A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data

Tianwei Yu

PLOS Computational Biology, 2018, vol. 14, issue 8, 1-22

Abstract: 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.Author summary: 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006391

DOI: 10.1371/journal.pcbi.1006391

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