ShapoGraphy: a glyph-oriented visualisation approach for creating pictorial representations of bioimaging data

Motivation: High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating morphological measurements of individual cells into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, there is currently no standard approach to morphological profiling and no solution is available for the high-performance Julia programming language. Results: Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets. Availability: The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks Contact: joerg.menche@univie.ac.at

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PeaGlyph: Glyph design for investigation of balanced data structures

Koc

McGough

Fernstad

2021

Information Visualization

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For many data analysis tasks, such as the formation of well-balanced groups for a fair race or collaboration in learning settings, the balancing between data attributes is at least as important as the actual values of items. At the same time, comparison of values is implicitly desired for these tasks. Even with statistical methods available to measure the level of balance, human judgment, and domain expertise plays an important role in judging the level of balance, and whether the level of unbalance is acceptable or not. Accordingly, there is a need for techniques that improve decision-making in the context of group formation that can be used as a visual complement to statistical analysis. This paper introduces a novel glyph-based visualization, PeaGlyph, which aims to support the understanding of balanced and unbalanced data structures, for instance by using a frequency format through countable marks and salient shape characteristics. The glyph was designed particularly for tasks of relevance for investigation of properties of balanced and unbalanced groups, such as looking-up and comparing values. Glyph-based visualization methods provide flexible and useful abstractions for exploring and analyzing multivariate data sets. The PeaGlyph design was based on an initial study that compared four glyph visualization methods in a joint study, including two base glyphs and their variations. The performance of the novel PeaGlyph was then compared to the best “performers” of the first study through evaluation. The initial results from the study are encouraging, and the proposed design may be a good alternative to the traditional glyphs for depicting multivariate data and allowing viewers to form an intuitive impression as to how balanced or unbalanced a set of objects are. Furthermore, a set of design considerations is discussed in context of the design of the glyphs.

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ShapoGraphy: a glyph-oriented visualisation approach for creating pictorial representations of bioimaging data

Cited by 3 publications

References 20 publications

Enjoying the small things

Enjoying the small things

BioProfiling.jl: Profiling biological perturbations with high-content imaging in single cells and heterogeneous populations

PeaGlyph: Glyph design for investigation of balanced data structures

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