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Object-Oriented Data Analysis of Cell Images

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  • Xiaosun Lu
  • J. S. Marron
  • Perry Haaland

Abstract

This article discusses a study of cell images in cell culture biology from an object-oriented point of view. The motivation of this research is to develop a statistical approach to cell image analysis that better supports the automated development of stem cell growth media. A major hurdle in this process is the need for human expertise, based on studying cells under the microscope, to make decisions about the next step of the cell culture process. We aim to use digital imaging technology coupled with statistical analysis to tackle this important problem. The discussion in this article highlights a common critical issue: choice of data objects. Instead of conventionally treating either the individual cells or the wells (a container in which the cells are grown) as data objects, a new type of data object is proposed, that is the union of a well with its corresponding set of cells. The image data analysis suggests that the cell-well unions can be a better choice of data objects than the cells or the wells alone. The data are available in the online supplementary materials.

Suggested Citation

  • Xiaosun Lu & J. S. Marron & Perry Haaland, 2014. "Object-Oriented Data Analysis of Cell Images," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 548-559, June.
    • Handle: RePEc:taf:jnlasa:v:109:y:2014:i:506:p:548-559
    DOI: 10.1080/01621459.2014.884503
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    References listed on IDEAS

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    1. Marron, J.S. & Todd, Michael J. & Ahn, Jeongyoun, 2007. "Distance-Weighted Discrimination," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1267-1271, December.
    2. Yuan Wang & J. S. Marron & Burcu Aydin & Alim Ladha & Elizabeth Bullitt & Haonan Wang, 2012. "A Nonparametric Regression Model With Tree-Structured Response," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1272-1285, December.
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    Cited by:

    1. Marron, J.S., 2017. "Big Data in context and robustness against heterogeneity," Econometrics and Statistics, Elsevier, vol. 2(C), pages 73-80.
    2. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.

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