Reconstructing the Temporal Progression of Biological Data Using Cluster Spanning Trees.
Reconstructing the Temporal Progression of Biological Data Using Cluster Spanning Trees.
复制标题
使用簇生成树重建生物数据的时间进程。
DOI：
10.1109/tnb.2017.2667402
复制
发表时间：
2017
影响因子：
3.9
通讯作者：
Singh,Rahul
中科院分区：
文献类型：
--
作者：
Eshleman,Ryan;Singh,Rahul
识别一组生物样品的时间进展对于理解潜在分子相互作用的动力学是至关重要的。它通常也是数据去噪和同步的基本步骤。最后，确定进展顺序对于细胞谱系鉴定、疾病进展、肿瘤分类和流行病学等问题至关重要，因此影响了基础生物学、药物发现和公共卫生等学科的范围。当前试图解决这个问题的方法在需要将数据中的复杂关系（例如分组、偏序或分叉或多分叉进展）考虑在内时面临困难。我们提出了集群生成树（CST）的概念，可以模拟线性以及上述复杂的进展关系，在时间上不断变化的数据。通过大量的实验研究，包括合成数据集以及细胞周期，细胞分化，表型筛选和遗传变异的数据集，我们表明，建议的CST方法优于现有的方法在重建数据的时间进程。
Identifying the temporal progression of a set of biological samples is crucial for comprehending the dynamics of the underlying molecular interactions. It is often also a basic step in data denoising and synchronization. Finally, identifying the progression order is crucial for problems like cell lineage identification, disease progression, tumor classification, and epidemiology and thus impacts the spectrum of disciplines spanning basic biology, drug discovery, and public health. Current methods that attempt solving this problem, face difficulty when it is necessary to factor-in complex relationships within the data, such as grouping, partial ordering or bifurcating or multifurcating progressions. We propose the notion of cluster spanning trees (CST) that can model both linear as well as the aforementioned complex progression relationships in temporally evolving data. Through a number of experimental investigations involving synthetic data sets as well as data sets from the cell cycle, cellular differentiation, phenotypic screening, and genetic variation, we show that the proposed CST approach outperforms existing methods in reconstructing the temporal progression of the data.