Contents

1 Quick start

  1. Load piano and example data:
library(piano)
## Registered S3 method overwritten by 'ggplot2':
##   method        from
##   print.element sets
data("gsa_input")
  1. Take a look at the structure of the input data:
head(gsa_input$gsc,10)
##       g      s    
##  [1,] "g103" "s1" 
##  [2,] "g106" "s19"
##  [3,] "g118" "s16"
##  [4,] "g130" "s21"
##  [5,] "g130" "s6" 
##  [6,] "g131" "s46"
##  [7,] "g132" "s32"
##  [8,] "g132" "s3" 
##  [9,] "g139" "s1" 
## [10,] "g140" "s21"
head(gsa_input$pvals, 10)
##           g1           g2           g3           g4           g5 
## 2.351900e-05 2.838832e-05 2.885141e-05 6.566243e-05 7.107615e-05 
##           g6           g7           g8           g9          g10 
## 7.770070e-05 1.436830e-04 1.532264e-04 1.626607e-04 1.644806e-04
head(gsa_input$directions, 10)
##  g1  g2  g3  g4  g5  g6  g7  g8  g9 g10 
##  -1  -1   1  -1  -1   1  -1  -1  -1  -1
  1. Load gene-set collection and take a look at the resulting object:
geneSets <- loadGSC(gsa_input$gsc)
geneSets
## First 50 (out of 50) gene set names:
##  [1] "s1"  "s19" "s16" "s21" "s6"  "s46" "s32" "s3"  "s34" "s14" "s7"  "s13"
## [13] "s5"  "s42" "s2"  "s11" "s22" "s8"  "s15" "s10" "s33" "s37" "s35" "s43"
## [25] "s36" "s27" "s17" "s9"  "s23" "s30" "s18" "s25" "s41" "s24" "s20" "s39"
## [37] "s31" "s12" "s29" "s4"  "s26" "s44" "s28" "s47" "s38" "s49" "s50" "s40"
## [49] "s45" "s48"
## 
## First 50 (out of 1136) gene names:
##  [1] "g103" "g139" "g150" "g235" "g304" "g479" "g130" "g157" "g171" "g180"
## [11] "g218" "g243" "g251" "g302" "g319" "g32"  "g329" "g372" "g373" "g403"
## [21] "g41"  "g43"  "g456" "g476" "g48"  "g521" "g527" "g554" "g581" "g585"
## [31] "g591" "g62"  "g660" "g665" "g698" "g71"  "g711" "g723" "g726" "g75" 
## [41] "g758" "g77"  "g808" "g816" "g838" "g9"   "g907" "g924" "g931" "g935"
## 
## Gene set size summary:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2.00   20.50   39.00   39.50   53.75   95.00 
## 
## No additional info available.
  1. Run gene-set analysis:
gsares <- runGSA(gsa_input$pvals,
                 gsa_input$directions,
                 gsc = geneSets,
                 nPerm = 500) # set to 500 for fast run

Note: nPerm was set to 500 to get a short runtime for this vignette, in reality use a higher number, e.g. 10,000 (default).

  1. Explore the results in an interactive Shiny app:
exploreGSAres(gsares)

This opens a browser window with an interactive interface where the results can be explored in detail.

2 Session info

Here is the output of sessionInfo() on the system on which this document was compiled.

## R Under development (unstable) (2019-10-24 r77329)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-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] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] piano_2.3.0      BiocStyle_2.15.0
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.2           lattice_0.20-38      visNetwork_2.0.8    
##  [4] relations_0.6-9      gtools_3.8.1         assertthat_0.2.1    
##  [7] digest_0.6.22        mime_0.7             slam_0.1-46         
## [10] R6_2.4.0             evaluate_0.14        ggplot2_3.2.1       
## [13] pillar_1.4.2         gplots_3.0.1.1       rlang_0.4.1         
## [16] lazyeval_0.2.2       data.table_1.12.6    gdata_2.18.0        
## [19] Matrix_1.2-17        DT_0.9               rmarkdown_1.16      
## [22] shinyjs_1.0          sets_1.0-18          BiocParallel_1.21.0 
## [25] stringr_1.4.0        htmlwidgets_1.5.1    igraph_1.2.4.1      
## [28] munsell_0.5.0        shiny_1.4.0          fgsea_1.13.0        
## [31] compiler_4.0.0       httpuv_1.5.2         xfun_0.10           
## [34] pkgconfig_2.0.3      BiocGenerics_0.33.0  marray_1.65.0       
## [37] htmltools_0.4.0      tidyselect_0.2.5     tibble_2.1.3        
## [40] gridExtra_2.3        bookdown_0.14        crayon_1.3.4        
## [43] dplyr_0.8.3          later_1.0.0          bitops_1.0-6        
## [46] grid_4.0.0           jsonlite_1.6         xtable_1.8-4        
## [49] gtable_0.3.0         magrittr_1.5         scales_1.0.0        
## [52] KernSmooth_2.23-16   stringi_1.4.3        promises_1.1.0      
## [55] limma_3.43.0         fastmatch_1.1-0      tools_4.0.0         
## [58] Biobase_2.47.0       glue_1.3.1           purrr_0.3.3         
## [61] parallel_4.0.0       fastmap_1.0.1        yaml_2.2.0          
## [64] colorspace_1.4-1     cluster_2.1.0        BiocManager_1.30.9  
## [67] caTools_1.17.1.2     shinydashboard_0.7.1 knitr_1.25