs 12. nTP: number of true positive non-unique AMRs found 13. tFP: total number of false positive AMRs 14. utpCor: average correlation coefficient for true positive unique AMRs found 15. ntpCor: average correlation coefficient for true positive non-unique AMRs found 16. ufnCor: average correlation coefficient for false negative unique AMRs 17. nfnCor: average correlation coefficient for false negative non-unique AMRs 18. tGTN: total number of ground true negative AMRs 19. uFN: number of false negative unique AMRs 20. nFN: number of false negative non-unique AMRs 21. tTN: total number of true negative AMRs 22. tFPR: total false positive AMR rate 23. uPrecision: precision metric for unique AMRs 24. uRecall: recall metric for unique AMRs 25. uMCC: Matthews correlation coefficient metric for unique AMRs 26. uF1: F1 metric for unique AMRs 27. uAuROC: area under the recall-tFPR curve for unique AMRs 28. uAuPR: area under the precision-recall curve for unique AMRs 29. nPrecision: precision metric for non-unique AMRs 30. nRecall: recall metric for non-unique AMRs 31. nMCC: Matthews correlation coefficient metric for non-unique AMRs 32. nF1: F1 metric for non-unique AMRs 33. nAuROC: area under the recall-tFPR curve for non-unique AMRs 34. nAuPR: area under the precision-recall curve for non-unique AMRslibUNF:6:uiRFFYIIUdsrpY9Z/GRhDw==delta0.0.0.0250.50.10.1850.1760987015516356280.0UNF:6:jCsTJ1OreqxZ2Qybt2KA4g==cutoff0.010.00327652128303133460.00138888887499999965.5E-6.0.0280.01.0E-9UNF:6:1ua5V14fH0GueMJmoUaIhQ==user.self2.96583214285715266.523999999999890.00.0529999999998836.2.55350000000021462.574685161607194280.0UNF:6:ep0CzdoZNbud7y4SkYZg9Q==sys.self1.41709642857143740.85399710490394880.0280.01.486000000000050.3690000000000283.40300000000002.UNF:6:cORP8De9TiRxeakO9FOv2g==elapsed1047.43273865796487.79499999999825.11.19099999999160.0530.1005142857147280.03106.84100000001UNF:6:RrHCvfFa5ULxyPF+2LrSQg==user.child0.0280.017.2370000000228421.04699999996256075.886427847371.18043.9582984.9033607142856UNF:6:rzzpo0sv564vtTnwcq6uZg==sys.child65.791114285714330.0.280.0153.46800000000157.6963166978883355.4269999999996854.35350000000015UNF:6:67DFlYOT03guezew4CTf0Q==uGTP1000.01000.01000.0280.00.01000.0.0.0UNF:6:FNFTaAbN43a+2K/BnFPrkA==uTP430.14905963498774480.889285714285850.00.01000.0522.5.280.0UNF:6:7iRuPjQzSr9v3Bfuw2nnRA==nGTP3000.00.0.0.03000.03000.03000.0280.0UNF:6:VqFpuPR7lPeFEi7f5E4CsQ==nTP1283.7860811085825109.00.03000.01089.3785714285725.0.0280.0UNF:6:fdTDlKjYx796M3yiuV1d0g==tFP5431.710714285702.278361.034430.5813350073440.0280.00.00.0UNF:6:78xyDi6zTd8uRjUMlt3ifw==tGTN0.02803900.02803900.02803900.0.280.00.02803900.0UNF:6:KtZ5S20Tm/Xgy2PS7Xav/w==uFN477.5430.14905963498774.0.01000.0280.0519.11071428571420.0UNF:6:5u2a7aAqSqm6ByE8nZvLqw==nFN0.0280.0.2891.00.01283.78608110858251910.62142857142753000.0UNF:6:AkMM9EK+3FWcVkIgVEeTEA==tTN34430.581335007344.2525539.02798468.28928571432803900.00.02803900.0280.0UNF:6:7dsHgI7Ihd7h8iT2tjrUqQ==uPrecision1.00.2917629069303389674.00.8762258065965057206.01.00.00230462645696836.UNF:6:4d343l2fbtqHjYYE+PUf7Q==uRecall0.480889285714285740.0280.00.43014905963498784.1.00.52250.0UNF:6:A0ipQD/XwoESLXNOtjeQYg==uMCC0.03161714467568170.34405290558381761.00.76683327630394960.6675345244490059.74.0206.0UNF:6:sQXPtSTXQ2ui2bDvNBZh8g==uF10.751138129385488.0.001998001998002206.00.3817661053144612674.00.62912007470581971.0UNF:6:xZvQberW2BzuCR2Mu4nYcQ==uAuPR1.0.0.3068528194400550.20698472463768908216.064.00.8584898910448511.0UNF:6:7nxwuPhDw80Exua09VZfeA==nPrecision193.00.267737990258022587.01.01.00.89311083525472390.00680065794403194.UNF:6:36jzLiS17SYr83r9338s7g==nRecall0.00.36312619047619041.00.4279286937028607280.00.03633333333333330.0.UNF:6:6ct80NKBBFKQR3wH21XwEQ==nMCC0.6440112427314660.9996662545655770.38876181867331510.0182476625090128.0.545474937073782387.0193.0UNF:6:bWPHnk6GDc7PzD7rIEa1sw==nF10.9996665555185060.5879314393050010.50371723222144196.66444518493835E-4.87.0193.00.41780202819100243UNF:6:/At7hbq/QTBfGod+prBm1g==nAuPR48.00.90805526216734790.168928164650370970.307513762754091.1.0232.01.0UNF:6:VqtyknMbaKxychU+nnvSbA==libUNF:6:hjY5jxt1uv7sys6V0Q3TGA==delta0.18500000000000003.0.0250.00.5450.00.10.1759795999515545UNF:6:TjfPMCD0J/2fvI1pqXuYTg==cutoff450.00.014941601166722848.0.051.0E-100.0061111111099999965.5E-60.0UNF:6:0b4oBfezYJIwQhbDmvWhHA==user.self450.02.021000000000020.35338000000000040.00.04000000000002050.47424812376573860.0.UNF:6:uAwbpTkoGXTEaOd0uJVoBQ==sys.self1.32400000000001.450.00.00.19500000000000050.48255111111111140.0770.45849287171936626UNF:6:dc/LkaCX/w34gVvnD29tYg==elapsed0.04670.268186.28449999998702999.7191622222223450.01555.294549330697.13.2889999999898UNF:6:aHAmde5+VtvUhvKPXiWGjg==user.child450.015862.3363622.753313333332240.1560000000172.5670.450848415524381.2590.0UNF:6:1JUQJtG65dT7N7LVt3hTkw==sys.child.0.04.31700000000274254.64214734337807450.01004.346146.9269400000002370.9534999999996UNF:6:8N+EwDHnp5lXe8PLBbTcww==uGTP1000.01000.0450.00.01000.0.1000.00.0UNF:6:YcdsP1naXPjw5NXxnxZrGw==uTP0.0423.20643390851114578.5.0.0450.0520.10666666666681000.0UNF:6:BJpDBl8WWM0lKsOgXKa39g==nGTP3000.03000.03000.0450.0.0.03000.00.0UNF:6:EMHmILdv87Iwx/XDBPZDZA==nTP.450.03000.01297.3795023738842594.50.01270.9622222222210.0UNF:6:oxMUa2wBbvGOdo28WQiKuQ==tFP450.0.0.01278490.0131775.89015663850.00.020049.884444444448UNF:6:QgAz2FzlCdlEOJ/thysNDQ==utpCor450.00.00.9135831480855860.6304923738179067.0.00.9427617624324330.4061453599480377UNF:6:QZmfCOddEsq305JXy389ig==ntpCor0.9110006047588540.0450.00.55376850895314440.9513902861874020.0.0.427732046412866UNF:6:bIe+ut7rndNfZcBR2POxQw==ufnCor450.00.6147878885422986.0.9150560397082110.87355495148402110.36718529063663860.00.0UNF:6:P0EtDA0Lzu86SP+TQe+LDg==nfnCor0.328448219443078560.90882353725722650.00.00.913474025766923.450.00.7177199472249159UNF:6:YPxPCRzM6UFsIx+XMycVgQ==tGTN2803900.0450.0.2803900.02803900.02803900.00.00.0UNF:6:9aBDtg6u896vZML0rgOh2g==uFN0.0450.0.0.0423.20643390851114421.51000.0479.89333333333326UNF:6:qwbRbxp6Wmk6/tO2FoB7mQ==nFN0.0450.03000.0.1729.0377777777790.02405.51297.3795023738842UNF:6:gfMjRuf0w30hkhryAN1qCQ==tTN1525410.02803900.0.131775.89015663852783850.11555555562803900.00.0450.0UNF:6:tbgYzEY5TJmbosE5cL25Bw==tFPR0.00.00.046997357308262950.0071507130940634284450.0.0.00.455968472484753UNF:6:Cy9BsaP3nUiPEsY37h4qtA==uPrecision362.0.1.00.688947179937078288.00.392166531152023660.96785063492967350.0UNF:6:7mUx1Uq/5FVzrCqOF1n2og==uRecall0.42320643390851090.0.0.57850.01.0450.00.5201066666666666UNF:6:f/oEmaz7ONjMnRiW+3Bm0A==uMCC88.0-1.17728534002087E-4.0.3544690257948995362.00.65970787270698050.57591390603993650.999500196453802UNF:6:UiXwDhCMQXKNQGtceTSfTw==uF189.00.5410734407725310.623537508602891.361.00.37792875544403270.001034527350316820.999500249875063UNF:6:DsOZTKDoF9QuwMQRY8/Sqw==uAuROC.0.9999999998216770.3720074487923377450.00.00.8744382823923820.68901302963729090.0UNF:6:cK5tkjJUhlSyV6h4lNgtkA==uAuPR420.0.2.37988993009073E-60.59560175390910440.99999950049950.72109280845242850.371999750287013830.0UNF:6:n2Q3rgJvLwzeit56YnDKZw==nPrecision0.001780151312861590.36932340783791870.94682697654597491.00.7258924894350665332.0118.0.UNF:6:trhX2yWDGjN4ih7xtp2djA==nRecall0.01.00.43245983412462840.1981666666666665.0.4236540740740738450.00.0UNF:6:/98z1CQoV+XQoCftrmWvDw==nMCC0.53503090335280380.00106817696753959118.00.372347197084207050.617179132035501.0.999833196695076332.0UNF:6:sMFgzEiYfxg7DQC+xkuiuw==nF1.0.590200258474930.5008784837923426118.00.9998333611064820.3943974095935564332.06.66444518493835E-4UNF:6:85iaXJq8352jEbStEeEKAw==nAuROC450.00.66505026782008660.00.385092256450458860.0.0.9999999998811180.863778462676986UNF:6:utfNdZ5dI8qRSnd0WCgIsg==nAuPR0.9999998889259147.13118407368891E-660.0390.00.65624943501276130.34986947112603330.738448057833912.UNF:6:Kti2J4bPCHv5kEDe+yXR5w==00-README.txtThe README filetext/plainGSE105018.data.RdataAn R data file containing the following objects: 1. geo.ranges: a GRanges object containing beta values for 430802 CpGs across 1658 samples from GSE105018 Illumina HumanMethylation 450 data set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE105018). 2. sample.info: a list containing sample metadata for all samples in the GSE105018 data set. 3. sample.ids: a character vector of sample IDs.application/gzipGSE51032.data.RdataAn R data file containing the following objects: 1. geo.ranges: a GRanges object containing beta values for 485512 CpGs across 845 samples from GSE51032 Illumina HumanMethylation 450 data set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51032). This data set was used as a template to create simulated test data sets. 2. sample.info: a list containing sample metadata for all samples in the GSE51032 data set. 3. sample.ids: a character vector of sample IDs.application/gzipGRCh37.p13.2019.12.12.RdataAn R data file containing GRanges object with all the gene coordinates from GRCh37 annotation.application/gzipGRCh38.p13.2020.08.10.RdataAn R data file containing GRanges object with all the gene coordinates from GRCh38 annotation.application/gzipto.remove.RDataAn R data file containing character vector with all polymorphic and non-specific Illumina HumanMethylation 450 probe IDs.application/gziphg19ToMm9.over.chainhuman hg19 to mouse mm9 liftover chain. Required for R/PoC.results.GSE105018.R.application/octet-streammouse.embryonic.marks.tar.gzLOLA database containing information on mouse embryonic chromatin marks. Required for R/PoC.results.GSE105018.R. Ungzip before using.application/gzipresults-5-1000.RdataAn R data file with all the performance metrics obtained by R/PoC.results.SIMULATED.R.application/gzipresults-plots-5-1000.pdfplots for performance metrics obtained by R/PoC.results.SIMULATED.R.application/pdfSIMULATED-5-1000-0.025.data.RdataAn R data file containing the following objects: 1. simulated.ranges: a GRanges object containing simulated beta values for 485512 CpGs across 100 samples with aberrant methylation events introduced in some of the regions. 2. modified.regions.unique: a GRanges object containing genomic ranges and sample ID for 1000 true positive unique aberrantly methylated regions. 3. modified.regions.nonunique: a GRanges object containing genomic ranges and sample ID for 3000 true positive non-unique aberrantly methylated regions. 4. delta: delta value by which CpG beta values corresponding to particular region/sample pair in "simulated.ranges" were increased or decreased. 5. merge.window: "merge.window" parameter value to use during testing. 6. min.cpgs: "min.cpgs" parameter value to use during testing. 7. sample.ids: a character vector of sample IDs.application/gzipSIMULATED-5-1000-0.050.data.RdataAn R data file which is similar to "data∕SIMULATED/SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.050"application/gzipSIMULATED-5-1000-0.100.data.RdataAn R data file which is similar to "data∕SIMULATED/SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.100"application/gzipSIMULATED-5-1000-0.250.data.RdataAn R data file which is similar to "data∕SIMULATED/SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.250"application/gzipSIMULATED-5-1000-0.500.data.RdataAn R data file which is similar to "data∕SIMULATED/SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.500"application/gzipwriter-plots-5-1000.pdfMethylation profiles of eight random genomic regions from the GSE51032 template data set (“GSE51032”) together with the methylation profiles of the same regions in each of five simulated test data sets obtained by changing beta values within those regions for a subset of samples (highlighted) by a particular delta (“delta=0.025”, “0.050”, “0.100”, “0.250” or “0.500”).application/pdffiles.2019-10-10.jsonJSON file containing information on all Illumina Human Methylation 450 assay results in TCGA.application/jsonTCGA-COAD.tcga.data.RdataAn R data file containing the following objects: 1. tcga.data: an object containing beta values for 487192 CpGs across 192 samples from TCGA-COAD Illumina HumanMethylation 450 data set (https://portal.gdc.cancer.gov/). This data set was used to find epimutations undergoing positive selection during carcinogenesys.application/gziptcga.coad.AMRs.RdataAn R data file containing the following objects: 1. tcga.coad.ranges: GRanges object with TCGA-COAD subset of methylation beta values for adjacent and tumour tissue pairs. 2. tcga.coad.ramr.hg19: GRanges object with aberrantly methylated regions found in adjacent TCGA-COAD tissue samples.application/gzipPoC.results.GSE105018.Ran R script to reproduce all findings from GSE105018 data set.type/x-r-syntaxPoC.results.GSE51032.Ran R script to reproduce all findings from GSE51032 data set.type/x-r-syntaxPoC.results.SIMULATED.Ran R script that evaluates method performance for ramr and other methods across all test scenarios. NB that some methods (e.g. comb-p) are slow, thus modern hardware and parallelism is advised. As comb-p uses at least four threads by default, it was forced to run in a single threaded mode by limiting "processes" parameter of multiprocessing.pool.Pool call to "1" in comb-p sorce code (cpv/_common.py).type/x-r-syntaxPoC.results.TCGA-COAD.Ran R script that finds all aberantly methylated regions in a subset of adjacent tissue samples from TCGA-COAD.type/x-r-syntaxPoC.writer.SIMULATED.Ran R script that generates simulated test data sets using GSE51032 as a template.type/x-r-syntaxREVISION-SIMULATED-5-1000-0.025.data.RdataAn R data file generated by revision/R/REVISION-performance-metrics.R and containing the following objects: 1. simulated.ranges: a GRanges object containing simulated beta values for 485512 CpGs across 100 samples with aberrant methylation events introduced in some of the regions. 2. modified.regions.unique: a GRanges object containing genomic ranges and sample ID for 1000 true positive unique aberrantly methylated regions. 3. modified.regions.nonunique: a GRanges object containing genomic ranges and sample ID for 3000 true positive non-unique aberrantly methylated regions. 4. modified.regions.noise: a GRanges object containing genomic ranges and sample ID for 1000 single-base aberrations. 5. delta: delta value by which CpG beta values corresponding to particular region/sample pair in "simulated.ranges" were increased or decreased. 6. merge.window: "merge.window" parameter value to use during testing. 7. min.cpgs: "min.cpgs" parameter value to use during testing. 8. sample.ids: a character vector of sample IDs.application/gzipREVISION-SIMULATED-5-1000-0.050.data.RdataAn R data file which is similar to "revision∕data/REVISION-SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.050". File was generated by revision/R/REVISION-performance-metrics.R.application/gzipREVISION-SIMULATED-5-1000-0.100.data.RdataAn R data file which is similar to "revision∕data/REVISION-SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.100". File was generated by revision/R/REVISION-performance-metrics.R.application/gzipREVISION-SIMULATED-5-1000-0.250.data.RdataAn R data file which is similar to "revision∕data/REVISION-SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.250". File was generated by revision/R/REVISION-performance-metrics.R.application/gzipREVISION-SIMULATED-5-1000-0.500.data.RdataAn R data file which is similar to "revision∕data/REVISION-SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.500". File was generated by revision/R/REVISION-performance-metrics.R.application/gzipREVISION-SIMULATED-plots-5-1000.pdfapplication/pdfREVISION-SIMULATED-results-5-1000.RdataAn R data file with all the performance metrics obtained by revision/R/REVISION-performance-metrics.R.application/gzipREVISION-SIMULATED-suppl-plots-5-1000.pdfplots for performance metrics generated by revision/R/REVISION-performance-metrics.R.application/pdfREVISION-SIMULATED-suppl-table-5-1000.pdftop results generated by revision/R/REVISION-performance-metrics.R.application/pdfREVISION-SIMULATED-suppl-time-5-1000.pdftime plot generated by revision/R/REVISION-performance-metrics.R.application/pdfREVISION-algorithm-diagram.Ran R script that builds ramr flowcharttype/x-r-syntaxREVISION-dataset-similarity.Ran R script that compares template and simulated data setstype/x-r-syntaxREVISION-LOLA-bootstrap.Ran R script that checks LOLA analysis specificitytype/x-r-syntaxREVISION-performance-metrics.Ran R script that generates simulated test data sets using GSE51032 as a template and evaluates method performance for ramr and other methods across all test scenarios. NB that some methods (e.g. comb-p) are slow, thus modern hardware and parallelism is advised. As comb-p uses at least four threads by default, it was forced to run in a single threaded mode by limiting "processes" parameter of multiprocessing.pool.Pool call to "1" in comb-p sorce code (cpv/_common.py).type/x-r-syntaxREVISION-ramr-dmrcate.Ran R script that compares ramr and DMRcate across various random seedstype/x-r-syntax