# Creation of a HDF5-backed RangedSummarizedExperiment from methylation array files

#### 14 January 2024

# Contents

    library(methodical)
    library(TCGAbiolinks)

## 0.1 Introduction

Most functions from `methodical` take as input RangedSummarizedExperiment objects with methylation data. If there are many samples, there can be many millions or, in the case of WGBS data, even billions of data points within a DNA methylation dataset. It can be unfeasible to load all this data into memory at once. This problem can be overcome by using DelayedArrays backed by HDF5 files, enabling data to be read into memory only as needed. `methodical` enables these HDF5-backed RangedSummarizedExperiments objects to be created flexibly from bedGraph-like files, such as those produced by WGBS and RRBS experiments, using the `makeMethRSEFromBedgraphs` function or from methylation array files, such as those for the Illumina methylation arrays, using the `makeMethRSEFromArrayFiles` function.

Here we demonstrate how a methylation RangedSummarizedExperiment (meth RSEs) can be constructed easily using `makeMethRSEFromArrayFiles` from
Illumina HumanMethylation450 array files downloaded from TCGA. .

We first download four files with methylation array data for colon adenocarcinoma (COAD) from TCGA using TCGAbiolinks.

    # Create a vector with the barcdoes of four files to download
    coad_barcodes = c(
      "TCGA-D5-5538-01A-01D-1651-05", "TCGA-A6-5660-01A-01D-1651-05",
      "TCGA-F4-6855-01A-11D-1926-05", "TCGA-DM-A288-01A-11D-A16X-05")

    # Create a GDC query to download methylation files
    coad_query = GDCquery(project = "TCGA-COAD", data.category = "DNA Methylation",
      platform = "Illumina Human Methylation 450",
      data.type = "Methylation Beta Value", sample.type = "Primary Tumor",
      barcode = coad_barcodes)
    #> --------------------------------------
    #> o GDCquery: Searching in GDC database
    #> --------------------------------------
    #> Genome of reference: hg38
    #> --------------------------------------------
    #> oo Accessing GDC. This might take a while...
    #> --------------------------------------------
    #> ooo Project: TCGA-COAD
    #> --------------------
    #> oo Filtering results
    #> --------------------
    #> ooo By platform
    #> ooo By data.type
    #> ooo By barcode
    #> ooo By sample.type
    #> ----------------
    #> oo Checking data
    #> ----------------
    #> ooo Checking if there are duplicated cases
    #> ooo Checking if there are results for the query
    #> -------------------
    #> o Preparing output
    #> -------------------

    # Download files
    GDCdownload(query = coad_query, directory = tempdir())
    #> Downloading data for project TCGA-COAD
    #> GDCdownload will download 4 files. A total of 52.652855 MB
    #> Downloading as: Sun_Jan_14_18_00_21_2024.tar.gz
    #>
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    # Get paths to the methylation array files
    meth_array_files = list.files(paste(tempdir(), "TCGA-COAD", sep = "/"), recursive = TRUE, full.names = TRUE)

    # Match the file names to sample submitter ID using the query result
    coad_query_metadata = getResults(coad_query)
    names(meth_array_files) = coad_query_metadata$sample.submitter_id[
      match(basename(meth_array_files), coad_query_metadata$file_name)]

## 0.2 Creation of methylation RSE from array files

Generally, files from methylation array files will have one column with the name of the probe used to measure methylation at a particular CpG site and another column with the value associated with this probe. `makeMethRSEFromArrayFiles` takes a GRanges object for the `probe_ranges` argument to match the names of these probe to genomic coordinates when creating the meth RSE. This should be a GRanges object with one metadata object called “name” which gives the names of the probes and matches the names in the array files.

One such GRanges object, `infinium_450k_probe_granges_hg19` for mapping probes from the Illumina HumanMethylation450 array to genomic coordinates of the hg19 human genome build is provided as part of the package. If mapping the probes to a genome build besides hg19 or using a different methylation array, another similar GRanges object with the coordinates of probes and a metadata column with their names should be provided.

We’ll take a look at one of the methylation files we just downloaded and at `infinium_450k_probe_granges_hg19`.

    # View the top of one of the COAD methylation array files
    system2("head", meth_array_files[1])

    # Load infinium_450k_probe_granges_hg19 and view the first few ranges
    data("infinium_450k_probe_granges_hg19")
    head(infinium_450k_probe_granges_hg19)
    #> GRanges object with 6 ranges and 1 metadata column:
    #>       seqnames    ranges strand |        name
    #>          <Rle> <IRanges>  <Rle> | <character>
    #>   [1]     chr1     15865      * |  cg13869341
    #>   [2]     chr1     18827      * |  cg14008030
    #>   [3]     chr1     29407      * |  cg12045430
    #>   [4]     chr1     29425      * |  cg20826792
    #>   [5]     chr1     29435      * |  cg00381604
    #>   [6]     chr1     68849      * |  cg20253340
    #>   -------
    #>   seqinfo: 24 sequences from an unspecified genome; no seqlengths

Now we’ll create the meth RSE for the four files we downloaded. It is assumed that the first column in the methylation array files is the name of the probes and the second is the values associated with them but this can be changed using the `probe_name_column` and `beta_value_column` arguments.

Files can be processed in parallel using BiocParallel with the `BPPARAM` argument, which defaults to `BiocParallel::bpparam()`.

We’ll save the HDF5 directory within the temporary directory using the `hdf5_dir`, but this should be saved somewhere else if the meth RSE will be reused.

    # Create a data.frame with sample metadata associated with the array files
    sample_metadata = data.frame(cancer_type = rep("COAD",  4),
      sample_type = rep("primary_tumor", 4), row.names = names(meth_array_files))

    # Create meth RSE for COAD
    coad_meth_rse = makeMethRSEFromArrayFiles(
      array_files = meth_array_files,
      probe_name_column = 1, beta_value_column = 2,
      probe_ranges = infinium_450k_probe_granges_hg19,
      sample_metadata = sample_metadata, BPPARAM = BiocParallel::bpparam(),
      hdf5_dir = paste(tempdir(), "coad_meth_array_rse", sep = "/"))
    #> Writing chunk 1 out of 4
    #> Writing chunk 2 out of 4
    #> Writing chunk 3 out of 4
    #> Writing chunk 4 out of 4
    #> Serialize RangedSummarizedExperiment object to RDS file:
    #>   /tmp/Rtmp9R7E3S/coad_meth_array_rse/se.rds

    # Display coad_meth_rse
    print(coad_meth_rse)
    #> class: RangedSummarizedExperiment
    #> dim: 482421 4
    #> metadata(0):
    #> assays(1): beta
    #> rownames: NULL
    #> rowData names(1): name
    #> colnames(4): TCGA-DM-A288-01A TCGA-A6-5660-01A TCGA-D5-5538-01A
    #>   TCGA-F4-6855-01A
    #> colData names(2): cancer_type sample_type

## 0.3 SessionInfo

    sessionInfo()
    #> R Under development (unstable) (2023-11-11 r85510)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 22.04.3 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
    #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
    #>
    #> 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] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] TCGAbiolinks_2.31.2         rtracklayer_1.63.0
    #>  [3] AnnotationHub_3.11.1        BiocFileCache_2.11.1
    #>  [5] dbplyr_2.4.0                rhdf5_2.47.1
    #>  [7] TumourMethData_1.1.0        DESeq2_1.43.1
    #>  [9] methodical_0.99.21          SummarizedExperiment_1.33.2
    #> [11] Biobase_2.63.0              MatrixGenerics_1.15.0
    #> [13] matrixStats_1.2.0           ggplot2_3.4.4
    #> [15] GenomicRanges_1.55.1        GenomeInfoDb_1.39.5
    #> [17] IRanges_2.37.0              S4Vectors_0.41.3
    #> [19] BiocGenerics_0.49.1         BiocStyle_2.31.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] DBI_1.2.1                   bitops_1.0-7
    #>   [3] biomaRt_2.59.0              rlang_1.1.3
    #>   [5] magrittr_2.0.3              compiler_4.4.0
    #>   [7] RSQLite_2.3.4               png_0.1-8
    #>   [9] vctrs_0.6.5                 rvest_1.0.3
    #>  [11] stringr_1.5.1               pkgconfig_2.0.3
    #>  [13] crayon_1.5.2                fastmap_1.1.1
    #>  [15] magick_2.8.2                XVector_0.43.1
    #>  [17] labeling_0.4.3              utf8_1.2.4
    #>  [19] Rsamtools_2.19.2            rmarkdown_2.25
    #>  [21] tzdb_0.4.0                  purrr_1.0.2
    #>  [23] bit_4.0.5                   xfun_0.41
    #>  [25] zlibbioc_1.49.0             cachem_1.0.8
    #>  [27] jsonlite_1.8.8              progress_1.2.3
    #>  [29] blob_1.2.4                  rhdf5filters_1.15.1
    #>  [31] DelayedArray_0.29.0         Rhdf5lib_1.25.1
    #>  [33] BiocParallel_1.37.0         prettyunits_1.2.0
    #>  [35] parallel_4.4.0              R6_2.5.1
    #>  [37] stringi_1.8.3               bslib_0.6.1
    #>  [39] jquerylib_0.1.4             iterators_1.0.14
    #>  [41] Rcpp_1.0.12                 bookdown_0.37
    #>  [43] knitr_1.45                  downloader_0.4
    #>  [45] R.utils_2.12.3              readr_2.1.5
    #>  [47] Matrix_1.6-5                tidyselect_1.2.0
    #>  [49] abind_1.4-5                 yaml_2.3.8
    #>  [51] codetools_0.2-19            curl_5.2.0
    #>  [53] plyr_1.8.9                  lattice_0.22-5
    #>  [55] tibble_3.2.1                withr_2.5.2
    #>  [57] KEGGREST_1.43.0             evaluate_0.23
    #>  [59] xml2_1.3.6                  ExperimentHub_2.11.1
    #>  [61] Biostrings_2.71.1           pillar_1.9.0
    #>  [63] BiocManager_1.30.22         filelock_1.0.3
    #>  [65] foreach_1.5.2               generics_0.1.3
    #>  [67] RCurl_1.98-1.14             hms_1.1.3
    #>  [69] BiocVersion_3.19.1          munsell_0.5.0
    #>  [71] scales_1.3.0                glue_1.7.0
    #>  [73] tools_4.4.0                 BiocIO_1.13.0
    #>  [75] data.table_1.14.10          GenomicAlignments_1.39.1
    #>  [77] locfit_1.5-9.8              XML_3.99-0.16
    #>  [79] grid_4.4.0                  tidyr_1.3.0
    #>  [81] AnnotationDbi_1.65.2        colorspace_2.1-0
    #>  [83] GenomeInfoDbData_1.2.11     HDF5Array_1.31.1
    #>  [85] restfulr_0.0.15             cli_3.6.2
    #>  [87] rappdirs_0.3.3              fansi_1.0.6
    #>  [89] S4Arrays_1.3.2              dplyr_1.1.4
    #>  [91] gtable_0.3.4                R.methodsS3_1.8.2
    #>  [93] TCGAbiolinksGUI.data_1.23.0 sass_0.4.8
    #>  [95] digest_0.6.34               SparseArray_1.3.3
    #>  [97] farver_2.1.1                rjson_0.2.21
    #>  [99] memoise_2.0.1               htmltools_0.5.7
    #> [101] R.oo_1.25.0                 lifecycle_1.0.4
    #> [103] httr_1.4.7                  mime_0.12
    #> [105] bit64_4.0.5