A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data.
A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data.
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DOI：
10.1371/journal.pcbi.1006391
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发表时间：
2018-08
影响因子：
4.3
通讯作者：
Yu T
中科院分区：
文献类型：
--
作者：
Yu T
动态相关性在高吞吐量数据中是普遍存在的。大量的基因对可以改变它们的相关模式，以响应观察到的/未观察到的生理状态的变化。发现相关模式的变化可以揭示重要的调控机制。目前还没有一种方法可以有效地检测数据集中的全局动态相关模式。鉴于该问题的挑战性，目前可用的方法使用基因作为生理状态的替代测量，其不能忠实地表示真实的潜在生物信号。在这项研究中，我们开发了一种新的方法，直接识别强潜在的动态相关信号的数据矩阵，名为DCA：动态相关分析。在该方法的中心是一个新的指标，用于识别的变量对，很可能是动态相关的，而不知道潜在的生理状态，管理动态相关性。我们验证了广泛的模拟的方法的性能。我们将该方法应用于三个真实的数据集：单细胞RNA-seq数据集，批量RNA-seq数据集和微阵列基因表达数据集。在所有三个数据集中，该方法揭示了具有明确生物学意义的新的潜在因素，为数据带来了新的见解。动态相关性是表达数据中的一个重要方面。然而，由于缺乏有效的方法来解开这一复杂的关系，它并没有得到太多的关注。在这里，我们描述了一种新的方法，代表了现有方法的实质性改进。它实现了有效地发现RNA-seq数据中的动态相关模式以及检测与动态相关模式相关的生物功能的目标。与传统的方法，专注于一阶结构，线性或非线性，我们的方法发现二阶模式，使洞察复杂系统的规定。新方法的一些有趣的发现，如一些肠上皮细胞的免疫功能，已被最近的生物学出版物所证实。
Dynamic correlations are pervasive in high-throughput data. Large numbers of gene pairs can change their correlation patterns in response to observed/unobserved changes in physiological states. Finding changes in correlation patterns can reveal important regulatory mechanisms. Currently there is no method that can effectively detect global dynamic correlation patterns in a dataset. Given the challenging nature of the problem, the currently available methods use genes as surrogate measurements of physiological states, which cannot faithfully represent true underlying biological signals. In this study we develop a new method that directly identifies strong latent dynamic correlation signals from the data matrix, named DCA: Dynamic Correlation Analysis. At the center of the method is a new metric for the identification of pairs of variables that are highly likely to be dynamically correlated, without knowing the underlying physiological states that govern the dynamic correlation. We validate the performance of the method with extensive simulations. We applied the method to three real datasets: a single cell RNA-seq dataset, a bulk RNA-seq dataset, and a microarray gene expression dataset. In all three datasets, the method reveals novel latent factors with clear biological meaning, bringing new insights into the data. Dynamic correlation is an important area in expression data. However it hasn’t received much attention because of the lack of effective methods that can unravel the complex relationship. Here we describe a new method that represents a substantial improvement over existing approaches. It achieves the goal of efficiently finding patterns of dynamic correlation in RNA-seq data, as well as detecting biological functions associated with the dynamic correlation patterns. Unlike traditional methods that focus on first-order structures, linear or nonlinear, our method finds second-order patterns that bring insights into the regulations of the complex system. Some of the interesting discoveries by the new method, such as immunological functions of some intestinal epithelial cells, are validated by recent biological publications.
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DOI：
10.1007/978-1-4939-6603-5_14
发表时间：
2017-01-01
期刊：
CELL CYCLE SYNCHRONIZATION: METHODS AND PROTOCOLS, 2ND EDITION
影响因子：
--
作者：
Smith, Jessica;Manukyan, Arkadi;Schneider, Brandt L.
通讯作者：
Schneider, Brandt L.
影响因子：
20.1
作者：
Chan SY;Loscalzo J
通讯作者：
Loscalzo J
影响因子：
7.8
作者：
Hyvärinen, A;Oja, E
通讯作者：
Oja, E
乳腺癌中免疫浸润及其临床意义的模式：一项基于基因表达的回顾性研究。
影响因子：
15.8
作者：
Ali HR;Chlon L;Pharoah PD;Markowetz F;Caldas C
通讯作者：
Caldas C
影响因子：
5.8
作者：
Wang, Lin;Liu, Silvia;Tseng, George C.
通讯作者：
Tseng, George C.