A survey of visualisation for live cell imaging

Article English OPEN
Pretorius, AJ ; Khan, IA ; Errington, RJ (2017)
  • Publisher: Wiley

Live cell imaging is an important biomedical research paradigm for studying dynamic cellular behaviour. Although phenotypic data derived from images are difficult to explore and analyse, some researchers have successfully addressed this with visualisation. Nonetheless, visualisation methods for live cell imaging data have been reported in an ad hoc and fragmented fashion. This leads to a knowledge gap where it is difficult for biologists and visualisation developers to evaluate the advantages and disadvantages of different visualisation methods, and for visualisation researchers to gain an overview of existing work to identify research priorities. To address this gap, we survey existing visualisation methods for live cell imaging from a visualisation research perspective for the first time. Based on recent visualisation theory, we perform a structured qualitative analysis of visualisation methods that includes characterising the domain and data, abstracting tasks, and describing visual encoding and interaction design. Based on our survey, we identify and discuss research gaps that future work should address: the broad analytical context of live cell imaging; the importance of behavioural comparisons; links with dynamic data visualisation; the consequences of different data modalities; shortcomings in interactive support; and, in addition to analysis, the value of the presentation of phenotypic data and insights to other stakeholders.
  • References (92)
    92 references, page 1 of 10

    [AA13] ANDRIENKO N., ANDRIENKO G.: Visual analytics of movement: an overview of methods, tools and procedures. Information Visualization 12, 1 (2013), 3-24. 15

    [AES05] AMAR R., EAGAN J., STASKO J.: Low-level components of analytic activity in information visualization. In Proceedings of the IEEE Symposium on Information Visualization (2005), pp. 111-117. 3

    [AMM∗07] AIGNER W., MIKSCH S., MÜLLER W., SCHUMANN H., TOMINSKI C.: Visualizing time-oriented data-a systematic view. Computers & Graphics 31, 3 (2007), 401-409. 15

    [AMST11] AIGNER W., MIKSCH S., SCHUMANN S., TOMINSKI C.: Visualization of Time-Oriented Data. Springer Verlag, 2011. 15

    [ARG∗06] AL-KOFAHI O., RADKE R. J., GODERIE S. K., SHEN Q., TEMPLE S., ROYSAM B.: Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells. Cell Cycle 5, 3 (2006), 327-335. 2, 4, 6, 7, 8, 11, 14, 16

    [AS05] AMAR R. A., STASKO J. T.: Knowledge precepts for design and evaluation of information visualizations. IEEE Transactions on Visualization and Computer Graphics 11, 4 (2005), 432-442. 15

    [Bar03] BARD J.: Ontologies: formalising biological knowledge for bioinformatics. BioEssays 25, 5 (2003), 501-506. 14

    [BDA∗14] BACH B., DRAGICEVIC P., ARCHAMBAULT D., HURTER C., CARPENDALE S.: A review of temporal data visualizations based on space-time cube operations. In Proceedigns of the EG/VGTC Conference on Visualisation (2014), pp. 23-41. 15

    [Bec] BECTON, DICKINSON AND COMPANY: Company website. http://www.bd.com. Last accessed 15 July 2015. 2

    [BFHC12] BRAY M. A., FRASER A. N., HASAKA T. P., CARPENTER A. E.: Workflow and metrics for image quality control in large-scale high-content screens. Journal of Biomolecular Screening 17, 2 (2012), 266-274. 15

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