异质细胞群模型的可视化分析方法。

Jan Hasenauer, Julian Heinrich, Malgorzata Doszczak, Peter Scheurich, Daniel Weiskopf, Frank Allgöwer
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引用次数: 12

摘要

近年来,细胞群体模型已经变得越来越普遍。与经典的单细胞模型相比,群体模型允许研究细胞间的变异性,这是大多数原代细胞、癌细胞和干细胞群体中的关键现象。不幸的是,对人口模型进行深入分析的工具仍然缺失。这个问题源于人口模型的复杂性。特别重要的是确定异质性来源(例如,遗传学或表观遗传学差异)和选择潜在(生物)标记的方法。我们提出了一种基于视觉分析的分析方法来解决这个问题。我们的方法结合了平行坐标图,用于高维依赖性的视觉评估,以及非线性支持向量机,用于效果的量化。该方法可用于研究细胞间的定性和定量差异。为了说明不同的组成部分,我们进行了一个案例研究,使用促凋亡信号转导途径参与细胞凋亡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A visual analytics approach for models of heterogeneous cell populations.

A visual analytics approach for models of heterogeneous cell populations.

A visual analytics approach for models of heterogeneous cell populations.

A visual analytics approach for models of heterogeneous cell populations.

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.

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