Bronstein L, Zechner C, Koeppl H
Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.
Department of Biosystems Sciences and Engineering, ETH Zürich, Basel, Switzerland.
Methods. 2015 Sep 1;85:22-35. doi: 10.1016/j.ymeth.2015.05.012. Epub 2015 May 15.
Single-cell experimental techniques provide informative data to help uncover dynamical processes inside a cell. Making full use of such data requires dedicated computational methods to estimate biophysical process parameters and states in a model-based manner. In particular, the treatment of heterogeneity or cell-to-cell variability deserves special attention. The present article provides an introduction to one particular class of algorithms which employ marginalization in order to take heterogeneity into account. An overview of alternative approaches is provided for comparison. We treat two frequently encountered scenarios in single-cell experiments, namely, single-cell trajectory data and single-cell distribution data.
单细胞实验技术提供了丰富的信息数据，有助于揭示细胞内部的动态过程。充分利用这些数据需要专门的计算方法，以便以基于模型的方式估计生物物理过程参数和状态。特别是，异质性或细胞间变异性的处理值得特别关注。本文介绍了一类特定的算法，这类算法采用边缘化来考虑异质性。还提供了其他方法的概述以供比较。我们处理单细胞实验中经常遇到的两种情况，即单细胞轨迹数据和单细胞分布数据。
