ArticlePLoS computational biology2018
A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.Statistics in medicine · 2025Article
- Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data.BMC bioinformatics · 2025Article
- Dissecting genetic regulation of metabolic coordination.Briefings in bioinformatics · 2025Article
- Genome-wide search algorithms for identifying dynamic gene co-expression via Bayesian variable selection.Statistics in medicine · 2023Article
- A bivariate zero-inflated negative binomial model and its applications to biomedical settings.Statistical methods in medical research · 2023Article
- Small RNA sequencing identified miR-3180 as a potential prognostic biomarker for Chinese hepatocellular carcinoma patients.Frontiers in genetics · 2023Article
- Generalized Liquid Association Analysis for Multimodal Data Integration.Journal of the American Statistical Association · 2023Article
- Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data.Biometrics · 2022Article
- LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation.Frontiers in genetics · 2020Article
- DNLC: differential network local consistency analysis.BMC bioinformatics · 2019Article
- A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale.BMC genomics · 2019Article
- Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity.Frontiers in genetics · 2019Article
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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.
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