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

TL;DR

StaggR is a web tool that helps scientists plan and visualize complex staggered experiments with multiple samples and precise timing.

Contribution

StaggR introduces an interactive R/Shiny application for designing and visualizing conflict-free staggered experimental protocols.

Findings

StaggR automates the calculation of valid treatment intervals for complex workflows.

The tool provides color-coded visualizations and a built-in timer for executing staggered protocols.

StaggR supports reproducibility by allowing users to save, share, and re-import experimental designs.

Abstract

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.

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