Visualization & Quality Control Tools for Large-scale Multiplex Tissue Analysis in TissUUmaps 3

Abstract

Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in e.g. tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users' trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.

TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data

“…Classification techniques and/or parameter settings may result in varying classification results. We created the Classification Visualization and Quality Control, or ClassV&QC plugin to visualize and set side-by-side results of two techniques for classification, as described in more detail in [12]. Briefly, when comparing the output of two classification techniques, one output is displayed as large circles on the spatial viewport, while the output of the second technique is displayed as small stars on top of the circles.…”

“…This test assigns higher positive values to a pair of marker categories that are spatially closer to each other than they would be by random, values around zero to a pair of marker categories that are randomly distributed in relation to one another, and lower negative values to a pair of marker categories that are spatially further away from each other than they would be by random. The results of the neighborhood enrichment test can be exported as a matrix where columns and rows represent different marker categories, as described in more detail in [12]. We created an Interaction Visualization and Quality Control, or InteractionV&QC plugin, which can load a neighborhood enrichment matrix and make it interactive, so the user can click on the elements of the matrix and only those two corresponding marker categories are displayed on the Spatial viewport.…”