Getting started with shinyngs

What is shinyngs?

shinyngs turns the results of an RNA-seq (or similar matrix-based) analysis into an interactive Shiny application for exploration and mining: PCA, heatmaps, boxplots, clustering, differential views (volcano/MA), gene pages, gene-set enrichment and more. Its inputs are an extension of the Bioconductor SummarizedExperiment format called ExploratorySummarizedExperiment, and its individual plotting components can also be used on their own, outside any app.

Example: the gene page

Installation

# install.packages("devtools")
devtools::install_github("pinin4fjords/shinyngs")

shinyngs builds on SummarizedExperiment (Bioconductor) and needs a recent version of R.

A five-minute app

The quickest path is to wrap an existing SummarizedExperiment as an ExploratorySummarizedExperiment, collect it into an ExploratorySummarizedExperimentList (which represents a whole study), and hand it to prepare_app().

library(shinyngs)

data(airway, package = "airway")
ese <- as(airway, "ExploratorySummarizedExperiment")
eselist <- ExploratorySummarizedExperimentList(
  ese,
  title = "airway",
  group_vars = c("cell", "dex")
)

app <- prepare_app("rnaseq", eselist)
shiny::shinyApp(ui = app$ui, server = app$server)

prepare_app() produces the ui and server to pass to Shiny. Single-panel apps are built the same way, e.g. prepare_app("heatmap", eselist) or prepare_app("pca", eselist).

The zhangneurons dataset gives a fuller, pre-configured example that also carries differential results and gene sets:

devtools::install_github("pinin4fjords/zhangneurons")
data("zhangneurons")

app <- prepare_app("rnaseq", zhangneurons)
shiny::shinyApp(app$ui, app$server)

Where next

To…	See
Build the input objects from your own matrices, metadata, contrasts and gene sets	The data model
Drive an app from files or a YAML config	Building an app from files
Use the command-line scripts (`make_app_from_files.R`, …)	CLI reference
See every analysis panel and what it needs	Module and panel catalogue
Call the plotting functions from a report or your own app	Reusing components
Theme the app, change palettes, share views	Theming and shareable views
Extend the package with a new module	Developer guide

Technical information

?shinyngs

Session information

sessionInfo()
#> R version 4.4.0 (2024-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 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=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] BiocStyle_2.34.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.39       desc_1.4.3          R6_2.6.1           
#>  [4] bookdown_0.47       fastmap_1.2.0       xfun_0.60          
#>  [7] cachem_1.1.0        knitr_1.51          htmltools_0.5.9    
#> [10] rmarkdown_2.31      lifecycle_1.0.5     cli_3.6.6          
#> [13] sass_0.4.10         pkgdown_2.2.1       textshaping_1.0.5  
#> [16] jquerylib_0.1.4     systemfonts_1.3.2   compiler_4.4.0     
#> [19] tools_4.4.0         ragg_1.5.2          bslib_0.11.0       
#> [22] evaluate_1.0.5      yaml_2.3.12         BiocManager_1.30.27
#> [25] otel_0.2.0          jsonlite_2.0.0      rlang_1.3.0        
#> [28] fs_2.1.0            htmlwidgets_1.6.4