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.
Highlights
Cell populations are heterogeneous in terms of, e.g, cell age, cell cycle state, and protein abundance [1,2]
Proapoptotic signaling is involved in the process of apoptosis [32,33,34], called programmed cell death
The apoptotic signaling pathways converge at the caspase cascade [32], where initiator caspases and effector caspases are activated
Summary
Cell populations are heterogeneous in terms of, e.g, cell age, cell cycle state, and protein abundance [1,2]. To understand and control the behavior of populations, the key sources of cell-to-cell variability have to be unraveled. This is challenging due to experimental constraints. Most experimental systems and measurement devices only allow for the simultaneous assessment of a few cellular properties on a single cell basis. This prohibits the purely experimental analysis of processes which depend on many different cellular properties. Spencer et al [5] have shown that the experimental limitations can be overcome partially using mathematical models
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