vignettes/dnaEPICO-local.Rmd
dnaEPICO-local.RmdThis 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:
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 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()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$tempDir
#> [1] "/tmp/RtmpxE8C75/dnaEPICO-idat-example-32b0370050be"
basename(preprocessing_inputs$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$RGSet
save(RGSet, file = sva_rgset_file)
basename(c(sva_targets_file, sva_rgset_file))
#> [1] "sva_targets.csv" "sva_RGSet.RData"
file.exists(c(sva_targets_file, sva_rgset_file))
#> [1] TRUE TRUEsva_inputs is another helper list used to make the
vignette self-contained. names(sva_inputs) shows that it
provides the in-memory RGSet, the matching
targets table, and a crossReactivePath
reference. In this step, the wrapper needs file inputs rather than the
in-memory objects, so the chunk writes targets to
sva_targets.csv and saves the RGSet object to
sva_RGSet.RData.
svaResult <- svaEnmix(
phenoFile = sva_targets_file,
rgsetData = sva_rgset_file,
outputLogs = file.path(tempdir(), "logs"),
SampleID = "Sample_Name",
arrayType = "IlluminaHumanMethylation450k",
annotationVersion = "ilmn12.hg19",
SentrixIDColumn = "Sentrix_ID",
SentrixPositionColumn = "Sentrix_Position",
ctrlSvaPercVar = 0.90,
ctrlSvaFlag = 1,
scriptLabel = "svaEnmix",
display = FALSE,
verbose = FALSE,
logs = FALSE,
saveOutputs = FALSE
)
#> 3 surrogate variables explain 100 % of
#> data variation
class(svaResult)
#> [1] "dnaEPICO_svaEnmix"
names(svaResult)
#> [1] "targets" "RGSet" "svaData" "mergedPheno" "analysisData"
#> [6] "plotFiles" "savedFiles" "logFile"
dim(svaResult$svaData$sva)
#> [1] 4 3
head(svaResult$mergedPheno[, 1:min(6, ncol(svaResult$mergedPheno))])
#> Sample_Name Sample_Well Sample_Plate Sample_Group Pool_ID person
#> 1 GroupA_3 H5 NA GroupA NA id3
#> 2 GroupA_2 D5 NA GroupA NA id2
#> 3 GroupB_3 C6 NA GroupB NA id3
#> 4 GroupB_1 F7 NA GroupB NA id1The returned object has class dnaEPICO_svaEnmix, and
names(svaResult) shows the main SVA components returned by
the wrapper. The svaData$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$tempDir
#> [1] "/tmp/RtmpxE8C75/dnaEPICO-preprocessingPheno-example-32b06f54b410"
basename(c(
pheno_inputs$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] "2026-05-19 00:10:17 UTC"Wallclock time spent generating the vignette.
#> Time difference of 48.756 secsR session information.
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 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-r0.3.26.so; LAPACK version 3.12.0
#>
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#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
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#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> 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_1.52.1
#> [4] bumphunter_1.48.0
#> [5] locfit_1.5-9.12
#> [6] iterators_1.0.14
#> [7] foreach_1.5.2
#> [8] Biostrings_2.74.1
#> [9] XVector_0.46.0
#> [10] SummarizedExperiment_1.36.0
#> [11] Biobase_2.66.0
#> [12] MatrixGenerics_1.18.1
#> [13] matrixStats_1.5.0
#> [14] GenomicRanges_1.58.0
#> [15] GenomeInfoDb_1.42.3
#> [16] IRanges_2.40.1
#> [17] S4Vectors_0.44.0
#> [18] BiocGenerics_0.52.0
#> [19] dnaEPICO_0.99.18
#> [20] BiocStyle_2.34.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_2.0.0
#> [3] magrittr_2.0.5 GenomicFeatures_1.58.0
#> [5] rmarkdown_2.31 fs_2.1.0
#> [7] BiocIO_1.16.0 zlibbioc_1.52.0
#> [9] ragg_1.5.2 vctrs_0.7.3
#> [11] multtest_2.62.0 memoise_2.0.1
#> [13] Rsamtools_2.22.0 DelayedMatrixStats_1.28.1
#> [15] ENmix_1.42.2 RCurl_1.98-1.18
#> [17] askpass_1.2.1 htmltools_0.5.9
#> [19] S4Arrays_1.6.0 AnnotationHub_3.14.0
#> [21] dynamicTreeCut_1.63-1 curl_7.1.0
#> [23] Rhdf5lib_1.28.0 RPMM_1.25
#> [25] SparseArray_1.6.2 rhdf5_2.50.2
#> [27] sass_0.4.10 KernSmooth_2.23-26
#> [29] nor1mix_1.3-3 bslib_0.11.0
#> [31] htmlwidgets_1.6.4 desc_1.4.3
#> [33] plyr_1.8.9 impute_1.80.0
#> [35] cachem_1.1.0 GenomicAlignments_1.42.0
#> [37] lifecycle_1.0.5 pkgconfig_2.0.3
#> [39] Matrix_1.7-5 R6_2.6.1
#> [41] fastmap_1.2.0 GenomeInfoDbData_1.2.13
#> [43] digest_0.6.39 siggenes_1.80.0
#> [45] reshape_0.8.10 minfiData_0.52.0
#> [47] AnnotationDbi_1.68.0 irlba_2.3.7
#> [49] ExperimentHub_2.14.0 geneplotter_1.84.0
#> [51] textshaping_1.0.5 RSQLite_3.52.0
#> [53] base64_2.0.2 filelock_1.0.3
#> [55] httr_1.4.8 abind_1.4-8
#> [57] compiler_4.4.2 beanplot_1.3.1
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#> [63] DBI_1.3.0 gplots_3.3.0
#> [65] HDF5Array_1.34.0 MASS_7.3-65
#> [67] openssl_2.4.1 rappdirs_0.3.4
#> [69] DelayedArray_0.32.0 rjson_0.2.23
#> [71] caTools_1.18.3 gtools_3.9.5
#> [73] tools_4.4.2 otel_0.2.0
#> [75] rentrez_1.2.4 glue_1.8.1
#> [77] quadprog_1.5-8 restfulr_0.0.16
#> [79] nlme_3.1-169 rhdf5filters_1.18.1
#> [81] grid_4.4.2 cluster_2.1.8.2
#> [83] generics_0.1.4 tzdb_0.5.0
#> [85] preprocessCore_1.68.0 tidyr_1.3.2
#> [87] data.table_1.18.4 hms_1.1.4
#> [89] xml2_1.5.2 BiocVersion_3.20.0
#> [91] pillar_1.11.1 limma_3.62.2
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#> [99] rtracklayer_1.66.0 bit_4.6.0
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#> [111] evaluate_1.0.5 codetools_0.2-20
#> [113] tibble_3.3.1 BiocManager_1.30.27
#> [115] cli_3.6.6 xtable_1.8-8
#> [117] systemfonts_1.3.2 jquerylib_0.1.4
#> [119] Rcpp_1.1.1-1.1 dbplyr_2.5.2
#> [121] png_0.1-9 XML_3.99-0.23
#> [123] pkgdown_2.2.0 readr_2.2.0
#> [125] blob_1.3.0 mclust_6.1.2
#> [127] doRNG_1.8.6.3 sparseMatrixStats_1.18.0
#> [129] bitops_1.0-9 illuminaio_0.48.0
#> [131] purrr_1.2.2 crayon_1.5.3
#> [133] rlang_1.2.0 KEGGREST_1.46.0As 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.