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Updated: Sep 13, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics

Shuyi Yang1, Anderson Bussing2, Giampiero Marra3

  • 1Department of Statistics, University of South Carolina, Columbia, USA. shuyi@email.sc.edu.

BMC Bioinformatics
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

TIME-CoExpress models non-linear gene co-expression dynamics in single-cell RNA sequencing (scRNAseq) data. This approach captures complex gene interactions and expression changes during cellular development for deeper biological insights.

Keywords:
Covariate-dependent correlation structureDynamic correlationNon-linear regressionPseudotimeSemiparametric regressionSingle-cell RNA sequencingZero-inflated bivariate count data

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Area of Science:

  • Genomics
  • Computational Biology
  • Developmental Biology

Background:

  • Single-cell RNA sequencing (scRNAseq) offers high-resolution transcriptomic data.
  • Traditional gene expression analysis often overlooks gene interactions.
  • Existing methods for gene co-expression analysis may not capture complex, non-linear relationships.

Purpose of the Study:

  • To develop a flexible framework for modeling non-linear gene co-expression dynamics.
  • To address limitations of existing methods in capturing complex gene interactions.
  • To provide a more comprehensive analysis of scRNAseq data, including gene-level characteristics.

Main Methods:

  • Proposed a copula-based framework named TIME-CoExpress.
  • Incorporated data-driven smoothing functions for non-linear modeling.
  • Accounted for over-dispersion and zero-inflation common in scRNAseq data.

Main Results:

  • TIME-CoExpress successfully models non-linear changes in gene co-expression along temporal trajectories.
  • The framework captures dynamic changes in gene-level zero-inflation and mean expression.
  • Identified differentially co-expressed gene pairs during pituitary embryonic development in mice.

Conclusions:

  • The TIME-CoExpress framework enables robust identification of dynamic, non-linear changes in scRNAseq data.
  • This approach enhances understanding of gene regulation and biological processes during cellular development.
  • Provides deeper insights into complex gene interactions throughout development.