Scientific Accountability

Generating reproducible reports with Shiny apps

January Weiner

Core Unit for Bioinformatics, BIH@Charité

The CUBI environment

Clinical and translational research
Large university hospital and related institutions (BIH, MDC, Charité)

The CUBI environment

Clinical and translational research
Large university hospital and related institutions (BIH, MDC, Charité)

The CUBI environment

Clinical and translational research
Large university hospital and related institutions (BIH, MDC, Charité)

Reproducibility vs Accountability

In a high-throughput bioinformatic project, a lot of analyses, models, figures, tables are generated.

Reproducibility vs Accountability

Only some of them make it to the final paper.

Reproducibility vs Accountability

When you read a paper, how do you link the figures back to the data?

Reproducibility vs Accountability

We know how to do it: use R! (and Rmarkdown, Quarto, etc.) In practice, what is reproducible, is often not accountable.

How to turn interactive work into reproducible reports?

The Bioshmods project

library(bioshmods)
data(C19)

ui <- fluidPage(
       fluidRow(selectizeInput("id", label="Search for a gene",
         choices=NULL),
       fluidRow(geneBrowserPlotUI("gplot", TRUE))
       ))

server <- function(input, output, session) {
  gene_id <- reactiveValues()
  updateSelectizeInput(session, "id", choices=C19$annotation$SYMBOL)

  # translate symbol to primary ID
  observeEvent(input$id, {
    nn <- match(input$id, C19$annotation$SYMBOL)
    gene_id$id <- C19$annotation$PrimaryID[ nn ]
  })

  geneBrowserPlotServer("gplot", gene_id=gene_id, 
                        covar=C19$covariates, 
                        exprs=C19$expression,
                        annot=C19$annotation, 
                        cntr=C19$contrasts
   )
}
shinyApp(ui, server)

The Bioshmods project

Thank you

Links

github.com/january3/useR2026
github.com/bihealth/bioshmods

I’d love to hear your feedback!