recountmethylation User’s Guide (original) (raw)

Contents

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")

Database files and access

Database compilation file download and access is managed by the get_dbfunctions, 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-SummarizedExperimentdatabase directories, and HDF5 database files with the .h5 extension.

The databases are located athttps://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")}

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 recountmethylationis 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 classRGChannelSet, 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 RGChannelSets can contain control assays), derived objects like MethylSets and RatioSets may be smaller and/or faster to access.

Three SummarizedExperiment databases are provided asHDF5-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).

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

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

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

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:

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"

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 manuscriptfor analysis examples and details about data compilations.

Session info

sessionInfo()
## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.21-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] ExperimentHub_2.16.0                               
##  [2] AnnotationHub_3.16.0                               
##  [3] BiocFileCache_2.16.0                               
##  [4] dbplyr_2.5.0                                       
##  [5] basilisk_1.20.0                                    
##  [6] reticulate_1.42.0                                  
##  [7] limma_3.64.0                                       
##  [8] gridExtra_2.3                                      
##  [9] knitr_1.50                                         
## [10] recountmethylation_1.18.0                          
## [11] HDF5Array_1.36.0                                   
## [12] h5mread_1.0.0                                      
## [13] rhdf5_2.52.0                                       
## [14] DelayedArray_0.34.0                                
## [15] SparseArray_1.8.0                                  
## [16] S4Arrays_1.8.0                                     
## [17] abind_1.4-8                                        
## [18] Matrix_1.7-3                                       
## [19] ggplot2_3.5.2                                      
## [20] minfiDataEPIC_1.33.0                               
## [21] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
## [22] IlluminaHumanMethylationEPICmanifest_0.3.0         
## [23] minfiData_0.53.0                                   
## [24] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1 
## [25] IlluminaHumanMethylation450kmanifest_0.4.0         
## [26] minfi_1.54.0                                       
## [27] bumphunter_1.50.0                                  
## [28] locfit_1.5-9.12                                    
## [29] iterators_1.0.14                                   
## [30] foreach_1.5.2                                      
## [31] Biostrings_2.76.0                                  
## [32] XVector_0.48.0                                     
## [33] SummarizedExperiment_1.38.0                        
## [34] Biobase_2.68.0                                     
## [35] MatrixGenerics_1.20.0                              
## [36] matrixStats_1.5.0                                  
## [37] GenomicRanges_1.60.0                               
## [38] GenomeInfoDb_1.44.0                                
## [39] IRanges_2.42.0                                     
## [40] S4Vectors_0.46.0                                   
## [41] BiocGenerics_0.54.0                                
## [42] generics_0.1.3                                     
## [43] BiocStyle_2.36.0                                   
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.5.0             BiocIO_1.18.0            
##   [3] bitops_1.0-9              filelock_1.0.3           
##   [5] tibble_3.2.1              basilisk.utils_1.20.0    
##   [7] preprocessCore_1.70.0     XML_3.99-0.18            
##   [9] lifecycle_1.0.4           lattice_0.22-7           
##  [11] MASS_7.3-65               base64_2.0.2             
##  [13] scrime_1.3.5              magrittr_2.0.3           
##  [15] sass_0.4.10               rmarkdown_2.29           
##  [17] jquerylib_0.1.4           yaml_2.3.10              
##  [19] doRNG_1.8.6.2             askpass_1.2.1            
##  [21] DBI_1.2.3                 RColorBrewer_1.1-3       
##  [23] quadprog_1.5-8            purrr_1.0.4              
##  [25] RCurl_1.98-1.17           rappdirs_0.3.3           
##  [27] GenomeInfoDbData_1.2.14   rentrez_1.2.3            
##  [29] genefilter_1.90.0         annotate_1.86.0          
##  [31] DelayedMatrixStats_1.30.0 codetools_0.2-20         
##  [33] xml2_1.3.8                tidyselect_1.2.1         
##  [35] UCSC.utils_1.4.0          farver_2.1.2             
##  [37] beanplot_1.3.1            illuminaio_0.50.0        
##  [39] GenomicAlignments_1.44.0  jsonlite_2.0.0           
##  [41] multtest_2.64.0           survival_3.8-3           
##  [43] tools_4.5.0               Rcpp_1.0.14              
##  [45] glue_1.8.0                xfun_0.52                
##  [47] mgcv_1.9-3                dplyr_1.1.4              
##  [49] withr_3.0.2               BiocManager_1.30.25      
##  [51] fastmap_1.2.0             rhdf5filters_1.20.0      
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##  [55] R6_2.6.1                  colorspace_2.1-1         
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##  [61] httr_1.4.7                pkgconfig_2.0.3          
##  [63] gtable_0.3.6              blob_1.2.4               
##  [65] siggenes_1.82.0           htmltools_0.5.8.1        
##  [67] bookdown_0.43             scales_1.3.0             
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##  [71] rjson_0.2.23              nlme_3.1-168             
##  [73] curl_6.2.2                cachem_1.1.0             
##  [75] BiocVersion_3.21.1        AnnotationDbi_1.70.0     
##  [77] restfulr_0.0.15           GEOquery_2.76.0          
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##  [85] readr_2.1.5               tinytex_0.57             
##  [87] GenomicFeatures_1.60.0    magick_2.8.6             
##  [89] cli_3.6.4                 compiler_4.5.0           
##  [91] Rsamtools_2.24.0          rlang_1.1.6              
##  [93] crayon_1.5.3              rngtools_1.5.2           
##  [95] labeling_0.4.3            nor1mix_1.3-3            
##  [97] mclust_6.1.1              plyr_1.8.9               
##  [99] BiocParallel_1.42.0       munsell_0.5.1            
## [101] dir.expiry_1.16.0         hms_1.1.3                
## [103] sparseMatrixStats_1.20.0  bit64_4.6.0-1            
## [105] Rhdf5lib_1.30.0           KEGGREST_1.48.0          
## [107] statmod_1.5.0             memoise_2.0.1            
## [109] bslib_0.9.0               bit_4.6.0

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].

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.