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 3.5.0 (2018-04-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.7-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_1.26.0                bumphunter_1.22.0          
##  [3] locfit_1.5-9.1              iterators_1.0.9            
##  [5] foreach_1.4.4               Biostrings_2.48.0          
##  [7] XVector_0.20.0              SummarizedExperiment_1.10.0
##  [9] DelayedArray_0.6.0          BiocParallel_1.14.0        
## [11] matrixStats_0.53.1          GenomicRanges_1.32.0       
## [13] GenomeInfoDb_1.16.0         IRanges_2.14.0             
## [15] S4Vectors_0.18.0            rexposome_1.2.0            
## [17] Biobase_2.40.0              BiocGenerics_0.26.0        
## [19] BiocStyle_2.8.0            
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.1.2          circlize_0.4.3          
##   [3] Hmisc_4.1-1              corrplot_0.84           
##   [5] plyr_1.8.4               igraph_1.2.1            
##   [7] gmm_1.6-2                lazyeval_0.2.1          
##   [9] splines_3.5.0            ggplot2_2.2.1           
##  [11] pryr_0.1.4               digest_0.6.15           
##  [13] BiocInstaller_1.30.0     htmltools_0.3.6         
##  [15] gdata_2.18.0             magrittr_1.5            
##  [17] checkmate_1.8.5          memoise_1.1.0           
##  [19] cluster_2.0.7-1          limma_3.36.0            
##  [21] readr_1.1.1              annotate_1.58.0         
##  [23] imputeLCMD_2.0           sandwich_2.4-0          
##  [25] siggenes_1.54.0          prettyunits_1.0.2       
##  [27] colorspace_1.3-2         blob_1.1.1              
##  [29] ggrepel_0.7.0            dplyr_0.7.4             
##  [31] xfun_0.1                 RCurl_1.95-4.10         
##  [33] genefilter_1.62.0        lme4_1.1-17             
##  [35] bindr_0.1.1              GEOquery_2.48.0         
##  [37] impute_1.54.0            glue_1.2.0              
##  [39] survival_2.42-3          zoo_1.8-1               
##  [41] registry_0.5             gtable_0.2.0            
##  [43] zlibbioc_1.26.0          Rhdf5lib_1.2.0          
##  [45] shape_1.4.4              HDF5Array_1.8.0         
##  [47] scales_0.5.0             mvtnorm_1.0-7           
##  [49] DBI_0.8                  rngtools_1.2.4          
##  [51] Rcpp_0.12.16             xtable_1.8-2            
##  [53] progress_1.1.2           htmlTable_1.11.2        
##  [55] flashClust_1.01-2        foreign_0.8-70          
##  [57] bit_1.1-12               mclust_5.4              
##  [59] preprocessCore_1.42.0    Formula_1.2-2           
##  [61] glmnet_2.0-16            htmlwidgets_1.2         
##  [63] httr_1.3.1               gplots_3.0.1            
##  [65] RColorBrewer_1.1-2       acepack_1.4.1           
##  [67] pkgconfig_2.0.1          reshape_0.8.7           
##  [69] XML_3.98-1.11            nnet_7.3-12             
##  [71] labeling_0.3             rlang_0.2.0             
##  [73] reshape2_1.4.3           AnnotationDbi_1.42.0    
##  [75] munsell_0.4.3            tools_3.5.0             
##  [77] RSQLite_2.1.0            evaluate_0.10.1         
##  [79] stringr_1.3.0            yaml_2.1.18             
##  [81] knitr_1.20               bit64_0.9-7             
##  [83] beanplot_1.2             caTools_1.17.1          
##  [85] purrr_0.2.4              bindrcpp_0.2.2          
##  [87] nlme_3.1-137             doRNG_1.6.6             
##  [89] nor1mix_1.2-3            xml2_1.2.0              
##  [91] leaps_3.0                biomaRt_2.36.0          
##  [93] compiler_3.5.0           rstudioapi_0.7          
##  [95] tibble_1.4.2             stringi_1.1.7           
##  [97] GenomicFeatures_1.32.0   lattice_0.20-35         
##  [99] Matrix_1.2-14            nloptr_1.0.4            
## [101] multtest_2.36.0          tmvtnorm_1.4-10         
## [103] pillar_1.2.2             norm_1.0-9.5            
## [105] GlobalOptions_0.0.13     data.table_1.10.4-3     
## [107] bitops_1.0-6             rtracklayer_1.40.0      
## [109] R6_2.2.2                 latticeExtra_0.6-28     
## [111] pcaMethods_1.72.0        bookdown_0.7            
## [113] KernSmooth_2.23-15       gridExtra_2.3           
## [115] codetools_0.2-15         MASS_7.3-50             
## [117] gtools_3.5.0             assertthat_0.2.0        
## [119] rhdf5_2.24.0             openssl_1.0.1           
## [121] pkgmaker_0.22            rprojroot_1.3-2         
## [123] GenomicAlignments_1.16.0 Rsamtools_1.32.0        
## [125] GenomeInfoDbData_1.1.0   hms_0.4.2               
## [127] quadprog_1.5-5           grid_3.5.0              
## [129] rpart_4.1-13             psygenet2r_1.12.0       
## [131] tidyr_0.8.0              base64_2.0              
## [133] minqa_1.2.4              rmarkdown_1.9           
## [135] DelayedMatrixStats_1.2.0 illuminaio_0.22.0       
## [137] lsr_0.5                  scatterplot3d_0.3-41    
## [139] base64enc_0.1-3          FactoMineR_1.40