Object-Oriented Data Analysis of Cell Images

• 成果类型: Article
• 署名作者: Lu, Xiaosun; Marron, J. S.; Haaland, Perry
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.884503
• 发表日期: 2014
• 页码: 548-559
• 关键词: mesenchymal stem-cells; GROWTH; differentiation

摘要

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.