StaggR: an interactive R/Shiny application for planning and visualizing staggered experimental protocols

<ns3:p>Biological experiments often require a series of precisely timed operations, and small variations in treatment can result in inconsistent or biased results. To handle multiple samples in parallel with precise temporal resolution, experimentalists may stagger treatments by initiating the workflow of one sample during the wait or incubation time of another. However, as the number of samples processed in parallel and the number of operations increase, it becomes increasingly difficult to identify and execute valid treatment regimens that permit the handling of each sample. To address this, I developed StaggR, an interactive web application that calculates and visualizes compatible staggering intervals for complex experimental workflows. This tool provides a user-friendly interface for defining protocol operations, durations, and wait times. It can automatically calculate the shortest possible conflict-free interval for initiating sample treatments, or allow users to simulate specific intervals to explore potential treatment regimens or bottlenecks. Using StaggR, users of any experience level can rapidly generate complete, color-coded experimental workflows, visualize these workflows in an easy-to-read chart, and execute them using a built-in timer displaying a treatment schedule with live updates. The experimental designs can be saved, shared, and re-imported, ensuring full reproducibility and user control. The application of StaggR is expected to expedite the design and throughput of complex experimental workflows while maximizing reproducibility.</ns3:p>

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Publication Details

Journal
F1000Research
Published
2025-09-15
DOI
https://doi.org/10.12688/f1000research.168987.1
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
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article

StaggR: an interactive R/Shiny application for planning and visualizing staggered experimental protocols

Alex Michael Francette
F1000Research
Gene expression and cancer classification
article

StaggR: an interactive R/Shiny application for planning and visualizing staggered experimental protocols

Alex Michael Francette
article en

Abstract

<ns3:p>Biological experiments often require a series of precisely timed operations, and small variations in treatment can result in inconsistent or biased results. To handle multiple samples in parallel with precise temporal resolution, experimentalists may stagger treatments by initiating the workflow of one sample during the wait or incubation time of another. However, as the number of samples processed in parallel and the number of operations increase, it becomes increasingly difficult to identify and execute valid treatment regimens that permit the handling of each sample. To address this, I developed StaggR, an interactive web application that calculates and visualizes compatible staggering intervals for complex experimental workflows. This tool provides a user-friendly interface for defining protocol operations, durations, and wait times. It can automatically calculate the shortest possible conflict-free interval for initiating sample treatments, or allow users to simulate specific intervals to explore potential treatment regimens or bottlenecks. Using StaggR, users of any experience level can rapidly generate complete, color-coded experimental workflows, visualize these workflows in an easy-to-read chart, and execute them using a built-in timer displaying a treatment schedule with live updates. The experimental designs can be saved, shared, and re-imported, ensuring full reproducibility and user control. The application of StaggR is expected to expedite the design and throughput of complex experimental workflows while maximizing reproducibility.</ns3:p>

F1000ResearchVol. 14
Washington University in St. Louis (US)
Openalex Percentile: Top 78%
Gene expression and cancer classification
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StaggR: an interactive R/Shiny application for planning and visualizing staggered experimental protocols — Alex Michael Francette · F1000Research (2025) | TGRS Research Map | TGRS