# Running gene-set anaysis with `piano`

# Quick start

1. Load piano and example data:

    library(piano)
    data("gsa_input")

2. 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

3. 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.

4. 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).

5. 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.

# Session info

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

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## 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
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] piano_2.27.0     BiocStyle_2.39.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] fastmatch_1.1-8      gtable_0.3.6         xfun_0.56
    ##  [4] bslib_0.10.0         ggplot2_4.0.1        visNetwork_2.1.4
    ##  [7] shinyjs_2.1.1        htmlwidgets_1.6.4    sets_1.0-25
    ## [10] caTools_1.18.3       lattice_0.22-7       Biobase_2.71.0
    ## [13] vctrs_0.7.1          tools_4.5.2          bitops_1.0-9
    ## [16] generics_0.1.4       parallel_4.5.2       tibble_3.3.1
    ## [19] cluster_2.1.8.1      pkgconfig_2.0.3      Matrix_1.7-4
    ## [22] KernSmooth_2.23-26   data.table_1.18.0    RColorBrewer_1.1-3
    ## [25] S7_0.2.1             lifecycle_1.0.5      compiler_4.5.2
    ## [28] farver_2.1.2         gplots_3.3.0         statmod_1.5.1
    ## [31] fgsea_1.37.4         codetools_0.2-20     httpuv_1.6.16
    ## [34] htmltools_0.5.9      sys_3.4.3            buildtools_1.0.0
    ## [37] sass_0.4.10          yaml_2.3.12          marray_1.89.0
    ## [40] later_1.4.5          pillar_1.11.1        jquerylib_0.1.4
    ## [43] relations_0.6-15     DT_0.34.0            BiocParallel_1.45.0
    ## [46] cachem_1.1.0         limma_3.67.0         mime_0.13
    ## [49] gtools_3.9.5         tidyselect_1.2.1     digest_0.6.39
    ## [52] slam_0.1-55          dplyr_1.1.4          maketools_1.3.2
    ## [55] cowplot_1.2.0        fastmap_1.2.0        grid_4.5.2
    ## [58] cli_3.6.5            magrittr_2.0.4       scales_1.4.0
    ## [61] promises_1.5.0       rmarkdown_2.30       igraph_2.2.1
    ## [64] otel_0.2.0           shiny_1.12.1         evaluate_1.0.5
    ## [67] knitr_1.51           rlang_1.1.7          Rcpp_1.1.1
    ## [70] xtable_1.8-4         glue_1.8.0           BiocManager_1.30.27
    ## [73] BiocGenerics_0.57.0  shinydashboard_0.7.3 jsonlite_2.0.0
    ## [76] R6_2.6.1