Object-Oriented Data Analysis of Cell Images

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.

KEY WORDS:

• Cell confluence
• Data objects
• Distance weighted discrimination
• Principal component analysis

SUPPLEMENTARY MATERIALS

• Data: The data are contained in a single archive, including the bright field cell images, the IPLab images of cell segmentation, the extracted image features and the bio-assessment by the four biologists.