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 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.6-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_1.24.0               bumphunter_1.20.0         
##  [3] locfit_1.5-9.1             iterators_1.0.9           
##  [5] foreach_1.4.4              Biostrings_2.46.0         
##  [7] XVector_0.18.0             SummarizedExperiment_1.8.1
##  [9] DelayedArray_0.4.1         matrixStats_0.53.0        
## [11] GenomicRanges_1.30.1       GenomeInfoDb_1.14.0       
## [13] IRanges_2.12.0             S4Vectors_0.16.0          
## [15] rexposome_1.0.0            Biobase_2.38.0            
## [17] BiocGenerics_0.24.0        BiocStyle_2.6.1           
## 
## 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_1.1.2            
##   [7] gmm_1.6-1                lazyeval_0.2.1          
##   [9] splines_3.4.3            BiocParallel_1.12.0     
##  [11] ggplot2_2.2.1            pryr_0.1.3              
##  [13] digest_0.6.15            BiocInstaller_1.28.0    
##  [15] htmltools_0.3.6          gdata_2.18.0            
##  [17] magrittr_1.5             checkmate_1.8.5         
##  [19] memoise_1.1.0            cluster_2.0.6           
##  [21] limma_3.34.8             readr_1.1.1             
##  [23] annotate_1.56.1          imputeLCMD_2.0          
##  [25] sandwich_2.4-0           siggenes_1.52.0         
##  [27] prettyunits_1.0.2        colorspace_1.3-2        
##  [29] blob_1.1.0               ggrepel_0.7.0           
##  [31] dplyr_0.7.4              xfun_0.1                
##  [33] RCurl_1.95-4.10          genefilter_1.60.0       
##  [35] lme4_1.1-15              bindr_0.1               
##  [37] GEOquery_2.46.14         impute_1.52.0           
##  [39] glue_1.2.0               survival_2.41-3         
##  [41] zoo_1.8-1                registry_0.5            
##  [43] gtable_0.2.0             zlibbioc_1.24.0         
##  [45] shape_1.4.3              scales_0.5.0            
##  [47] mvtnorm_1.0-7            DBI_0.7                 
##  [49] rngtools_1.2.4           Rcpp_0.12.15            
##  [51] xtable_1.8-2             progress_1.1.2          
##  [53] htmlTable_1.11.2         flashClust_1.01-2       
##  [55] foreign_0.8-69           bit_1.1-12              
##  [57] mclust_5.4               preprocessCore_1.40.0   
##  [59] Formula_1.2-2            glmnet_2.0-13           
##  [61] htmlwidgets_1.0          httr_1.3.1              
##  [63] gplots_3.0.1             RColorBrewer_1.1-2      
##  [65] acepack_1.4.1            pkgconfig_2.0.1         
##  [67] reshape_0.8.7            XML_3.98-1.9            
##  [69] nnet_7.3-12              labeling_0.3            
##  [71] rlang_0.1.6              reshape2_1.4.3          
##  [73] AnnotationDbi_1.40.0     munsell_0.4.3           
##  [75] tools_3.4.3              RSQLite_2.0             
##  [77] evaluate_0.10.1          stringr_1.2.0           
##  [79] yaml_2.1.16              knitr_1.19              
##  [81] bit64_0.9-7              beanplot_1.2            
##  [83] caTools_1.17.1           purrr_0.2.4             
##  [85] bindrcpp_0.2             nlme_3.1-131            
##  [87] doRNG_1.6.6              nor1mix_1.2-3           
##  [89] xml2_1.2.0               leaps_3.0               
##  [91] biomaRt_2.34.2           compiler_3.4.3          
##  [93] rstudioapi_0.7           tibble_1.4.2            
##  [95] stringi_1.1.6            GenomicFeatures_1.30.3  
##  [97] lattice_0.20-35          Matrix_1.2-12           
##  [99] nloptr_1.0.4             tmvtnorm_1.4-10         
## [101] multtest_2.34.0          pillar_1.1.0            
## [103] norm_1.0-9.5             GlobalOptions_0.0.12    
## [105] data.table_1.10.4-3      bitops_1.0-6            
## [107] rtracklayer_1.38.3       R6_2.2.2                
## [109] latticeExtra_0.6-28      pcaMethods_1.70.0       
## [111] bookdown_0.6             RMySQL_0.10.13          
## [113] KernSmooth_2.23-15       gridExtra_2.3           
## [115] codetools_0.2-15         MASS_7.3-48             
## [117] gtools_3.5.0             assertthat_0.2.0        
## [119] openssl_1.0              pkgmaker_0.22           
## [121] rprojroot_1.3-2          GenomicAlignments_1.14.1
## [123] Rsamtools_1.30.0         GenomeInfoDbData_1.0.0  
## [125] hms_0.4.1                quadprog_1.5-5          
## [127] grid_3.4.3               rpart_4.1-12            
## [129] psygenet2r_1.10.0        tidyr_0.8.0             
## [131] base64_2.0               minqa_1.2.4             
## [133] rmarkdown_1.8            illuminaio_0.20.0       
## [135] lsr_0.5                  scatterplot3d_0.3-40    
## [137] base64enc_0.1-3          FactoMineR_1.39