Open Access Research

A visual analytics approach for models of heterogeneous cell populations

Jan Hasenauer1*, Julian Heinrich2, Malgorzata Doszczak3, Peter Scheurich3, Daniel Weiskopf2 and Frank Allgöwer1

Author Affiliations

1 Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany

2 Visualization Research Center, University of Stuttgart, Allmandring 19, 70569 Stuttgart, Germany

3 Institute of Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany

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EURASIP Journal on Bioinformatics and Systems Biology 2012, 2012:4  doi:10.1186/1687-4153-2012-4

Published: 31 May 2012

Abstract

In recent years, cell population models have become increasingly common. In contrast to classic single cell models, population models allow for the study of cell-to-cell variability, a crucial phenomenon in most populations of primary cells, cancer cells, and stem cells. Unfortunately, tools for in-depth analysis of population models are still missing. This problem originates from the complexity of population models. Particularly important are methods to determine the source of heterogeneity (e.g., genetics or epigenetic differences) and to select potential (bio-)markers. We propose an analysis based on visual analytics to tackle this problem. Our approach combines parallel-coordinates plots, used for a visual assessment of the high-dimensional dependencies, and nonlinear support vector machines, for the quantification of effects. The method can be employed to study qualitative and quantitative differences among cells. To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.