dnaEPICO 0.99.14
This vignette demonstrates the local, interactive use of dnaEPICO. In this
mode, the main functions return structured in-memory result objects that can be
inspected directly in the R session. This is the recommended route when you are
checking inputs, reviewing quality-control outputs, or learning the structure of
the package results before moving to larger analyses.
The examples below focus on the first three workflow stages:
preprocessingMinfiEwasWater()svaEnmix()preprocessingPheno()All examples use saveOutputs = FALSE, which keeps the workflow in memory and
avoids writing the legacy file outputs.
The dnaEPICO package uses methods from several external
packages. Because no single manuscript describes all components, the guidance
below explains how to cite dnaEPICO depending on the functions you use.
This citation guidance is adapted from the vignettes and user guides of minfi, ENmix, and wateRmelon.
preprocessingMinfiEwasWater(), please cite Aryee et al. (2014); Fortin, Triche Jr., and Hansen (2017);
Xu, Niu, and Taylor (2021); Pidsley et al. (2013); Murat et al. (2020); Maksimovic, Gordon, and Oshlack (2012); Fortin et al. (2014); Triche et al. (2013); Touleimat and Tost (2012).svaEnmix(), please cite Aryee et al. (2014); Xu, Niu, and Taylor (2021).dnaEPICO is built on core Bioconductor infrastructure for high-dimensional genomic data, with a focus on Illumina DNA methylation arrays. To read this vignette, we assume that you are already familiar with the general DNA methylation workflow. If not, we recommend first reading this tutorial, which provides a practical introduction to the main concepts and analysis steps.
Preprocessing and quality control are performed using established Bioconductor tools, including minfi, ENmix, and wateRmelon. Users are expected to have basic familiarity with R, Bioconductor workflows, and Illumina IDAT file structures.
If you are asking yourself the question “Where do I start using Bioconductor?”, you might be interested in this blog post.
The dnaEPICO package depends on minfi, ENmix, and wateRmelon, among others. Install dependencies with BiocManager:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("dnaEPICO")
BiocManager::valid()library("dnaEPICO")The preprocessing wrapper reads a phenotype table and IDAT files, performs quality control, normalisation, filtering, and cell-composition estimation, and returns a list-based result object. The next chunk runs the function on a small example input and prints the class, element names, and a preview of the filtered phenotype table.
The next chunk shows how to recreate the temporary files used in this example. It writes a small phenotype table to a temporary directory, copies a small set of IDAT files, and records the cross-reactive probe reference used during probe filtering.
preprocessing_inputs <- dnaEPICO:::exampleMinfiIdatInputsDnaEpico(n = 6L)
names(preprocessing_inputs)
#> [1] "tempDir" "idatFolder" "phenoFile"
#> [4] "targets" "arrayType" "annotationVersion"
#> [7] "crossReactivePath"
preprocessing_inputs$tempDir
#> [1] "/tmp/Rtmp6rWi3o/dnaEPICO-idat-example-3a4cfb42a656eb"
basename(preprocessing_inputs$phenoFile)
#> [1] "pheno.csv"
head(basename(list.files(preprocessing_inputs$idatFolder, full.names = TRUE)), 4)
#> [1] "5723646052_R02C02_Grn.idat" "5723646052_R02C02_Red.idat"
#> [3] "5723646052_R04C01_Grn.idat" "5723646052_R04C01_Red.idat"
basename(preprocessing_inputs$crossReactivePath)
#> [1] "12864_2024_10027_MOESM8_ESM.csv"preprocessing_inputs is a small list returned by the internal example helper.
names(preprocessing_inputs) shows its main elements: tempDir is the
temporary working directory, idatFolder contains the copied example IDAT
files, phenoFile is the phenotype table used by the wrapper, targets is the
same phenotype information already loaded into memory, arrayType and
annotationVersion describe the array platform, and crossReactivePath points
to the cross-reactive probe reference used later during filtering.
preprocessResult <- preprocessingMinfiEwasWater(
phenoFile = preprocessing_inputs$phenoFile,
idatFolder = preprocessing_inputs$idatFolder,
outputLogs = file.path(preprocessing_inputs$tempDir, "logs"),
nSamples = 6,
SampleID = "Sample_Name",
arrayType = preprocessing_inputs$arrayType,
annotationVersion = preprocessing_inputs$annotationVersion,
scriptLabel = "preprocessingMinfiEwasWater",
baseDataFolder = file.path(preprocessing_inputs$tempDir, "rData"),
figureBaseDir = file.path(preprocessing_inputs$tempDir, "figures"),
detPThreshold = 1,
normMethods = "quantile",
sexColumn = "Sex",
pvalThreshold = 1,
chrToRemove = "",
snpsToRemove = "SBE",
mafThreshold = 1,
crossReactivePath = preprocessing_inputs$crossReactivePath,
plotGroupVar = "Sex",
lcRef = "saliva",
phenoOrder = "Sample_Name;Sex;Basename;Sentrix_ID;Sentrix_Position",
lcPhenoDir = preprocessing_inputs$tempDir,
display = FALSE,
verbose = FALSE,
logs = FALSE,
saveOutputs = FALSE
)
class(preprocessResult)
#> [1] "dnaEPICO_preprocessingMinfiEwasWater"
names(preprocessResult)
#> [1] "targets" "RGSet" "rawData" "assessment" "sexData"
#> [6] "normData" "filterData" "metricsData" "lcData" "logFile"
head(preprocessResult$targets[, c(
"Sample_Name",
"Sex",
"Sentrix_ID",
"Sentrix_Position"
)])
#> Sample_Name Sex Sentrix_ID Sentrix_Position
#> 1 GroupA_3 1 5723646052 R02C02
#> 2 GroupA_2 0 5723646052 R04C01
#> 3 GroupB_3 1 5723646052 R05C02
#> 4 GroupB_1 0 5723646053 R04C02
#> 5 GroupA_1 0 5723646053 R05C02
#> 6 GroupB_2 0 5723646053 R06C02The returned object has class dnaEPICO_preprocessingMinfiEwasWater.
names(preprocessResult) lists the main elements returned by the wrapper. The
targets element is the filtered phenotype table aligned to the retained
samples. The remaining elements are nested result objects for the major
preprocessing stages, including the RGSet, the methylation metrics, and the
cell-composition results.
Arguments:
phenoFile and idatFolder define the phenotype table and the directory of
raw IDAT pairs.nSamples = 6 keeps the example small enough for interactive use.SampleID = "Sample_Name" identifies the column used to align phenotype data
and methylation objects.arrayType and annotationVersion must match the array platform being
processed.detPThreshold, normMethods, pvalThreshold, chrToRemove,
snpsToRemove, and mafThreshold control quality filtering and probe
filtering.crossReactivePath supplies the cross-reactive probe reference used during
filtering.sexColumn, plotGroupVar, lcRef, and phenoOrder control sex
prediction, quality-control grouping, cell-type estimation, and the structure
of the returned phenotype table.display = FALSE, verbose = FALSE, logs = FALSE, and
saveOutputs = FALSE keep the example quiet, in memory, and free of file
outputs.The next chunk inspects three important elements: the filtered RGSet, the
metric matrices, and the phenotype table augmented with cell-composition
estimates.
class(preprocessResult$RGSet)
#> [1] "RGChannelSet"
#> attr(,"package")
#> [1] "minfi"
names(preprocessResult$metricsData)
#> [1] "beta" "m" "cn"
head(preprocessResult$lcData$phenoLC[, 1:6])
#> Sample_Name Sex Basename Sentrix_ID Sentrix_Position Sample_Well
#> 1 GroupA_3 1 5723646052_R02C02 5723646052 R02C02 H5
#> 2 GroupA_2 0 5723646052_R04C01 5723646052 R04C01 D5
#> 3 GroupB_3 1 5723646052_R05C02 5723646052 R05C02 C6
#> 4 GroupB_1 0 5723646053_R04C02 5723646053 R04C02 F7
#> 5 GroupA_1 0 5723646053_R05C02 5723646053 R05C02 G7
#> 6 GroupB_2 0 5723646053_R06C02 5723646053 R06C02 H7In local use, these are the most useful objects to inspect first.
preprocessResult$RGSet is the core methylation object. metricsData
contains the beta-value, M-value, and copy-number matrices used in downstream
steps. lcData$phenoLC is the phenotype table augmented with estimated cell
composition and is the object passed forward to later workflow stages.
The SVA wrapper loads a phenotype table and a saved RGSet, estimates
surrogate variables from ENmix control probes, and returns both the surrogate
variables and the merged phenotype table.
The next chunk shows how to recreate the temporary files consumed by
svaEnmix(): a phenotype CSV file and a saved RGSet object in .RData
format.
sva_inputs <- dnaEPICO:::exampleMinfiBaseDataDnaEpico()
sva_temp_dir <- tempdir()
sva_targets_file <- file.path(sva_temp_dir, "sva_targets.csv")
sva_rgset_file <- file.path(sva_temp_dir, "sva_RGSet.RData")
names(sva_inputs)
#> [1] "RGSet" "targets" "crossReactivePath"
class(sva_inputs$RGSet)
#> [1] "RGChannelSet"
#> attr(,"package")
#> [1] "minfi"
utils::write.csv(sva_inputs$targets, sva_targets_file, row.names = FALSE)
RGSet <- sva_inputs$RGSet
save(RGSet, file = sva_rgset_file)
basename(c(sva_targets_file, sva_rgset_file))
#> [1] "sva_targets.csv" "sva_RGSet.RData"
file.exists(c(sva_targets_file, sva_rgset_file))
#> [1] TRUE TRUEsva_inputs is another helper list used to make the vignette self-contained.
names(sva_inputs) shows that it provides the in-memory RGSet, the matching
targets table, and a crossReactivePath reference. In this step, the wrapper
needs file inputs rather than the in-memory objects, so the chunk writes
targets to sva_targets.csv and saves the RGSet object to
sva_RGSet.RData.
svaResult <- svaEnmix(
phenoFile = sva_targets_file,
rgsetData = sva_rgset_file,
outputLogs = file.path(tempdir(), "logs"),
SampleID = "Sample_Name",
arrayType = "IlluminaHumanMethylation450k",
annotationVersion = "ilmn12.hg19",
SentrixIDColumn = "Sentrix_ID",
SentrixPositionColumn = "Sentrix_Position",
ctrlSvaPercVar = 0.90,
ctrlSvaFlag = 1,
scriptLabel = "svaEnmix",
display = FALSE,
verbose = FALSE,
logs = FALSE,
saveOutputs = FALSE
)
#> 3 surrogate variables explain 91.17398 % of
#> data variation
class(svaResult)
#> [1] "dnaEPICO_svaEnmix"
names(svaResult)
#> [1] "targets" "RGSet" "svaData" "mergedPheno" "analysisData"
#> [6] "plotFiles" "savedFiles" "logFile"
dim(svaResult$svaData$sva)
#> [1] 6 3
head(svaResult$mergedPheno[, 1:min(6, ncol(svaResult$mergedPheno))])
#> Sample_Name Sample_Well Sample_Plate Sample_Group Pool_ID person
#> 1 GroupA_3 H5 NA GroupA NA id3
#> 2 GroupA_2 D5 NA GroupA NA id2
#> 3 GroupB_3 C6 NA GroupB NA id3
#> 4 GroupB_1 F7 NA GroupB NA id1
#> 5 GroupA_1 G7 NA GroupA NA id1
#> 6 GroupB_2 H7 NA GroupB NA id2The returned object has class dnaEPICO_svaEnmix, and names(svaResult)
shows the main SVA components returned by the wrapper. The svaData$sva
matrix contains one row per sample and one column per surrogate variable. The
mergedPheno table is often the most convenient object to inspect because it
combines the original phenotype data and the new SVA columns in one place.
Arguments:
phenoFile points to the phenotype table that already includes the cell
composition estimates from preprocessing.rgsetData points to the saved RGSet used to derive ENmix control-probe
surrogate variables.SampleID, SentrixIDColumn, and SentrixPositionColumn identify the
sample and array-position columns used in the SVA analysis.ctrlSvaPercVar = 0.90 asks the procedure to keep enough control-derived
surrogate variables to explain 90% of the control-probe variance.ctrlSvaFlag = 1 enables the control-based SVA workflow.display = FALSE, verbose = FALSE, logs = FALSE, and
saveOutputs = FALSE keep the example interactive and in-memory.The association analysis between the surrogate variables and array-position
metadata is stored separately in analysisData.
class(svaResult$analysisData)
#> [1] "dnaEPICO_svaEnmix_analysis"
names(svaResult$analysisData)
#> [1] "sva" "K" "sentrixID" "sentrixPosition"
#> [5] "fullModels" "reducedModels" "droptermSteps" "anovaFull"
#> [9] "anovaReduced"This object contains the fitted models and ANOVA summaries used to assess how the surrogate variables relate to Sentrix chip and position effects.
The phenotype-preparation wrapper aligns the phenotype table with the beta, M-value, and copy-number matrices; splits the data by timepoint; constructs a combined longitudinal object; and prepares Clock Foundation export tables.
The next chunk shows how to recreate the temporary phenotype and matrix files used in this example. The helper writes a phenotype table plus aligned beta, M-value, and copy-number objects to a temporary directory.
pheno_inputs <- dnaEPICO:::examplePreprocessingPhenoStateDnaEpico()
names(pheno_inputs)
#> [1] "tempDir" "pheno" "phenoPath"
#> [4] "betaPath" "mPath" "cnPath"
#> [7] "metricsData" "timepointData" "combinedData"
#> [10] "clockFoundation" "preprocessingData"
pheno_inputs$tempDir
#> [1] "/tmp/Rtmp6rWi3o/dnaEPICO-preprocessingPheno-example-3a4cfb6cbe96f0"
basename(c(
pheno_inputs$phenoPath,
pheno_inputs$betaPath,
pheno_inputs$mPath,
pheno_inputs$cnPath
))
#> [1] "phenoLC.csv" "beta.RData" "m.RData" "cn.RData"pheno_inputs is a list that bundles both the temporary files and the
corresponding in-memory objects used in this stage. names(pheno_inputs) shows
that it contains the temporary directory, the phenotype table, the three saved
matrix files (phenoPath, betaPath, mPath, and cnPath), and precomputed
helper objects such as metricsData, timepointData, combinedData, and
clockFoundation.
phenoResult <- preprocessingPheno(
phenoFile = pheno_inputs$phenoPath,
betaPath = pheno_inputs$betaPath,
mPath = pheno_inputs$mPath,
cnPath = pheno_inputs$cnPath,
SampleID = "Sample_Name",
timeVar = "Timepoint",
timepoints = "1,2",
combineTimepoints = "1,2",
outputPheno = file.path(pheno_inputs$tempDir, "data", "preprocessingPheno"),
outputRData = file.path(
pheno_inputs$tempDir,
"rData",
"preprocessingPheno",
"metrics"
),
outputRDataMerge = file.path(
pheno_inputs$tempDir,
"rData",
"preprocessingPheno",
"mergeData"
),
sexColumn = "Sex",
outputLogs = file.path(pheno_inputs$tempDir, "logs"),
outputDir = file.path(pheno_inputs$tempDir, "clockFoundation"),
verbose = FALSE,
logs = FALSE,
saveOutputs = FALSE
)
class(phenoResult)
#> [1] "dnaEPICO_preprocessingPheno"
names(phenoResult)
#> [1] "pheno" "metricsData" "timepointData" "combinedData"
#> [5] "clockFoundation" "savedFiles" "logFile"
names(phenoResult$combinedData)
#> [1] "timepoints" "suffix" "pheno" "phenoBeta"
head(phenoResult$combinedData$phenoBeta[, 1:6])
#> Sample_Name Timepoint Sex Age cg00000029 cg00000108
#> 1 S1 1 Female 20 0.20 0.60
#> 2 S2 1 Male 22 0.25 0.55
#> 3 S3 2 Female 21 0.22 0.52
#> 4 S4 2 Male 23 0.27 0.58The returned object has class dnaEPICO_preprocessingPheno, and
names(phenoResult) shows the main returned components. The
combinedData element groups the combined longitudinal objects, and
phenoResult$combinedData$phenoBeta is the key downstream table for modeling.
It combines phenotype columns and CpG beta values in a single data frame that
can be passed to the GLM or GLMM wrappers.
Arguments:
phenoFile, betaPath, mPath, and cnPath define the phenotype table and
the three methylation matrices that are aligned in this step.SampleID = "Sample_Name" is the key used to match samples across the loaded
objects.timeVar = "Timepoint", timepoints = "1,2", and
combineTimepoints = "1,2" define the separate exports and the combined
longitudinal object.outputPheno, outputRData, outputRDataMerge, and outputDir still need
to be specified even when saveOutputs = FALSE, because they define the
canonical output structure for the wrapper.sexColumn = "Sex" identifies the sex variable used by the helper functions.verbose = FALSE, logs = FALSE, and saveOutputs = FALSE keep the example
focused on the returned object rather than file writing.The Clock Foundation export objects are returned in the same result, which makes it possible to inspect the export-ready tables before deciding whether they should be used in a reporting or export workflow.
names(phenoResult$clockFoundation)
#> [1] "betaCSV" "phenoCF"
head(phenoResult$clockFoundation$phenoCF)
#> id Timepoint Sex Age
#> 1 S1 1 Female 20
#> 2 S2 1 Male 22
#> 3 S3 2 Female 21
#> 4 S4 2 Male 23The clockFoundation element contains the export-ready objects, and
phenoResult$clockFoundation$phenoCF is the phenotype table prepared for
Clock Foundation use.
In interactive local use, the most important habit is to inspect the returned objects after each step. In practice, the following checks are usually enough to confirm that the workflow is behaving as expected:
class(result) to confirm which wrapper produced the objectnames(result) to see the main return elementshead() on the phenotype-like tablesclass() or dim() on the methylation objects and matricesThe next vignette shows how to use saveOutputs = TRUE and the Makefile
workflow. The file-based vignette also describes the regression and
mixed-effects model structures used downstream, including how phenotype-specific
PRS terms are added through prsMap.
Date the vignette was generated.
#> [1] "2026-04-22 21:02:06 EDT"Wallclock time spent generating the vignette.
#> Time difference of 1.358 minsR session information.
#> R version 4.6.0 RC (2026-04-17 r89917)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
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#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] parallel stats4 stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
#> [2] IlluminaHumanMethylation450kmanifest_0.4.0
#> [3] minfi_1.57.0
#> [4] bumphunter_1.53.0
#> [5] locfit_1.5-9.12
#> [6] iterators_1.0.14
#> [7] foreach_1.5.2
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#> [9] XVector_0.51.0
#> [10] SummarizedExperiment_1.41.1
#> [11] Biobase_2.71.0
#> [12] MatrixGenerics_1.23.0
#> [13] matrixStats_1.5.0
#> [14] GenomicRanges_1.63.2
#> [15] Seqinfo_1.1.0
#> [16] IRanges_2.45.0
#> [17] S4Vectors_0.49.2
#> [18] BiocGenerics_0.57.1
#> [19] generics_0.1.4
#> [20] dnaEPICO_0.99.14
#> [21] BiocStyle_2.39.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_2.0.0
#> [3] magrittr_2.0.5 GenomicFeatures_1.63.2
#> [5] rmarkdown_2.31 BiocIO_1.21.0
#> [7] vctrs_0.7.3 multtest_2.67.0
#> [9] memoise_2.0.1 Rsamtools_2.27.2
#> [11] DelayedMatrixStats_1.33.0 RCurl_1.98-1.18
#> [13] ENmix_1.47.3 askpass_1.2.1
#> [15] htmltools_0.5.9 S4Arrays_1.11.1
#> [17] AnnotationHub_4.1.0 dynamicTreeCut_1.63-1
#> [19] curl_7.1.0 Rhdf5lib_1.99.6
#> [21] RPMM_1.25 SparseArray_1.11.13
#> [23] rhdf5_2.55.16 sass_0.4.10
#> [25] KernSmooth_2.23-26 nor1mix_1.3-3
#> [27] bslib_0.10.0 httr2_1.2.2
#> [29] plyr_1.8.9 impute_1.85.0
#> [31] cachem_1.1.0 GenomicAlignments_1.47.0
#> [33] lifecycle_1.0.5 pkgconfig_2.0.3
#> [35] Matrix_1.7-5 R6_2.6.1
#> [37] fastmap_1.2.0 digest_0.6.39
#> [39] siggenes_1.85.0 reshape_0.8.10
#> [41] minfiData_0.57.0 AnnotationDbi_1.73.1
#> [43] irlba_2.3.7 ExperimentHub_3.1.0
#> [45] geneplotter_1.89.0 RSQLite_2.4.6
#> [47] base64_2.0.2 filelock_1.0.3
#> [49] httr_1.4.8 abind_1.4-8
#> [51] compiler_4.6.0 beanplot_1.3.1
#> [53] rngtools_1.5.2 bit64_4.8.0
#> [55] doParallel_1.0.17 BiocParallel_1.45.0
#> [57] DBI_1.3.0 gplots_3.3.0
#> [59] HDF5Array_1.39.1 MASS_7.3-65
#> [61] openssl_2.4.0 rappdirs_0.3.4
#> [63] DelayedArray_0.37.1 rjson_0.2.23
#> [65] caTools_1.18.3 gtools_3.9.5
#> [67] tools_4.6.0 otel_0.2.0
#> [69] rentrez_1.2.4 glue_1.8.1
#> [71] quadprog_1.5-8 h5mread_1.3.3
#> [73] restfulr_0.0.16 nlme_3.1-169
#> [75] rhdf5filters_1.23.3 grid_4.6.0
#> [77] cluster_2.1.8.2 tzdb_0.5.0
#> [79] preprocessCore_1.73.0 tidyr_1.3.2
#> [81] data.table_1.18.2.1 hms_1.1.4
#> [83] xml2_1.5.2 BiocVersion_3.23.1
#> [85] pillar_1.11.1 limma_3.67.3
#> [87] genefilter_1.93.0 splines_4.6.0
#> [89] dplyr_1.2.1 BiocFileCache_3.1.0
#> [91] lattice_0.22-9 survival_3.8-6
#> [93] rtracklayer_1.71.3 bit_4.6.0
#> [95] GEOquery_2.79.0 annotate_1.89.0
#> [97] tidyselect_1.2.1 knitr_1.51
#> [99] bookdown_0.46 xfun_0.57
#> [101] scrime_1.3.7 statmod_1.5.1
#> [103] yaml_2.3.12 evaluate_1.0.5
#> [105] codetools_0.2-20 cigarillo_1.1.0
#> [107] tibble_3.3.1 BiocManager_1.30.27
#> [109] cli_3.6.6 xtable_1.8-8
#> [111] jquerylib_0.1.4 Rcpp_1.1.1-1
#> [113] dbplyr_2.5.2 png_0.1-9
#> [115] XML_3.99-0.23 readr_2.2.0
#> [117] blob_1.3.0 mclust_6.1.2
#> [119] doRNG_1.8.6.3 sparseMatrixStats_1.23.0
#> [121] bitops_1.0-9 illuminaio_0.53.0
#> [123] purrr_1.2.2 crayon_1.5.3
#> [125] rlang_1.2.0 KEGGREST_1.51.1As package developers, we try to explain clearly how to use our packages and in
which order to use the functions. But R and Bioconductor have a steep
learning curve, so it is critical to learn where to ask for help. We would like
to highlight the Bioconductor support site
as the main resource for getting help. Please remember to use the dnaEPICO
tag and check the
older posts. If you want to
receive help, please provide a small reproducible example and your session
information so the source of the problem can be tracked efficiently.
Aryee, Martin J, Andrew E Jaffe, Hector Corrada Bravo, Christine Ladd-Acosta, Andrew P Feinberg, Kasper D Hansen, and Rafael A Irizarry. 2014. “Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.” Bioinformatics 30 (10): 1363–9. https://doi.org/10.1093/bioinformatics/btu049.
Fortin, Jean-Philippe, Aurélie Labbe, Mathieu Lemire, Brent W Zanke, Thomas J Hudson, Elana J Frtig, Celia MT Greenwood, and Kasper D Hansen. 2014. “Functional normalization of 450k methylation array data improves replication in large cancer studies.” Genome Biology 15 (11): 503. https://doi.org/10.1186/s13059-014-0503-2.
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