This document indicates the different data-sets included in brgedata. The description includes the technology used to create the and the number of samples and features in each one.
brgedata 1.35.0
brgedata includes a collection of BRGE omic and exposome data from the same cohort. The diferent objects guarantees a minimum of samples in common between all sets.
Data available in this R package:
| Data Type | Number of Samples | Number of Features | Technology | Object Name | Class |
|---|---|---|---|---|---|
| Exposome | 110 | 15 | brge_expo |
ExposomeSet |
|
| Transcriptome | 75 | 67528 | Affymetrix HTA 2.0 | brge_gexp |
ExpressionSet |
| Methylome | 20 | 392277 | Illumina Human Methylation 450K | brge_methy |
GenomicRatioSet |
| Proteome | 90 | 47 | brge_prot |
ExpressionSet |
sex and age was included as phenotipic data in each set. Moreover, the ExposomeSet includes asthma status and rhinitis status of each sample.
To load the exposome data, stored in an ExposomeSet, run the follow commands:
data("brge_expo", package = "brgedata")
brge_expo## Object of class 'ExposomeSet' (storageMode: environment)
## . exposures description:
## . categorical: 0
## . continuous: 15
## . exposures transformation:
## . categorical: 0
## . transformed: 0
## . standardized: 0
## . imputed: 0
## . assayData: 15 exposures 110 individuals
## . element names: exp, raw
## . exposures: Ben_p, ..., PCB153
## . individuals: x0001, ..., x0119
## . phenoData: 110 individuals 6 phenotypes
## . individuals: x0001, ..., x0119
## . phenotypes: Asthma, ..., Age
## . featureData: 15 exposures 12 explanations
## . exposures: Ben_p, ..., PCB153
## . descriptions: Family, ..., .imp
## experimentData: use 'experimentData(object)'
## Annotation:The summary of the data contained by brge_expo:
| Data Type | Number of Samples | Number of Features | Technology | Object Name | Class |
|---|---|---|---|---|---|
| Exposome | 110 | 15 | brge_expo |
ExposomeSet |
To load the transcriptome data, saved in an ExpressionSet, run the follow commands:
data("brge_gexp", package = "brgedata")
brge_gexp## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 67528 features, 100 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: x0001 x0002 ... x0139 (100 total)
## varLabels: age sex
## varMetadata: labelDescription
## featureData
## featureNames: TC01000001.hg.1 TC01000002.hg.1 ...
## TCUn_gl000247000001.hg.1 (67528 total)
## fvarLabels: transcript_cluster_id probeset_id ... notes (11 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:The summary of the data contained by brge_gexp:
| Data Type | Number of Samples | Number of Features | Technology | Object Name | Class |
|---|---|---|---|---|---|
| Transcriptome | 75 | 67528 | Affymetrix HTA 2.0 | brge_gexp |
ExpressionSet |
To load the methylation data, encapsulated in a GenomicRatioSet, run the follow commands:
data("brge_methy", package = "brgedata")
brge_methy## class: GenomicRatioSet
## dim: 392277 20
## metadata(0):
## assays(1): Beta
## rownames(392277): cg13869341 cg24669183 ... cg26251715 cg25640065
## rowData names(14): Forward_Sequence SourceSeq ...
## Regulatory_Feature_Group DHS
## colnames(20): x0017 x0043 ... x0077 x0079
## colData names(9): age sex ... Mono Neu
## Annotation
## array: IlluminaHumanMethylation450k
## annotation: ilmn12.hg19
## Preprocessing
## Method: NA
## minfi version: NA
## Manifest version: NAThe summary of the data contained by brge_methy:
| Data Type | Number of Samples | Number of Features | Technology | Object Name | Class |
|---|---|---|---|---|---|
| Methylome | 20 | 392277 | Illumina Human Methylation 450K | brge_methy |
GenomicRatioSet |
To load the protein data, stored in an ExpressionSet, run the follow commands:
data("brge_prot", package = "brgedata")
brge_prot## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 47 features, 90 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: x0001 x0002 ... x0090 (90 total)
## varLabels: age sex
## varMetadata: labelDescription
## featureData
## featureNames: Adiponectin_ok Alpha1AntitrypsinAAT_ok ...
## VitaminDBindingProte_ok (47 total)
## fvarLabels: chr start end
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:The summary of the data contained by brge_prot:
| Data Type | Number of Samples | Number of Features | Technology | Object Name | Class |
|---|---|---|---|---|---|
| Proteome | 90 | 47 | brge_prot |
ExpressionSet |
## 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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] splines_4.6.0 BiocIO_1.23.3
## [3] norm_1.0-11.1 bitops_1.0-9
## [5] tibble_3.3.1 preprocessCore_1.75.0
## [7] XML_3.99-0.23 rpart_4.1.27
## [9] lifecycle_1.0.5 Rdpack_2.6.6
## [11] lattice_0.22-9 MASS_7.3-65
## [13] base64_2.0.2 scrime_1.3.7
## [15] flashClust_1.1-4 backports_1.5.1
## [17] magrittr_2.0.5 minfi_1.59.0
## [19] limma_3.69.0 Hmisc_5.2-5
## [21] sass_0.4.10 rmarkdown_2.31
## [23] jquerylib_0.1.4 yaml_2.3.12
## [25] otel_0.2.0 doRNG_1.8.6.3
## [27] askpass_1.2.1 DBI_1.3.0
## [29] minqa_1.2.8 RColorBrewer_1.1-3
## [31] multcomp_1.4-30 abind_1.4-8
## [33] quadprog_1.5-8 GenomicRanges_1.65.0
## [35] purrr_1.2.2 RCurl_1.98-1.18
## [37] BiocGenerics_0.59.0 nnet_7.3-20
## [39] TH.data_1.1-5 sandwich_3.1-1
## [41] circlize_0.4.18 IRanges_2.47.0
## [43] S4Vectors_0.51.1 ggrepel_0.9.8
## [45] rentrez_1.2.4 genefilter_1.95.0
## [47] annotate_1.91.0 DelayedMatrixStats_1.35.0
## [49] codetools_0.2-20 DelayedArray_0.39.1
## [51] xml2_1.5.2 DT_0.34.0
## [53] tidyselect_1.2.1 gmm_1.9-1
## [55] shape_1.4.6.1 farver_2.1.2
## [57] lme4_2.0-1 beanplot_1.3.1
## [59] matrixStats_1.5.0 stats4_4.6.0
## [61] base64enc_0.1-6 illuminaio_0.55.0
## [63] Seqinfo_1.3.0 GenomicAlignments_1.49.0
## [65] jsonlite_2.0.0 multtest_2.69.0
## [67] Formula_1.2-5 survival_3.8-6
## [69] iterators_1.0.14 emmeans_2.0.3
## [71] foreach_1.5.2 tools_4.6.0
## [73] Rcpp_1.1.1-1.1 glue_1.8.1
## [75] BiocBaseUtils_1.15.1 gridExtra_2.3
## [77] SparseArray_1.13.2 xfun_0.57
## [79] MatrixGenerics_1.25.0 dplyr_1.2.1
## [81] HDF5Array_1.41.0 BiocManager_1.30.27
## [83] fastmap_1.2.0 boot_1.3-32
## [85] rhdf5filters_1.25.0 openssl_2.4.0
## [87] caTools_1.18.3 digest_0.6.39
## [89] R6_2.6.1 estimability_1.5.1
## [91] imputeLCMD_2.1 colorspace_2.1-2
## [93] gtools_3.9.5 dichromat_2.0-0.1
## [95] RSQLite_3.52.0 cigarillo_1.3.0
## [97] h5mread_1.5.0 tidyr_1.3.2
## [99] generics_0.1.4 data.table_1.18.4
## [101] rtracklayer_1.73.0 httr_1.4.8
## [103] htmlwidgets_1.6.4 S4Arrays_1.13.0
## [105] scatterplot3d_0.3-45 pkgconfig_2.0.3
## [107] gtable_0.3.6 blob_1.3.0
## [109] S7_0.2.2 siggenes_1.87.0
## [111] impute_1.87.0 XVector_0.53.0
## [113] htmltools_0.5.9 bookdown_0.46
## [115] multcompView_0.1-11 scales_1.4.0
## [117] Biobase_2.73.1 rexposome_1.35.0
## [119] png_0.1-9 tmvtnorm_1.7
## [121] leaps_3.2 reformulas_0.4.4
## [123] corrplot_0.95 knitr_1.51
## [125] rstudioapi_0.18.0 tzdb_0.5.0
## [127] rjson_0.2.23 reshape2_1.4.5
## [129] curl_7.1.0 coda_0.19-4.1
## [131] checkmate_2.3.4 nlme_3.1-169
## [133] nloptr_2.2.1 bumphunter_1.55.0
## [135] cachem_1.1.0 zoo_1.8-15
## [137] rhdf5_2.57.0 GlobalOptions_0.1.4
## [139] stringr_1.6.0 KernSmooth_2.23-26
## [141] parallel_4.6.0 foreign_0.8-91
## [143] AnnotationDbi_1.75.0 restfulr_0.0.16
## [145] GEOquery_2.81.0 pillar_1.11.1
## [147] grid_4.6.0 reshape_0.8.10
## [149] vctrs_0.7.3 gplots_3.3.0
## [151] pcaMethods_2.5.0 xtable_1.8-8
## [153] cluster_2.1.8.2 htmlTable_2.5.0
## [155] evaluate_1.0.5 readr_2.2.0
## [157] GenomicFeatures_1.65.0 Rsamtools_2.29.0
## [159] locfit_1.5-9.12 mvtnorm_1.3-7
## [161] cli_3.6.6 compiler_4.6.0
## [163] rngtools_1.5.2 rlang_1.2.0
## [165] crayon_1.5.3 nor1mix_1.3-3
## [167] mclust_6.1.2 plyr_1.8.9
## [169] stringi_1.8.7 lsr_0.5.2
## [171] BiocParallel_1.47.0 Biostrings_2.81.1
## [173] glmnet_5.0 Matrix_1.7-5
## [175] hms_1.1.4 sparseMatrixStats_1.25.0
## [177] bit64_4.8.0 ggplot2_4.0.3
## [179] Rhdf5lib_2.1.0 KEGGREST_1.53.0
## [181] statmod_1.5.1 FactoMineR_2.14
## [183] SummarizedExperiment_1.43.0 rbibutils_2.4.1
## [185] memoise_2.0.1 bslib_0.10.0
## [187] bit_4.6.0