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 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] minfi_1.30.0                bumphunter_1.26.0          
##  [3] locfit_1.5-9.1              iterators_1.0.10           
##  [5] foreach_1.4.4               Biostrings_2.52.0          
##  [7] XVector_0.24.0              SummarizedExperiment_1.14.0
##  [9] DelayedArray_0.10.0         BiocParallel_1.18.0        
## [11] matrixStats_0.54.0          GenomicRanges_1.36.0       
## [13] GenomeInfoDb_1.20.0         IRanges_2.18.0             
## [15] S4Vectors_0.22.0            rexposome_1.6.0            
## [17] Biobase_2.44.0              BiocGenerics_0.30.0        
## [19] BiocStyle_2.12.0           
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.1.4          circlize_0.4.6          
##   [3] Hmisc_4.2-0              corrplot_0.84           
##   [5] plyr_1.8.4               gmm_1.6-2               
##   [7] lazyeval_0.2.2           splines_3.6.0           
##   [9] ggplot2_3.1.1            pryr_0.1.4              
##  [11] digest_0.6.18            htmltools_0.3.6         
##  [13] gdata_2.18.0             magrittr_1.5            
##  [15] checkmate_1.9.3          memoise_1.1.0           
##  [17] cluster_2.0.9            limma_3.40.0            
##  [19] readr_1.3.1              annotate_1.62.0         
##  [21] imputeLCMD_2.0           sandwich_2.5-1          
##  [23] askpass_1.1              siggenes_1.58.0         
##  [25] prettyunits_1.0.2        colorspace_1.4-1        
##  [27] blob_1.1.1               ggrepel_0.8.0           
##  [29] xfun_0.6                 dplyr_0.8.0.1           
##  [31] crayon_1.3.4             RCurl_1.95-4.12         
##  [33] genefilter_1.66.0        lme4_1.1-21             
##  [35] GEOquery_2.52.0          impute_1.58.0           
##  [37] survival_2.44-1.1        zoo_1.8-5               
##  [39] glue_1.3.1               registry_0.5-1          
##  [41] gtable_0.3.0             zlibbioc_1.30.0         
##  [43] Rhdf5lib_1.6.0           shape_1.4.4             
##  [45] HDF5Array_1.12.0         scales_1.0.0            
##  [47] mvtnorm_1.0-10           DBI_1.0.0               
##  [49] rngtools_1.3.1.1         bibtex_0.4.2            
##  [51] Rcpp_1.0.1               xtable_1.8-4            
##  [53] progress_1.2.0           htmlTable_1.13.1        
##  [55] mclust_5.4.3             flashClust_1.01-2       
##  [57] foreign_0.8-71           bit_1.1-14              
##  [59] preprocessCore_1.46.0    Formula_1.2-3           
##  [61] glmnet_2.0-16            htmlwidgets_1.3         
##  [63] httr_1.4.0               gplots_3.0.1.1          
##  [65] RColorBrewer_1.1-2       acepack_1.4.1           
##  [67] pkgconfig_2.0.2          reshape_0.8.8           
##  [69] XML_3.98-1.19            nnet_7.3-12             
##  [71] tidyselect_0.2.5         rlang_0.3.4             
##  [73] reshape2_1.4.3           AnnotationDbi_1.46.0    
##  [75] munsell_0.5.0            tools_3.6.0             
##  [77] RSQLite_2.1.1            evaluate_0.13           
##  [79] stringr_1.4.0            yaml_2.2.0              
##  [81] knitr_1.22               bit64_0.9-7             
##  [83] beanplot_1.2             scrime_1.3.5            
##  [85] caTools_1.17.1.2         purrr_0.3.2             
##  [87] nlme_3.1-139             doRNG_1.7.1             
##  [89] nor1mix_1.2-3            xml2_1.2.0              
##  [91] leaps_3.0                biomaRt_2.40.0          
##  [93] compiler_3.6.0           rstudioapi_0.10         
##  [95] tibble_2.1.1             stringi_1.4.3           
##  [97] GenomicFeatures_1.36.0   lattice_0.20-38         
##  [99] Matrix_1.2-17            nloptr_1.2.1            
## [101] multtest_2.40.0          tmvtnorm_1.4-10         
## [103] pillar_1.3.1             norm_1.0-9.5            
## [105] BiocManager_1.30.4       GlobalOptions_0.1.0     
## [107] data.table_1.12.2        bitops_1.0-6            
## [109] rtracklayer_1.44.0       R6_2.4.0                
## [111] latticeExtra_0.6-28      pcaMethods_1.76.0       
## [113] bookdown_0.9             KernSmooth_2.23-15      
## [115] gridExtra_2.3            codetools_0.2-16        
## [117] boot_1.3-22              MASS_7.3-51.4           
## [119] gtools_3.8.1             assertthat_0.2.1        
## [121] rhdf5_2.28.0             openssl_1.3             
## [123] pkgmaker_0.27            withr_2.1.2             
## [125] GenomicAlignments_1.20.0 Rsamtools_2.0.0         
## [127] GenomeInfoDbData_1.2.1   hms_0.4.2               
## [129] quadprog_1.5-7           grid_3.6.0              
## [131] rpart_4.1-15             tidyr_0.8.3             
## [133] base64_2.0               minqa_1.2.4             
## [135] rmarkdown_1.12           DelayedMatrixStats_1.6.0
## [137] illuminaio_0.26.0        lsr_0.5                 
## [139] scatterplot3d_0.3-41     base64enc_0.1-3         
## [141] FactoMineR_1.41