# recountmethylation User’s Guide

# Introduction and overview

`recountmethylation` is an R/Bioconductor package providing resources to access and analyze compilations of public DNA methylation (DNAm) array data from the Gene Expression Omnibus (GEO). The database compilation files span two array platforms and include mined, mapped, and model-based sample metadata. The DNAm signals can be accessed in a variety of formats and data storage types. This User’s Guide shows how to use the `recountmethylation` package, including crucial background about the platforms and datatypes, and runnable examples using 2 small example files. Additional info and more advanced analysis examples are contained in other package vignettes.

## Compilation releases

The `recountmethylation` resource now includes three compilation versions, detailed in the table below. The initial versions only included samples run using the HM450K platform, while newer versions also included samples run using the EPIC platform. These compilations currently include 93,306 samples run on the HM450K platform, 38,122 samples run on the EPIC platform, and 131,428 total samples.

    dft <- data.frame(release = c("first", "second", "third", "total"),
                      version.label = c("0.0.1", "0.0.2", "0.0.3", "all"),
                      date = c("11/20/2020", "01/06/2021", "12/21/2022", "12/21/2022"),
                      hm450k.samples = c(35360, 50400, 7546,
                                         sum(c(35360, 50400, 7546))),
                      epic.samples = c(0, 12650, 25472,
                                       sum(c(0, 12650, 25472))))
    dft$combined.samples <- dft$hm450k.samples + dft$epic.samples
    knitr::kable(dft, align = "c")

release | version.label | date | hm450k.samples | epic.samples | combined.samples
--- | --- | --- | --- | --- | ---
first | 0.0.1 | 11/20/2020 | 35360 | 0 | 35360
second | 0.0.2 | 01/06/2021 | 50400 | 12650 | 63050
third | 0.0.3 | 12/21/2022 | 7546 | 25472 | 33018
total | all | 12/21/2022 | 93306 | 38122 | 131428

## Database files and access

Database compilation file download and access is managed by the `get_db` functions, where the DNAm array platform type using the `platform` argument (see `?get_db` for details). Both HM450K and EPIC/HM850K platforms are currently supported (see below for platform details). Note you will need between 50-180 Gb of disk space to store a single database file. Files pair sample metadata and assay data in various formats, including `HDF5-SummarizedExperiment` database directories, and `HDF5` database files with the `.h5` extension.

The databases are located at https://methylation.recount.bio/, and file details are viewable as follows:

    sm <- as.data.frame(smfilt(get_servermatrix()))
    if(is(sm, "data.frame")){knitr::kable(sm, align = "c")}

filename | date | time | size (bytes)
--- | --- | --- | ---
remethdb_h5-rg_epic_0-0-2_1589820348.h5 | 14-Nov-2023 | 19:32 | 66751358297
remethdb_h5se-gm_epic_0-0-2_1589820348 | 14-Nov-2023 | 17:18 | `assays.h5` = 56956363488;`se.rds` = 8475111
remethdb_h5se-gr_epic_0-0-2_1607018051 | 14-Nov-2023 | 20:08 | `assays.h5` = 82090895411;`se.rds` = 8475201
remethdb_h5se-rg_epic_0-0-2_1589820348 | 14-Nov-2023 | 20:20 | `assays.h5` = 68707689800;`se.rds` = 3059883
remethdb_h5-rg_hm450k_0-0-2_1607018051.h5 | 14-Nov-2023 | 22:21 | 193342823766
remethdb_h5se-gm_hm450k_0-0-2_1607018051 | 14-Nov-2023 | 17:52 | `assays.h5` = 130935841655;`se.rds` = 5372091
remethdb_h5se-gr_hm450k_0-0-2_1607018051 | 14-Nov-2023 | 23:01 | `assays.h5` = 184355830172;`se.rds` = 5372008
remethdb_h5se-rg_hm450k_0-0-2_1607018051 | 14-Nov-2023 | 18:29 | `assays.h5` = 164788908310;`se.rds` = 3179962
remethdb-h5se_gr-test_0-0-1_1590090412 | 14-Nov-2023 | 21:53 | `assays.h5` = 132596;`se.rds` = 68522
remethdb-h5_rg-test_0-0-1_1590090412.h5 | 14-Nov-2023 | 21:28 | 252757

## ExperimentHub integration

The DNAm array database files are indexed on `ExperimentHub`, and are viewable as follows. Note, the cache needs to be set with `R_user_dir()` per instructions here.

    cache.path <- tools::R_user_dir("recountmethylation")
    setExperimentHubOption("CACHE", cache.path)
    hub <- ExperimentHub::ExperimentHub()                    # connect to the hubs
    rmdat <- AnnotationHub::query(hub, "recountmethylation") # query the hubs

In addition to using the `getdb` functions, the `HDF5` (“.h5”” extension) files may be downloaded from the hubs.

    fpath <- rmdat[["EH3778"]] # download with default caching
    rhdf5::h5ls(fpath)         # load the h5 file

Note that whether downloads use the hubs or `getdb` functions, caching is implemented to check for previously downloaded database files.

## Disclaimer

Please note the following disclaimer, which also shows when `recountmethylation` is loaded:

    Databases accessed with `recountmethylation` contain data from GEO
    (ncbi.nlm.nih.gov/geo/), a live public database where alterations to
    online records can cause discrepancies with stored data over time.
    We cannot guarantee the accuracy of stored data, and advise users
    cross-check their findings with latest available records.

# Background

This section includes essential background about DNAm array platforms, assays and file types, and sample metadata.

## DNAm arrays

Databases include human samples run on the Illumina Infinium HM450K BeadArray platform. HM450K is a popular 2-channel platform that probes over 480,000 CpG loci genome-wide, with enriched coverage at CG islands, genes, and enhancers [1]. The more recently released EPIC/HM850K platform contains an expanded probe set targeting over 850,000 CpGs, including more than 90% of the HM450K probes, with greater coverage of potential intergenic regulatory regions [2].

Array processing generates 2 intensity files (IDATs) per sample, one each for the red and green color channels. These raw files also contain control signals useful for quality evaluations [3]. The BeadArray probes use either of 2 bead technologies, known as Type I and Type II, where the majority (72%) of probes use the latter. For Type II probes, a single bead assay informs a single probe, while Type I probes use 2 beads each. Practically, this means the bead-specific matrices found in `RGChannelSet` objects are larger than the probe-specific matrices found in derived object types (e.g. for HM450K samples, 622,399 assays for red/green signal matrices versus 485,512 assays for methylated/unmethylated signal, DNAm fractions matrices, see below).

## `SummarizedExperiment` object classes

DNAm array sample IDATs can be read into an R session as an object of class `RGChannelSet`, a type of `SummarizedExperiment`. These objects support analyses of high-throughput genomics datasets, and they include slots for assay matrices, sample metadata, and experiment metadata. During a typical workflow, normalization and preprocessing convert `RGChannelSet` objects into new types like `MethylSet` and `RatioSet`. While not all IDAT information is accessible from every object type (e.g. only `RGChannelSet`s can contain control assays), derived objects like `MethylSet`s and `RatioSet`s may be smaller and/or faster to access.

Three `SummarizedExperiment` databases are provided as `HDF5-SummarizedExperiment` files, including an unnormalized `RGChannelSet` (red/green signals), an unnormalized `MethylSet` (methylated/unmethylated signals) and a normalized `GenomicRatioSet` (DNAm fractions). For the latter, DNAm fractions (logit2 Beta-values, or M-values) were normalized using the out-of-band signal or “noob” method, an effective within-sample normalization that removes signal artifacts [4].

## Database file types

Database files are stored as either `HDF5` or `HDF5-SummarizedExperiment`. For most R users, the latter files will be most convenient to work with. `HDF5`, or hierarchical data format 5, combines compression and chunking for convenient handling of large datasets. `HDF5-SummarizedExperiment` files combine the benefits of `HDF5` and `SummarizedExperiment` entities using a DelayedArray-powered backend. Once an `HDF5-SummarizedExperiment` file is loaded, it can be treated similarly to a `SummarizedExperiment` object in active memory. That is, summary and subset operations execute rapidly, and realization of large data chunks in active memory is delayed until called for by the script (see examples).

## Sample metadata

Sample metadata are included with DNAm assays in the database files. Currently, metadata variables include GEO record IDs for samples (GSM) and studies (GSE), sample record titles, learned labels for tissue and disease, sample type predictions from the MetaSRA-pipeline, and DNAm model-based predictions for age, sex, and blood cell types. Access sample metadata from `SummarizedExperiment` objects using the `pData` minfi function (see examples). Examples in the `data_analyses` vignette illustrate some ways to utilize the provided sample metadata.

Provided metadata derives from the GSE-specific SOFT files, which contain experiment, sample, and platform metadata. Considerable efforts were made to learn, harmonize, and predict metadata labels. Certain types of info lacking in the `recountmethylation` metadata may be available in the SOFT files, especially if it is sample non-specific (e.g. methods text, PubMed ID, etc.) or redundant with DNAm-derived metrics (e.g. DNAm summaries, predicted sex, etc.).

It is good practice to validate the harmonized metadata with original metadata records, especially where labels are ambiguous or there is insufficient information for a given query. GEO GSM and GSE records can be viewed from a browser, or SOFT files may be downloaded directly. Packages like GEOmetadb and GEOquery are also useful to query and summarize GEO metadata.

# `HDF5-SummarizedExperiment` example

This example shows basic handling for `HDF5-SummarizedExperiment` (a.k.a. “h5se”) files. For these files, the `getdb` function returns the loaded file. Thanks to a `DelayedArray` backend, even full-sized `h5se` databases can be treated as if they were fully loaded into active memory.

## Obtain the test database

The test `h5se` dataset includes sample metadata and noob-normalized DNAm fractions (Beta-values) for chromosome 22 probes for 2 samples. Datasets can be downloaded using the `getdb` series of functions (see `?getdb` for details), where the `dfp` argument specifies the download destination. The test `h5se` file is included in the package “inst” directory, and can be loaded as follows.

    dn <- "remethdb-h5se_gr-test_0-0-1_1590090412"
    path <- system.file("extdata", dn, package = "recountmethylation")
    h5se.test <- HDF5Array::loadHDF5SummarizedExperiment(path)

## Inspect and summarize the database

Common characterization functions can be used on the dataset after it has been loaded. These include functions for `SummarizedExperiment`-like objects, such as the `getBeta`, `pData`, and `getAnnotation` minfi functions. First, inspect the dataset using standard functions like `class`, `dim`, and `summary` as follows.

    class(h5se.test) # inspect object class

    ## [1] "GenomicRatioSet"
    ## attr(,"package")
    ## [1] "minfi"

    dim(h5se.test) # get object dimensions

    ## [1] 8552    2

    summary(h5se.test) # summarize dataset components

    ## [1] "GenomicRatioSet object of length 8552 with 0 metadata columns"

Access the sample metadata for the 2 available samples using `pData`.

    h5se.md <- minfi::pData(h5se.test) # get sample metadata
    dim(h5se.md)                       # get metadata dimensions

    ## [1]  2 19

    colnames(h5se.md) # get metadata column names

    ##  [1] "gsm"            "gsm_title"      "gseid"          "disease"
    ##  [5] "tissue"         "sampletype"     "arrayid_full"   "basename"
    ##  [9] "age"            "predage"        "sex"            "predsex"
    ## [13] "predcell.CD8T"  "predcell.CD4T"  "predcell.NK"    "predcell.Bcell"
    ## [17] "predcell.Mono"  "predcell.Gran"  "storage"

Next get CpG probe-specific DNAm fractions, or “Beta-values”, with `getBeta` (rows are probes, columns are samples).

    h5se.bm <- minfi::getBeta(h5se.test) # get dnam fractions
    dim(h5se.bm)                         # get dnam fraction dimensions

    ## [1] 8552    2

    colnames(h5se.bm) <- h5se.test$gsm       # assign sample ids to dnam fractions
    knitr::kable(head(h5se.bm), align = "c") # show table of dnam fractions

| GSM1038308 | GSM1038309
--- | --- | ---
cg00017461 | 0.9807283 | 0.9746836
cg00077299 | 0.3476970 | 0.3456837
cg00079563 | 0.8744652 | 0.9168005
cg00087182 | 0.9763206 | 0.9760947
cg00093544 | 0.0225112 | 0.0265087
cg00101350 | 0.9736359 | 0.9789818

Access manifest information for probes with `getAnnotation`. This includes the bead addresses, probe type, and genome coordinates and regions. For full details about the probe annotations, consult the minfi and Illumina platform documentation.

    an <- minfi::getAnnotation(h5se.test) # get platform annotation
    dim(an)                               # get annotation dimensions

    ## [1] 8552   33

    colnames(an) # get annotation column names

    ##  [1] "chr"                      "pos"
    ##  [3] "strand"                   "Name"
    ##  [5] "AddressA"                 "AddressB"
    ##  [7] "ProbeSeqA"                "ProbeSeqB"
    ##  [9] "Type"                     "NextBase"
    ## [11] "Color"                    "Probe_rs"
    ## [13] "Probe_maf"                "CpG_rs"
    ## [15] "CpG_maf"                  "SBE_rs"
    ## [17] "SBE_maf"                  "Islands_Name"
    ## [19] "Relation_to_Island"       "Forward_Sequence"
    ## [21] "SourceSeq"                "Random_Loci"
    ## [23] "Methyl27_Loci"            "UCSC_RefGene_Name"
    ## [25] "UCSC_RefGene_Accession"   "UCSC_RefGene_Group"
    ## [27] "Phantom"                  "DMR"
    ## [29] "Enhancer"                 "HMM_Island"
    ## [31] "Regulatory_Feature_Name"  "Regulatory_Feature_Group"
    ## [33] "DHS"

    ant <- as.matrix(t(an[c(1:4), c(1:3, 5:6, 9, 19, 24, 26)])) # subset annotation
    knitr::kable(ant, align = "c")                              # show annotation table

| cg00017461 | cg00077299 | cg00079563 | cg00087182
--- | --- | --- | --- | ---
chr | chr22 | chr22 | chr22 | chr22
pos | 30663316 | 18632618 | 43253521 | 24302043
strand | - | + | + | +
AddressA | 31616369 | 13618325 | 65630302 | 37797387
AddressB | 70798487 | 37626331 | 55610348 | 20767312
Type | I | I | I | I
Relation_to_Island | OpenSea | N_Shore | Island | N_Shore
UCSC_RefGene_Name | OSM | USP18 | ARFGAP3;ARFGAP3 | GSTT2B;GSTT2
UCSC_RefGene_Group | TSS1500 | TSS200 | TSS200;TSS200 | Body;Body

# `HDF5` database and example

To provide more workflow options, bead-specific red and green signal data have been provided with sample metadata in an `HDF5`/`h5` file. This example shows how to handle objects of this type with `recountmethylation`.

## Obtain the test database

The test `h5` file includes metadata and bead-specific signals from chromosome 22 for the same 2 samples as in the `h5se` test file. Note `getdb` functions for `h5` files simply return the database path. Since the test `h5` file has also been included in the package “inst” folder, get the path to load the file as follows.

    dn <- "remethdb-h5_rg-test_0-0-1_1590090412.h5"     # get the h5se directory name
    h5.test <- system.file("extdata", "h5test", dn,
                        package = "recountmethylation") # get the h5se dir path

## Inspect and summarize the database

Use the file path to read data into an `RGChannelSet` with the `getrg` function. Setting `all.gsm = TRUE` obtains data for all samples in the database files, while passing a vector of GSM IDs to `gsmv` argument will query a subset of available samples. Signals from all available probes are retrieved by default, and probe subsets can be obtained by passing a vector of valid bead addresses to the `cgv` argument.

    h5.rg <- getrg(dbn = h5.test, all.gsm = TRUE) # get red/grn signals from an h5 db

To avoid exhausting active memory with the full-sized `h5` dataset, provide either `gsmv` or `cgv` to `getrg`, and set either `all.cg` or `all.gsm` to FALSE (see `?getrg` for details).

As in the previous example, use `pData` and `getAnnotation` to get sample metadata and array manifest information, respectively. Access the green and red signal matrices in the `RGChannelSet` with the `getRed` and `getGreen` minfi functions.

    h5.red <- minfi::getRed(h5.rg)     # get red signal matrix
    h5.green <- minfi::getGreen(h5.rg) # get grn signal matrix
    dim(h5.red)                        # get dimensions of red signal matrix

    ## [1] 11162     2

    knitr::kable(head(h5.red), align = "c") # show first rows of red signal matrix

| GSM1038308 | GSM1038309
--- | --- | ---
10601475 | 1234 | 1603
10603366 | 342 | 344
10603418 | 768 | 963
10605304 | 2368 | 2407
10605460 | 3003 | 3322
10608343 | 357 | 399

    knitr::kable(head(h5.green), align = "c") # show first rows of grn signal matrix

| GSM1038308 | GSM1038309
--- | --- | ---
10601475 | 6732 | 8119
10603366 | 288 | 356
10603418 | 267 | 452
10605304 | 4136 | 4395
10605460 | 1395 | 1762
10608343 | 840 | 1269

    identical(rownames(h5.red), rownames(h5.green)) # check cpg probe names identical

    ## [1] TRUE

Rows in these signal matrices map to bead addresses rather than probe IDs. These matrices have more rows than the `h5se` test Beta-value matrix because any type I probes use data from 2 beads each.

# Validate DNAm datasets

This section demonstrates validation using the test databases. Full code to reproduce this section is provided but not evaluated, as it involves a download from the GEO servers. As the disclaimer notes, it is good practice to validate data against the latest available GEO files. This step may be most useful for newer samples published close to the end compilation date (through November 7, 2020 for current version), which may be more prone to revisions at initial publication.

## Download and read IDATs from the GEO database server

Use the `gds_idat2rg` function to download IDATs for the 2 test samples and load these into a new `RGChannelSet` object. Do this by passing a vector of GSM IDs to `gsmv` and the download destination to `dfp`. (note, chunks in this section are fully executable, but not evaluated for this vignette).

    # download from GEO
    dlpath <- tempdir()                                     # get a temp dir path
    gsmv <- c("GSM1038308", "GSM1038309")                   # set sample ids to identify
    geo.rg <- gds_idat2rg(gsmv, dfp = dlpath)               # load sample idats into rgset
    colnames(geo.rg) <- gsub("\\_.*", "", colnames(geo.rg)) # assign sample ids to columns

## Compare DNAm signals

Extract the red and green signal matrices from `geo.rg`.

    geo.red <- minfi::getRed(geo.rg)      # get red signal matrix
    geo.green <- minfi::getGreen(geo.rg)  # get grn signal matrix

Match indices and labels between the GEO and `h5` test signal matrices.

    int.addr <- intersect(rownames(geo.red), rownames(h5.red)) # get probe address ids
    geo.red <- geo.red[int.addr,]                              # subset geo rgset red signal
    geo.green <- geo.green[int.addr,]                          # subset gro rgset grn signal
    geo.red <- geo.red[order(match(rownames(geo.red), rownames(h5.red))),]
    geo.green <- geo.green[order(match(rownames(geo.green), rownames(h5.green))),]
    identical(rownames(geo.red), rownames(h5.red))             # check identical addresses, red
    identical(rownames(geo.green), rownames(h5.green))         # check identical addresses, grn
    class(h5.red) <- "integer"; class(h5.green) <- "integer"   # set matrix data classes to integer

Finally, compare the signal matrix data.

    identical(geo.red, h5.red) # compare matrix signals, red

    identical(geo.green, h5.green) # compare matrix signals, grn

## Compare DNAm Beta-values

Before comparing the GEO-downloaded data to data from the `h5se.test` database, normalize the data using the same out-of-band or “noob” normalization technique that was used to generate data in the `h5se` database.

    geo.gr <- minfi::preprocessNoob(geo.rg) # get normalized se data

Next, extract the Beta-values.

    geo.bm <- as.matrix(minfi::getBeta(geo.gr)) # get normalized dnam fractions matrix

Now match row and column labels and indices.

    h5se.bm <- as.matrix(h5se.bm) # set dnam fractions to matrix
    int.cg <- intersect(rownames(geo.bm), rownames(h5se.bm))
    geo.bm <- geo.bm[int.cg,]     # subset fractions on shared probe ids
    geo.bm <- geo.bm[order(match(rownames(geo.bm), rownames(h5se.bm))),]

Finally, compare the two datasets.

    identical(summary(geo.bm), summary(h5se.bm)) # check identical summary values

    identical(rownames(geo.bm), rownames(h5se.bm)) # check identical probe ids

# Troubleshooting and tips

This section describes how to address potential issues with accessing the database files or working with the `DelayedArray` based objects locally.

## Issue: large file downloads don’t complete

If repeated attempts to download the database compilation files fail, you may try the following:

- First ensure your internet connection is stable and there is sufficient space at the download destination for the database file.

- Second, try increasing your timeout duration beyond the default before repeating the download attempt with `getdb`. Check the current timeout for an R session with `getOptions('timeout')`, then manually increase the timeout duration with `options(timeout = new.time)`.

- Finally, you may attempt to download a server file using command line calls to your system terminal or console. For instance, on a Mac you might try `wget -r <file_url>`. If this doesn’t work, you can again attempt to increase the timeout duration and repeat the download attempt.

## Issue: unexpected function behaviors for `DelayedArray` inputs

Unexpected function behaviors may arise when using `DelayedArray`-based inputs. These essentially arise from lacking interoperativity between normal matrices and the `DelayedArray`-based matrices. Known examples include:

- `minfi::detectionP()`:

Throws error for specific subsets of data, such as for queries of exactly 50 samples.

    detectionP(rg[,1:50]) # get detection pvalues from rgset
    "Error in .local(Red, Green, locusNames, controlIdx, TypeI.Red, TypeI.Green, dim(Red_grid) == dim(detP_sink_grid) are not all TRUE"

- `minfi::preprocessFunnorm()`:

Throws error when called for an `RGChannelSet` of type `HDF5-SummarizedExperiment`.

    preprocessFunnorm(rg) # get noob-normalized data
    "Error: 'preprocessFunnorm()' only supports matrix-backed minfi objects.""

These and other related errors may be addressed by instantiating the data query, or the data chunk, as a new non-`DelayedArray` object. For example, remake a subset of the full `h5se` dataset, `rg`, as follows.

    rg.h5se <- loadHDF5SummarizedExperiment(rg.path)        # full h5se RGChannelSet
    rg.sub <- rg.h5se[,c(1:20)]                             # subset samples of interest
    rg.new <- RGChannelSet(Red = getRed(rg.sub),
                           Green = getGreen(rg.sub),
                           annotation = annotation(rg.sub)) # re-make as non-DA object
    gr <- preprocessFunnorm(rg.new)                         # repeat preprocessing

Alternatively, non-`DelayedArray` `RGChannelSet` objects can be readily generated from the full `h5` `RGChannelSet` database with the provided function `getrg()`.

# Get more help

Consult the Data Analyses vignette and main manuscript for analysis examples and details about data compilations.

# Session info

    sessionInfo()

    ## R version 4.6.0 (2026-04-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 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
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [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: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] ExperimentHub_3.3.0
    ##  [2] AnnotationHub_4.3.0
    ##  [3] BiocFileCache_3.3.0
    ##  [4] dbplyr_2.5.2
    ##  [5] basilisk_1.25.0
    ##  [6] reticulate_1.46.0
    ##  [7] limma_3.69.1
    ##  [8] gridExtra_2.3
    ##  [9] knitr_1.51
    ## [10] recountmethylation_1.23.0
    ## [11] HDF5Array_1.41.0
    ## [12] h5mread_1.5.0
    ## [13] rhdf5_2.57.1
    ## [14] DelayedArray_0.39.3
    ## [15] SparseArray_1.13.2
    ## [16] S4Arrays_1.13.0
    ## [17] abind_1.4-8
    ## [18] Matrix_1.7-5
    ## [19] ggplot2_4.0.3
    ## [20] minfiDataEPIC_1.39.0
    ## [21] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
    ## [22] IlluminaHumanMethylationEPICmanifest_0.3.0
    ## [23] minfiData_0.59.0
    ## [24] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
    ## [25] IlluminaHumanMethylation450kmanifest_0.4.0
    ## [26] minfi_1.59.1
    ## [27] bumphunter_1.55.1
    ## [28] locfit_1.5-9.12
    ## [29] iterators_1.0.14
    ## [30] foreach_1.5.2
    ## [31] Biostrings_2.81.2
    ## [32] XVector_0.53.0
    ## [33] SummarizedExperiment_1.43.0
    ## [34] Biobase_2.73.1
    ## [35] MatrixGenerics_1.25.0
    ## [36] matrixStats_1.5.0
    ## [37] GenomicRanges_1.65.0
    ## [38] Seqinfo_1.3.0
    ## [39] IRanges_2.47.2
    ## [40] S4Vectors_0.51.3
    ## [41] BiocGenerics_0.59.6
    ## [42] generics_0.1.4
    ## [43] 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    farver_2.1.2
    ##   [7] rmarkdown_2.31            BiocIO_1.23.3
    ##   [9] vctrs_0.7.3               multtest_2.69.0
    ##  [11] memoise_2.0.1             Rsamtools_2.29.0
    ##  [13] DelayedMatrixStats_1.35.0 RCurl_1.98-1.18
    ##  [15] askpass_1.2.1             htmltools_0.5.9
    ##  [17] BiocBaseUtils_1.15.1      curl_7.1.0
    ##  [19] Rhdf5lib_2.1.0            sass_0.4.10
    ##  [21] nor1mix_1.3-3             bslib_0.11.0
    ##  [23] httr2_1.2.2               plyr_1.8.9
    ##  [25] cachem_1.1.0              buildtools_1.0.0
    ##  [27] GenomicAlignments_1.49.0  lifecycle_1.0.5
    ##  [29] pkgconfig_2.0.3           R6_2.6.1
    ##  [31] fastmap_1.2.0             digest_0.6.39
    ##  [33] siggenes_1.87.0           reshape_0.8.10
    ##  [35] AnnotationDbi_1.75.0      RSQLite_3.53.1
    ##  [37] base64_2.0.2              filelock_1.0.3
    ##  [39] labeling_0.4.3            mgcv_1.9-4
    ##  [41] httr_1.4.8                compiler_4.6.0
    ##  [43] beanplot_1.3.1            rngtools_1.5.2
    ##  [45] withr_3.0.2               bit64_4.8.2
    ##  [47] S7_0.2.2                  BiocParallel_1.47.0
    ##  [49] DBI_1.3.0                 MASS_7.3-65
    ##  [51] openssl_2.4.1             rappdirs_0.3.4
    ##  [53] rjson_0.2.23              tools_4.6.0
    ##  [55] rentrez_1.2.4             glue_1.8.1
    ##  [57] quadprog_1.5-8            restfulr_0.0.16
    ##  [59] nlme_3.1-169              rhdf5filters_1.25.0
    ##  [61] grid_4.6.0                gtable_0.3.6
    ##  [63] tzdb_0.5.0                preprocessCore_1.75.0
    ##  [65] tidyr_1.3.2               data.table_1.18.4
    ##  [67] hms_1.1.4                 xml2_1.5.2
    ##  [69] BiocVersion_3.24.0        pillar_1.11.1
    ##  [71] genefilter_1.95.0         splines_4.6.0
    ##  [73] dplyr_1.2.1               lattice_0.22-9
    ##  [75] survival_3.8-6            rtracklayer_1.73.0
    ##  [77] bit_4.6.0                 GEOquery_2.81.0
    ##  [79] annotate_1.91.0           tidyselect_1.2.1
    ##  [81] maketools_1.3.2           xfun_0.57
    ##  [83] scrime_1.3.7              statmod_1.5.2
    ##  [85] yaml_2.3.12               evaluate_1.0.5
    ##  [87] codetools_0.2-20          cigarillo_1.3.0
    ##  [89] tibble_3.3.1              BiocManager_1.30.27
    ##  [91] cli_3.6.6                 xtable_1.8-8
    ##  [93] jquerylib_0.1.4           Rcpp_1.1.1-1.1
    ##  [95] dir.expiry_1.21.0         png_0.1-9
    ##  [97] XML_3.99-0.23             readr_2.2.0
    ##  [99] blob_1.3.0                mclust_6.1.2
    ## [101] doRNG_1.8.6.3             sparseMatrixStats_1.25.0
    ## [103] bitops_1.0-9              scales_1.4.0
    ## [105] illuminaio_0.55.0         purrr_1.2.2
    ## [107] crayon_1.5.3              rlang_1.2.0
    ## [109] KEGGREST_1.53.0

# Works Cited

1.
Sandoval, J., Heyn, H. A., Moran, S., Serra-Musach, J., Pujana, M. A., Bibikova, M., and Esteller, M. (2011). Validation of a DNA methylation microarry for 450,000 CpG sites in the human genome. Epigenetics 6, 692–702. Available at: http://www.landesbioscience.com/journals/epigenetics/article/16196/?nocache=1384341162.
2.
Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., Van Djik, S., Muhlhausler, B., Stirzaker, C., and Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology 17. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5055731/ [Accessed April 19, 2019].
3.
Illumina Genome Studio Methylation Module v1.8 (2010). Available at: https://support.illumina.com/content/dam/illumina-support/documents/documentation/software_documentation/genomestudio/genomestudio-2011-1/genomestudio-methylation-v1-8-user-guide-11319130-b.pdf [Accessed August 20, 2019].
4.
Triche, T. J., Weisenberger, D. J., Van Den Berg, D., Laird, P. W., and Siegmund, K. D. (2013). Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Research 41, e90.