dnaEPICO Overview
2026-05-24
Package
dnaEPICO 0.99.20
1 Introduction
This vignette provides a visual overview of the main dnaEPICO functions roles. It shows how the major functions organise their inputs, internal processing stages, and outputs.
The diagrams are intended as orientation maps. They are useful when deciding which function to run and checking how outputs move between stages. For detailed arguments and executable examples, use the local-use and pipeline-use vignettes.
Read each overview as follows:
- The arrows show the expected flow from inputs to derived objects and files.
- The grouped regions show the main analysis tasks performed inside a function.
- The final nodes show the outputs that are returned to R or written to disk.
- The same function can be used interactively or as part of a file-based
pipeline, depending on the value of
saveOutputs.
2 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 pipeline. 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. Downstream statistical modelling relies on base R and CRAN frameworks, including generalised linear models and linear mixed-effects models. Users are expected to have basic familiarity with R, Bioconductor pipelines, command-line execution, 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.
3 Functions
The sections below describe the role of each main function in the package dnaEPICO. They are ordered according to the analysis path: preprocessing, surrogate-variable estimation, phenotype preparation, cross-sectional modelling, longitudinal modelling, and report generation.
3.1 preprocessingMinfiEwasWater()
preprocessingMinfiEwasWater() reads the phenotype table and IDAT files, builds the methylation
objects, performs quality control and normalisation, filters probes, and
estimates cell composition.
Its role in the package is to create analysis-ready methylation data:
- Main inputs: phenotype metadata, IDAT files, array annotation, and filtering settings.
- Main processing tasks: import, quality control, normalisation, probe filtering, metric extraction, and cell-composition estimation.
- Main outputs: filtered phenotype data,
RGSet, beta values, M-values, copy-number values, quality-control figures, andphenoLC.
100%
3.2 svaEnmix()
svaEnmix() estimates surrogate variables from control-probe information and
adds them to the phenotype table. This step helps represent technical
variation that may otherwise influence downstream association models.
Its role is to prepare covariates for batch and technical adjustment:
- Main inputs: the phenotype table from preprocessing and the saved
RGSet. - Main processing tasks: control-probe extraction, surrogate-variable estimation, association checks with array-position metadata, and phenotype merging.
- Main outputs: surrogate-variable matrices, an updated phenotype table, and diagnostic summaries.
100%
3.3 preprocessingPheno()
preprocessingPheno() aligns phenotype information with methylation metrics.
It prepares timepoint-specific data, combines longitudinal records, and creates export-ready files for external methylation-age tools.
Its role is to organise samples and methylation matrices for modelling:
- Main inputs: phenotype data, beta values, M-values, copy-number values, and timepoint settings.
- Main processing tasks: sample alignment, timepoint splitting, longitudinal merging, and Clock Foundation export preparation.
- Main outputs: timepoint-specific phenotype-methylation tables, combined longitudinal tables, and Clock Foundation input files.
100%
3.4 methylationGLM_T1()
methylationGLM_T1() fits cross-sectional methylation association models. It
is designed for analyses where one phenotype is tested against CpG-level
methylation while adjusting for selected covariates.
Its role is to run single-timepoint association testing:
- Main inputs: a phenotype-methylation table, phenotype variables, covariates, optional PRS mappings, and annotation settings.
- Main processing tasks: model preparation, CpG-GLM model fitting, diagnostic plotting, result summarisation, multiple-testing adjustment, and annotation.
- Main outputs: model objects, summary tables, diagnostic figures, significant-CpG exports, and annotated workbooks.
100%
3.5 methylationGLMM_T1T2()
methylationGLMM_T1T2() fits longitudinal mixed-effects models. It supports
repeated-measures designs by including participant-level random effects and
timepoint-related terms.
Its role is to model methylation change across repeated observations:
- Main inputs: combined longitudinal phenotype-methylation data, participant identifiers, timepoint variables, phenotypes, covariates, and annotation settings.
- Main processing tasks: longitudinal model preparation, CpG-LME model fitting, interaction testing, diagnostic plotting, summarisation, and annotation.
- Main outputs: mixed-model objects, interaction summaries, diagnostic figures, significant-interaction exports, and annotated longitudinal workbooks.
100%
3.6 dnamReport()
dnamReport() assembles the main tables, figures, logs, and model summaries
into a report website. It can be run after preprocessing and modelling
outputs have been written to disk.
Its role is to make the package outputs easier to inspect and share:
- Main inputs: phenotype tables, QC figures, ENmix figures, SVA figures, metric figures, model annotation tables, detection P-value data, and logs.
- Main processing tasks: input validation, report file preparation, Quarto rendering, and post-processing.
- Main outputs: a rendered report site with organised tabs for data, preprocessing, modelling, logs, and supporting diagnostics.
100%
4 Summary
The dnaEPICO package is organised around functions with clear roles.
The preprocessing function create quality-controlled methylation data and
analysis-ready phenotype tables. The modelling functions fit cross-sectional and
longitudinal association models. The report function gathers the resulting
tables, figures, and logs into a browsable output.
In practice, the main functions are:
- run
preprocessingMinfiEwasWater()to prepare methylation objects and QC outputs, - run
svaEnmix()when control-probe surrogate variables are needed, - run
preprocessingPheno()to prepare modelling tables, - run
methylationGLM_T1()ormethylationGLMM_T1T2()for association testing, and - run
dnamReport()to review the completed outputs.
5 Basics
Date the vignette was generated.
#> [1] "2026-05-24 17:37:57 EDT"Wallclock time spent generating the vignette.
#> Time difference of 0.258 secsR 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-3.24-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [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: 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_1.59.0
#> [4] bumphunter_1.55.0
#> [5] locfit_1.5-9.12
#> [6] iterators_1.0.14
#> [7] foreach_1.5.2
#> [8] Biostrings_2.81.2
#> [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_1.65.0
#> [15] Seqinfo_1.3.0
#> [16] IRanges_2.47.1
#> [17] S4Vectors_0.51.2
#> [18] BiocGenerics_0.59.3
#> [19] generics_0.1.4
#> [20] dnaEPICO_0.99.20
#> [21] BiocStyle_2.41.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_2.0.0
#> [3] magrittr_2.0.5 GenomicFeatures_1.65.0
#> [5] rmarkdown_2.31 BiocIO_1.23.3
#> [7] vctrs_0.7.3 multtest_2.69.0
#> [9] memoise_2.0.1 Rsamtools_2.29.0
#> [11] DelayedMatrixStats_1.35.0 RCurl_1.98-1.18
#> [13] ENmix_1.49.0 askpass_1.2.1
#> [15] htmltools_0.5.9 S4Arrays_1.13.0
#> [17] BiocBaseUtils_1.15.1 AnnotationHub_4.3.0
#> [19] dynamicTreeCut_1.63-1 curl_7.1.0
#> [21] Rhdf5lib_2.1.0 RPMM_1.25
#> [23] SparseArray_1.13.2 rhdf5_2.57.0
#> [25] sass_0.4.10 KernSmooth_2.23-26
#> [27] nor1mix_1.3-3 bslib_0.11.0
#> [29] httr2_1.2.2 plyr_1.8.9
#> [31] impute_1.87.0 cachem_1.1.0
#> [33] GenomicAlignments_1.49.0 lifecycle_1.0.5
#> [35] pkgconfig_2.0.3 Matrix_1.7-5
#> [37] R6_2.6.1 fastmap_1.2.0
#> [39] digest_0.6.39 siggenes_1.87.0
#> [41] reshape_0.8.10 minfiData_0.59.0
#> [43] AnnotationDbi_1.75.0 irlba_2.3.7
#> [45] ExperimentHub_3.3.0 geneplotter_1.91.0
#> [47] RSQLite_3.53.1 base64_2.0.2
#> [49] filelock_1.0.3 httr_1.4.8
#> [51] abind_1.4-8 compiler_4.6.0
#> [53] beanplot_1.3.1 rngtools_1.5.2
#> [55] bit64_4.8.2 doParallel_1.0.17
#> [57] BiocParallel_1.47.0 DBI_1.3.0
#> [59] gplots_3.3.0 HDF5Array_1.41.0
#> [61] MASS_7.3-65 openssl_2.4.1
#> [63] rappdirs_0.3.4 DelayedArray_0.39.2
#> [65] rjson_0.2.23 caTools_1.18.3
#> [67] gtools_3.9.5 tools_4.6.0
#> [69] otel_0.2.0 rentrez_1.2.4
#> [71] glue_1.8.1 quadprog_1.5-8
#> [73] h5mread_1.5.0 restfulr_0.0.16
#> [75] nlme_3.1-169 rhdf5filters_1.25.0
#> [77] grid_4.6.0 cluster_2.1.8.2
#> [79] tzdb_0.5.0 preprocessCore_1.75.0
#> [81] tidyr_1.3.2 data.table_1.18.4
#> [83] hms_1.1.4 xml2_1.5.2
#> [85] BiocVersion_3.24.0 pillar_1.11.1
#> [87] limma_3.69.1 genefilter_1.95.0
#> [89] splines_4.6.0 dplyr_1.2.1
#> [91] BiocFileCache_3.3.0 lattice_0.22-9
#> [93] survival_3.8-6 rtracklayer_1.73.0
#> [95] bit_4.6.0 GEOquery_2.81.0
#> [97] annotate_1.91.0 tidyselect_1.2.1
#> [99] knitr_1.51 bookdown_0.46
#> [101] xfun_0.57 scrime_1.3.7
#> [103] statmod_1.5.2 yaml_2.3.12
#> [105] evaluate_1.0.5 codetools_0.2-20
#> [107] cigarillo_1.3.0 tibble_3.3.1
#> [109] BiocManager_1.30.27 cli_3.6.6
#> [111] xtable_1.8-8 jquerylib_0.1.4
#> [113] Rcpp_1.1.1-1.1 dbplyr_2.5.2
#> [115] png_0.1-9 XML_3.99-0.23
#> [117] readr_2.2.0 blob_1.3.0
#> [119] mclust_6.1.2 doRNG_1.8.6.3
#> [121] sparseMatrixStats_1.25.0 bitops_1.0-9
#> [123] illuminaio_0.55.0 purrr_1.2.2
#> [125] crayon_1.5.3 rlang_1.2.0
#> [127] KEGGREST_1.53.05.1 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.