Installing the Package

The MetaGxPancreas package is a compendium of Pancreatic Cancer datasets. The package is publicly available and can be installed from Bioconductor into R version 3.6.0 or higher. Currently, the phenoData for the datasets is overall survival status and overall survival time. This survival information is available for 11 of the 15 datasets.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("MetaGxPancreas")

Loading Datasets

First we load the MetaGxPancreas package into the workspace.

library(MetaGxPancreas)
## Loading required package: SummarizedExperiment
## Loading required package: MatrixGenerics
## Loading required package: matrixStats
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## Attaching package: 'MatrixGenerics'
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##     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
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##     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
##     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
##     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
##     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
##     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
##     rowWeightedSds, rowWeightedVars
## Loading required package: GenomicRanges
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## Attaching package: 'S4Vectors'
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## Welcome to Bioconductor
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## Loading required package: ExperimentHub
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## Attaching package: 'AnnotationHub'
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pancreasData <- loadPancreasDatasets()
## snapshotDate(): 2021-05-05
## Filtered out duplicated samples: ICGC_0400, ICGC_0402, GSM388116, GSM388118, GSM388120, GSM388145, GSM299238, GSM299239, GSM299240
duplicates <- pancreasData$duplicates
SEs <- pancreasData$SEs

This will load 15 expression datasets. Users can modify the parameters of the function to restrict datasets that do not meet certain criteria for loading. Some example parameters are shown below:

Obtaining Sample Counts in Datasets

To obtain the number of samples per dataset, run the following:

numSamples <- vapply(SEs, function(SE) length(colnames(SE)), FUN.VALUE=numeric(1))

sampleNumberByDataset <- data.frame(numSamples=numSamples,
                                    row.names=names(SEs))

totalNumSamples <- sum(sampleNumberByDataset$numSamples)
sampleNumberByDataset <- rbind(sampleNumberByDataset, totalNumSamples)
rownames(sampleNumberByDataset)[nrow(sampleNumberByDataset)] <- 'Total'

knitr::kable(sampleNumberByDataset)
X0
Total 0

SessionInfo

sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-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] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] MetaGxPancreas_1.12.0       ExperimentHub_2.0.0        
##  [3] AnnotationHub_3.0.0         BiocFileCache_2.0.0        
##  [5] dbplyr_2.1.1                SummarizedExperiment_1.22.0
##  [7] Biobase_2.52.0              GenomicRanges_1.44.0       
##  [9] GenomeInfoDb_1.28.0         IRanges_2.26.0             
## [11] S4Vectors_0.30.0            BiocGenerics_0.38.0        
## [13] MatrixGenerics_1.4.0        matrixStats_0.58.0         
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##  [1] Rcpp_1.0.6                    lattice_0.20-44              
##  [3] Biostrings_2.60.0             png_0.1-7                    
##  [5] assertthat_0.2.1              digest_0.6.27                
##  [7] utf8_1.2.1                    mime_0.10                    
##  [9] R6_2.5.0                      RSQLite_2.2.7                
## [11] evaluate_0.14                 highr_0.9                    
## [13] httr_1.4.2                    pillar_1.6.1                 
## [15] zlibbioc_1.38.0               rlang_0.4.11                 
## [17] curl_4.3.1                    blob_1.2.1                   
## [19] Matrix_1.3-3                  stringr_1.4.0                
## [21] RCurl_1.98-1.3                bit_4.0.4                    
## [23] shiny_1.6.0                   DelayedArray_0.18.0          
## [25] compiler_4.1.0                httpuv_1.6.1                 
## [27] xfun_0.23                     pkgconfig_2.0.3              
## [29] htmltools_0.5.1.1             KEGGREST_1.32.0              
## [31] tidyselect_1.1.1              tibble_3.1.2                 
## [33] GenomeInfoDbData_1.2.6        interactiveDisplayBase_1.30.0
## [35] fansi_0.4.2                   withr_2.4.2                  
## [37] crayon_1.4.1                  dplyr_1.0.6                  
## [39] later_1.2.0                   bitops_1.0-7                 
## [41] rappdirs_0.3.3                grid_4.1.0                   
## [43] xtable_1.8-4                  lifecycle_1.0.0              
## [45] DBI_1.1.1                     magrittr_2.0.1               
## [47] impute_1.66.0                 stringi_1.6.2                
## [49] cachem_1.0.5                  XVector_0.32.0               
## [51] promises_1.2.0.1              ellipsis_0.3.2               
## [53] filelock_1.0.2                generics_0.1.0               
## [55] vctrs_0.3.8                   tools_4.1.0                  
## [57] bit64_4.0.5                   glue_1.4.2                   
## [59] purrr_0.3.4                   BiocVersion_3.13.1           
## [61] fastmap_1.1.0                 yaml_2.2.1                   
## [63] AnnotationDbi_1.54.0          BiocManager_1.30.15          
## [65] memoise_2.0.0                 knitr_1.33