Abstract

QuPath Spatial Analysis and Visualisation tool

Multiplex immunofluorescence (mIF) is a useful technique that allows the quantification and spatial analysis of cellular characteristics within 2D tissue sections. While many spatial analysis pipelines are technically challenging or require proprietary software/hardware, the free and open-source software QuPath is a novel resource for quantifying and spatially profiling multiplex IF.

A major issue with QuPath single-cell data frame outputs is the extremely large data frame outputs that QuPath scripts deliver. This creates issues when trying to observe and analyze outputs, as applications normally used for these data frames, such as Microsoft Excel, struggle to run efficiently when handling such large data. One option to improve this issue would be to filter data within QuPath, but data is then being removed that may be useful for later analysis.

To combat this issue in a way that preserves the raw data generated by QuPath, I have created a light weight, browser-based tool named QuPath Spatial Analysis and Visualization as an extended protocol using Python. This tool aims to not only process large data frames extremely fast, but also break down the data into a small, digestible data frame of important metrics. Additionally, the tool recreates images as scatter plots using spatial coordinates collected through the QuPath pipeline and creates heatmaps showcasing the processed data automatically. These images allow the user to flexibly explore their uploaded and processed data rapidly, while also allowing for image export in several file types.