Contents

1 Getting started

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.

2 Data Resources

2.1 Exposome Data

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

2.2 Transcriptome Data

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

2.3 Methylome Data

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: NA

The 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

2.4 Proteome Data

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

Session info

## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-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] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] minfi_1.48.0                bumphunter_1.44.0          
##  [3] locfit_1.5-9.8              iterators_1.0.14           
##  [5] foreach_1.5.2               Biostrings_2.70.1          
##  [7] XVector_0.42.0              SummarizedExperiment_1.32.0
##  [9] MatrixGenerics_1.14.0       matrixStats_1.0.0          
## [11] GenomicRanges_1.54.0        GenomeInfoDb_1.38.0        
## [13] IRanges_2.36.0              S4Vectors_0.40.0           
## [15] rexposome_1.24.0            Biobase_2.62.0             
## [17] BiocGenerics_0.48.0         BiocStyle_2.30.0           
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.3.1             norm_1.0-11.1            
##   [3] BiocIO_1.12.0             bitops_1.0-7             
##   [5] filelock_1.0.2            tibble_3.2.1             
##   [7] preprocessCore_1.64.0     XML_3.99-0.14            
##   [9] rpart_4.1.21              lifecycle_1.0.3          
##  [11] base64_2.0.1              lattice_0.22-5           
##  [13] MASS_7.3-60               scrime_1.3.5             
##  [15] flashClust_1.01-2         backports_1.4.1          
##  [17] magrittr_2.0.3            limma_3.58.0             
##  [19] Hmisc_5.1-1               sass_0.4.7               
##  [21] rmarkdown_2.25            jquerylib_0.1.4          
##  [23] yaml_2.3.7                askpass_1.2.0            
##  [25] doRNG_1.8.6               RColorBrewer_1.1-3       
##  [27] DBI_1.1.3                 minqa_1.2.6              
##  [29] multcomp_1.4-25           abind_1.4-5              
##  [31] zlibbioc_1.48.0           quadprog_1.5-8           
##  [33] purrr_1.0.2               RCurl_1.98-1.12          
##  [35] nnet_7.3-19               TH.data_1.1-2            
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##  [39] circlize_0.4.15           GenomeInfoDbData_1.2.11  
##  [41] ggrepel_0.9.4             genefilter_1.84.0        
##  [43] annotate_1.80.0           DelayedMatrixStats_1.24.0
##  [45] codetools_0.2-19          DelayedArray_0.28.0      
##  [47] DT_0.30                   xml2_1.3.5               
##  [49] tidyselect_1.2.0          gmm_1.8                  
##  [51] shape_1.4.6               beanplot_1.3.1           
##  [53] lme4_1.1-34               BiocFileCache_2.10.0     
##  [55] base64enc_0.1-3           illuminaio_0.44.0        
##  [57] GenomicAlignments_1.38.0  jsonlite_1.8.7           
##  [59] multtest_2.58.0           Formula_1.2-5            
##  [61] survival_3.5-7            emmeans_1.8.9            
##  [63] tools_4.3.1               progress_1.2.2           
##  [65] pryr_0.1.6                Rcpp_1.0.11              
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## [103] htmltools_0.5.6.1         bookdown_0.36            
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## [117] checkmate_2.3.0           nlme_3.1-163             
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## [125] stringr_1.5.0             KernSmooth_2.23-22       
## [127] foreign_0.8-85            AnnotationDbi_1.64.0     
## [129] restfulr_0.0.15           GEOquery_2.70.0          
## [131] reshape_0.8.9             pillar_1.9.0             
## [133] grid_4.3.1                vctrs_0.6.4              
## [135] gplots_3.1.3              pcaMethods_1.94.0        
## [137] dbplyr_2.3.4              xtable_1.8-4             
## [139] cluster_2.1.4             htmlTable_2.4.1          
## [141] evaluate_0.22             readr_2.1.4              
## [143] GenomicFeatures_1.54.0    Rsamtools_2.18.0         
## [145] mvtnorm_1.2-3             cli_3.6.1                
## [147] compiler_4.3.1            rlang_1.1.1              
## [149] crayon_1.5.2              rngtools_1.5.2           
## [151] nor1mix_1.3-0             mclust_6.0.0             
## [153] plyr_1.8.9                stringi_1.7.12           
## [155] BiocParallel_1.36.0       lsr_0.5.2                
## [157] munsell_0.5.0             glmnet_4.1-8             
## [159] Matrix_1.6-1.1            hms_1.1.3                
## [161] sparseMatrixStats_1.14.0  bit64_4.0.5              
## [163] Rhdf5lib_1.24.0           ggplot2_3.4.4            
## [165] statmod_1.5.0             KEGGREST_1.42.0          
## [167] FactoMineR_2.9            memoise_2.0.1            
## [169] bslib_0.5.1               bit_4.0.5