Contents

1 Quick start

  1. Load piano and example data:
library(piano)
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           g6 
## 2.351900e-05 2.838832e-05 2.885141e-05 6.566243e-05 7.107615e-05 7.770070e-05 
##           g7           g8           g9          g10 
## 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) (2022-10-25 r83175)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              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_2.15.0     BiocStyle_2.27.0
## 
## loaded via a namespace (and not attached):
##  [1] fastmatch_1.1-3      gtable_0.3.1         xfun_0.34           
##  [4] bslib_0.4.0          ggplot2_3.3.6        visNetwork_2.1.2    
##  [7] shinyjs_2.1.0        htmlwidgets_1.5.4    sets_1.0-21         
## [10] caTools_1.18.2       lattice_0.20-45      Biobase_2.59.0      
## [13] vctrs_0.5.0          tools_4.3.0          bitops_1.0-7        
## [16] generics_0.1.3       parallel_4.3.0       tibble_3.1.8        
## [19] fansi_1.0.3          cluster_2.1.4        pkgconfig_2.0.3     
## [22] Matrix_1.5-1         KernSmooth_2.23-20   data.table_1.14.4   
## [25] assertthat_0.2.1     lifecycle_1.0.3      compiler_4.3.0      
## [28] stringr_1.4.1        gplots_3.1.3         munsell_0.5.0       
## [31] fgsea_1.25.0         codetools_0.2-18     httpuv_1.6.6        
## [34] htmltools_0.5.3      sass_0.4.2           yaml_2.3.6          
## [37] marray_1.77.0        later_1.3.0          pillar_1.8.1        
## [40] jquerylib_0.1.4      ellipsis_0.3.2       relations_0.6-12    
## [43] DT_0.26              BiocParallel_1.33.0  cachem_1.0.6        
## [46] limma_3.55.0         mime_0.12            gtools_3.9.3        
## [49] tidyselect_1.2.0     digest_0.6.30        stringi_1.7.8       
## [52] slam_0.1-50          dplyr_1.0.10         bookdown_0.29       
## [55] cowplot_1.1.1        fastmap_1.1.0        grid_4.3.0          
## [58] colorspace_2.0-3     cli_3.4.1            magrittr_2.0.3      
## [61] utf8_1.2.2           scales_1.2.1         promises_1.2.0.1    
## [64] rmarkdown_2.17       igraph_1.3.5         shiny_1.7.3         
## [67] evaluate_0.17        knitr_1.40           rlang_1.0.6         
## [70] Rcpp_1.0.9           xtable_1.8-4         glue_1.6.2          
## [73] DBI_1.1.3            BiocManager_1.30.19  BiocGenerics_0.45.0 
## [76] shinydashboard_0.7.2 jsonlite_1.8.3       R6_2.5.1