# Local Use of dnaEPICO

# Introduction

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:

1. `preprocessingMinfiEwasWater()`
2. `svaEnmix()`
3. `preprocessingPheno()`

All examples use `saveOutputs = FALSE`, which keeps the workflow in memory and avoids writing file outputs.

# Citation

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.

- If you use `preprocessingMinfiEwasWater()`, please cite Aryee et al. (2014); Fortin et al. (2017); Xu et al. (2021); Pidsley et al. (2013); Murat et al. (2020); Maksimovic et al. (2012); Fortin et al. (2014); Triche et al. (2013); Touleimat and Tost (2012).
- If you use `svaEnmix()`, please cite Aryee et al. (2014); Xu et al. (2021).

# Required knowledge

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 post.

## Probe-exclusion reference files

The lists of excluded probes depend on the Illumina methylation-array platform.

- For Illumina HumanMethylationEPIC v2.0, use the cross-reactive probe-exclusion file from Peters et al. (2024).

- For Illumina MethylationEPIC, also known as the 850k array, use the probe-exclusion resources from Pidsley et al. (2016). The supplementary files commonly used together are 13059_2016_1066_MOESM1_ESM.csv, 13059_2016_1066_MOESM4_ESM.csv, 13059_2016_1066_MOESM5_ESM.csv, and 13059_2016_1066_MOESM6_ESM.csv.

- For Illumina HumanMethylation450k, use the cross-reactive and polymorphic probe resources from Chen et al. (2013).

- Multiple probe-exclusion files can be supplied as a semicolon-separated value in `probeExclusionPath`. `dnaEPICO` reads probe IDs from each file, uses `probeExclusionIdColumn` when supplied, or otherwise auto-detects common probe-ID columns such as `ProbeID`, `TargetID`, `IlmnID`, and `Name`. The unique union of all probe IDs is then used to filter the normalised object.

- For EPICv2, setting `useEpicV2Manifest = TRUE` also retrieves the expanded Peters et al. manifest from AnnotationHub resource AH116484. Probes flagged in selected manifest columns are added to the same exclusion set. By default, probes flagged by `CH_WGBS_evidence`, `CH_BLAT`, or `MissingPos` are removed, while `MismatchPos` is retained unless explicitly enabled.

# Installation

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")

# Step 1: preprocessingMinfiEwasWater

The preprocessing function 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.

### Create example input files

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 probe-exclusion reference used during probe filtering.

    preprocessing_inputs <- dnaEPICO:::exampleMinfiIdatInputsDnaEpico(n = 6L)

    names(preprocessing_inputs)
    #> [1] "tempDir"            "idatFolder"         "phenoFile"
    #> [4] "targets"            "arrayType"          "annotationVersion"
    #> [7] "probeExclusionPath"
    preprocessing_inputs$tempDir
    #> [1] "/tmp/RtmptdU6hy/dnaEPICO-idat-example-2d425e309a25"
    basename(preprocessing_inputs$phenoFile)
    #> [1] "pheno.csv"
    head(basename(list.files(preprocessing_inputs$idatFolder, full.names = TRUE)), 4)
    #> [1] "5723646052_R02C02_Grn.idat" "5723646052_R02C02_Red.idat"
    #> [3] "5723646052_R04C01_Grn.idat" "5723646052_R04C01_Red.idat"
    basename(preprocessing_inputs$probeExclusionPath)
    #> [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 function, `targets` is the same phenotype information already loaded into memory, `arrayType` and `annotationVersion` describe the array platform, and `probeExclusionPath` points to the probe-exclusion 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$tempDir, "figures"),
        detPThreshold = 1,
        normMethods = "quantile",
        sexColumn = "Sex",
        removeSexMismatch = FALSE,
        pvalThreshold = 1,
        chrToRemove = "",
        snpsToRemove = "SBE",
        mafThreshold = 1,
        probeExclusionPath = preprocessing_inputs$probeExclusionPath,
        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   M 5723646052           R02C02
    #> 2    GroupA_2   F 5723646052           R04C01
    #> 3    GroupB_3   M 5723646052           R05C02
    #> 4    GroupB_1   F 5723646053           R04C02
    #> 5    GroupA_1   F 5723646053           R05C02
    #> 6    GroupB_2   F 5723646053           R06C02

The returned object has class `dnaEPICO_preprocessingMinfiEwasWater`. `names(preprocessResult)` lists the main elements returned by the function. 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.
- `probeExclusionPath` supplies the probe-exclusion reference used during filtering.
- `sexColumn` identifies reported sex for comparison with methylation-predicted sex. `removeSexMismatch = FALSE` retains and reports confirmed mismatches; `TRUE` removes samples only when both values are known and disagree. Missing or unknown sex values are retained.
- `plotGroupVar`, `lcRef`, and `phenoOrder` control 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"         "validation"
    head(preprocessResult$lcData$phenoLC[, 1:6])
    #>   Sample_Name Sex          Basename Sentrix_ID Sentrix_Position Sample_Well
    #> 1    GroupA_3   M 5723646052_R02C02 5723646052           R02C02          H5
    #> 2    GroupA_2   F 5723646052_R04C01 5723646052           R04C01          D5
    #> 3    GroupB_3   M 5723646052_R05C02 5723646052           R05C02          C6
    #> 4    GroupB_1   F 5723646053_R04C02 5723646053           R04C02          F7
    #> 5    GroupA_1   F 5723646053_R05C02 5723646053           R05C02          G7
    #> 6    GroupB_2   F 5723646053_R06C02 5723646053           R06C02          H7

In 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.

# Step 2: svaEnmix

The SVA function 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.

### Create example input files

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"            "probeExclusionPath"
    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 TRUE

`sva_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 `probeExclusionPath` reference. In this step, the function 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  100 % 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] 4 3
    head(svaResult$mergedPheno[, 1:min(6, ncol(svaResult$mergedPheno))])
    #>         Sample_Name Sample_Well Sample_Plate Sample_Group Pool_ID person
    #> 1 5723646052_R02C02          H5           NA       GroupA      NA    id3
    #> 2 5723646052_R04C01          D5           NA       GroupA      NA    id2
    #> 3 5723646052_R05C02          C6           NA       GroupB      NA    id3
    #> 4 5723646053_R04C02          F7           NA       GroupB      NA    id1

The returned object has class `dnaEPICO_svaEnmix`, and `names(svaResult)` shows the main SVA components returned by the function. 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] "technicalTerms"  "fullModels"      "reducedModels"   "droptermSteps"
    #>  [9] "anovaFull"       "anovaReduced"    "warnings"

This object contains the fitted models and ANOVA summaries used to assess how the surrogate variables relate to Sentrix chip and position effects.

# Step 3: preprocessingPheno

The phenotype-preparation function 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.

### Create example input files

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$tempDir
    #> [1] "/tmp/RtmptdU6hy/dnaEPICO-preprocessingPheno-example-2d427fdcd8e7"
    basename(c(
        pheno_inputs$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"
    #>  [3] "timepointData"           "combinedData"
    #>  [5] "methylationScale"        "methylationLabel"
    #>  [7] "methylationObjectPrefix" "clockFoundation"
    #>  [9] "savedFiles"              "logFile"
    names(phenoResult$combinedData)
    #> [1] "timepoints"              "suffix"
    #> [3] "pheno"                   "phenoMethylation"
    #> [5] "methylationScale"        "methylationObjectPrefix"
    #> [7] "phenoB"
    head(phenoResult$combinedData$phenoMethylation[, 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       0.58

The returned object has class `dnaEPICO_preprocessingPheno`, and `names(phenoResult)` shows the main returned components. The `combinedData` element groups the combined longitudinal objects, and `phenoResult$combinedData$phenoMethylation` is the key downstream table for modeling. By default it combines phenotype columns and CpG beta values in a single data frame that can be passed to the GLM or LME functions. Set `methylationScale = "m"` for M-values or `"cn"` for copy-number values.

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 function.
- `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$clockFoundation)
    #> [1] "betaCSV"               "phenoCF"               "methylationBoundaries"
    #> [4] "methylationIssues"     "invalidCpGs"
    head(phenoResult$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  23

The `clockFoundation` element contains the export-ready objects, and `phenoResult$clockFoundation$phenoCF` is the phenotype table prepared for Clock Foundation use.

## Variables used in GLM and LME models

The phenotype-methylation tables returned by `preprocessingPheno()` can be used by `methylationGLM()` or `methylationLME()`. `factorVars` identifies categorical fixed-effect variables, while `scaleVars` identifies numeric fixed-effect variables to centre and divide by their sample standard deviations before model fitting. For example, `factorVars = "Sex,TreatmentGroup"` treats those variables as categorical and `scaleVars = "Age,PC1"` standardises `Age` and `PC1`.

For GLM and LME analyses, the selected variables must be numeric, have at least two finite observations, and have non-zero sample variance. A variable cannot be listed in both `factorVars` and `scaleVars`. The applied centres and sample standard deviations are stored in the annotated workbook metadata.

# Summary

In interactive local use, the most important habit is to inspect the returned objects after each step. In practice, the following checks are enough to confirm that the workflow is behaving as expected:

- `class(result)` to confirm which function produced the object
- `names(result)` to see the main return elements
- `head()` on the phenotype-like tables
- `class()` or `dim()` on the methylation objects and matrices

The 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`.

# Basics

Date the vignette was generated.

    #> [1] "2026-07-19 16:05:06 UTC"

Wallclock time spent generating the vignette.

    #> Time difference of 1.014 mins

`R` session information.

    #> R version 4.6.1 (2026-06-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 26.04 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
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    #>
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    #> 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_1.59.1
    #>  [4] bumphunter_1.55.1
    #>  [5] locfit_1.5-9.12
    #>  [6] iterators_1.0.14
    #>  [7] foreach_1.5.2
    #>  [8] Biostrings_2.81.5
    #>  [9] XVector_0.53.0
    #> [10] SummarizedExperiment_1.43.0
    #> [11] Biobase_2.73.1
    #> [12] MatrixGenerics_1.25.0
    #> [13] matrixStats_1.5.0
    #> [14] GenomicRanges_1.65.1
    #> [15] Seqinfo_1.3.0
    #> [16] IRanges_2.47.2
    #> [17] S4Vectors_0.51.5
    #> [18] BiocGenerics_0.59.10
    #> [19] generics_0.1.4
    #> [20] dnaEPICO_0.99.34
    #> [21] BiocStyle_2.41.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RColorBrewer_1.1-3        sys_3.4.3
    #>   [3] jsonlite_2.0.0            magrittr_2.0.5
    #>   [5] GenomicFeatures_1.65.0    rmarkdown_2.31
    #>   [7] BiocIO_1.23.3             vctrs_0.7.3
    #>   [9] multtest_2.69.0           memoise_2.0.1
    #>  [11] Rsamtools_2.29.0          DelayedMatrixStats_1.35.0
    #>  [13] RCurl_1.98-1.19           ENmix_1.49.3
    #>  [15] askpass_1.2.1             htmltools_0.5.9
    #>  [17] S4Arrays_1.13.0           BiocBaseUtils_1.15.1
    #>  [19] AnnotationHub_4.3.2       dynamicTreeCut_1.63-1
    #>  [21] curl_7.1.0                Rhdf5lib_2.1.0
    #>  [23] RPMM_1.25                 SparseArray_1.13.2
    #>  [25] rhdf5_2.57.1              sass_0.4.10
    #>  [27] KernSmooth_2.23-26        nor1mix_1.3-3
    #>  [29] bslib_0.11.0              httr2_1.3.0
    #>  [31] plyr_1.8.9                impute_1.87.0
    #>  [33] cachem_1.1.0              buildtools_1.0.0
    #>  [35] GenomicAlignments_1.49.1  lifecycle_1.0.5
    #>  [37] pkgconfig_2.0.3           Matrix_1.7-5
    #>  [39] R6_2.6.1                  fastmap_1.2.0
    #>  [41] digest_0.6.39             siggenes_1.87.0
    #>  [43] reshape_0.8.10            minfiData_0.59.0
    #>  [45] AnnotationDbi_1.75.0      irlba_2.3.7
    #>  [47] ExperimentHub_3.3.1       geneplotter_1.91.0
    #>  [49] RSQLite_3.53.3            base64_2.0.2
    #>  [51] filelock_1.0.3            httr_1.4.8
    #>  [53] abind_1.4-8               compiler_4.6.1
    #>  [55] beanplot_1.3.1            rngtools_1.5.2
    #>  [57] doParallel_1.0.17         bit64_4.8.2
    #>  [59] BiocParallel_1.47.0       DBI_1.3.0
    #>  [61] gplots_3.3.0              HDF5Array_1.41.0
    #>  [63] MASS_7.3-66               openssl_2.4.2
    #>  [65] rappdirs_0.3.4            DelayedArray_0.39.3
    #>  [67] rjson_0.2.23              caTools_1.18.3
    #>  [69] gtools_3.9.5              tools_4.6.1
    #>  [71] otel_0.2.0                rentrez_1.2.4
    #>  [73] glue_1.8.1                quadprog_1.5-8
    #>  [75] h5mread_1.5.0             restfulr_0.0.17
    #>  [77] nlme_3.1-170              rhdf5filters_1.25.0
    #>  [79] grid_4.6.1                cluster_2.1.8.2
    #>  [81] tzdb_0.5.0                preprocessCore_1.75.0
    #>  [83] tidyr_1.3.2               data.table_1.18.4
    #>  [85] hms_1.1.4                 xml2_1.6.0
    #>  [87] BiocVersion_3.24.0        pillar_1.11.1
    #>  [89] limma_3.69.2              genefilter_1.95.0
    #>  [91] splines_4.6.1             dplyr_1.2.1
    #>  [93] BiocFileCache_3.3.0       lattice_0.22-9
    #>  [95] survival_3.8-9            rtracklayer_1.73.0
    #>  [97] bit_4.6.0                 GEOquery_2.81.28
    #>  [99] annotate_1.91.0           tidyselect_1.2.1
    #> [101] maketools_1.3.2           knitr_1.51
    #> [103] xfun_0.60                 scrime_1.3.7
    #> [105] statmod_1.5.2             yaml_2.3.12
    #> [107] evaluate_1.0.5            codetools_0.2-20
    #> [109] cigarillo_1.3.1           tibble_3.3.1
    #> [111] BiocManager_1.30.27       cli_3.6.6
    #> [113] xtable_1.8-8              jquerylib_0.1.4
    #> [115] Rcpp_1.1.2                dbplyr_2.6.0
    #> [117] png_0.1-9                 XML_3.99-0.23
    #> [119] readr_2.2.0               blob_1.3.0
    #> [121] mclust_6.1.3              doRNG_1.8.6.3
    #> [123] sparseMatrixStats_1.25.0  bitops_1.0-9
    #> [125] illuminaio_0.55.0         purrr_1.2.2
    #> [127] crayon_1.5.3              rlang_1.3.0
    #> [129] KEGGREST_1.53.5

## Asking for help

As 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.

# References

Aryee, Martin J, Andrew E Jaffe, Hector Corrada Bravo, et al. 2014. “Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.” Bioinformatics 30 (10): 1363–69. https://doi.org/10.1093/bioinformatics/btu049.
Fortin, Jean-Philippe, Aurélie Labbe, Mathieu Lemire, et al. 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.
Fortin, Jean-Philippe, Timothy Triche Jr., and Kasper D Hansen. 2017. “Preprocessing, Normalization and Integration of the Illumina HumanMethylationEPIC Array with Minfi.” Bioinformatics 33 (4): 558–60. https://doi.org/10.1093/bioinformatics/btw691.
Maksimovic, Jovana, Lavinia Gordon, and Alicia Oshlack. 2012. “SWAN: Subset quantile Within-Array Normalization for Illumina Infinium HumanMethylation450 BeadChips.” Genome Biology 13 (6): R44. https://doi.org/10.1186/gb-2012-13-6-r44.
Murat, Kubra, Björn Grüning, Pawel W Poterlowicz, Gareth Westgate, Desmond J Tobin, and Krzysztof Poterlowicz. 2020. “Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxy.” GigaScience 9 (5): giaa049. https://doi.org/10.1093/gigascience/giaa049.
Pidsley, Ruth, Ching Ching Y Wong, Matteo Volta, Katie Lunnon, Jonathan Mill, and Leonard C Schalkwyk. 2013. “A data-driven approach to preprocessing Illumina 450K methylation array data.” BMC Genomics 14: 293. https://doi.org/10.1186/1471-2164-14-293.
Touleimat, Nizar, and Jörg Tost. 2012. “Complete Pipeline for Infinium(®) Human Methylation 450K BeadChip Data Processing Using Subset Quantile Normalization for Accurate DNA Methylation Estimation.” Epigenomics 4 (3): 325–41. https://doi.org/10.2217/epi.12.21.
Triche, Timothy J, Daniel J Weisenberger, David Van Den Berg, Peter W Laird, and Kimberly D Siegmund. 2013. “Low-level processing of Illumina Infinium DNA Methylation BeadArrays.” Nucleic Acids Research 41 (7): e90. https://doi.org/10.1093/nar/gkt090.
Xu, Zongli, Li Niu, and Jack A Taylor. 2021. “The ENmix DNA methylation analysis pipeline for Illumina BeadChip and comparisons with seven other preprocessing pipelines.” Clinical Epigenetics 13 (1): 216. https://doi.org/10.1186/s13148-021-01207-1.