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Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing

Zhen Yang1, Yen-Yi Ho1

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina, USA.

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Summary

Researchers developed a new statistical model, ZENCO, to analyze dynamic gene coexpression in single-cell RNA sequencing data. This model addresses challenges like overdispersion and zero inflation, improving the understanding of cellular signaling activities.

Keywords:
correlated count datacovariate-dependent correlationdynamic coexpressionliquid associationsingle-cell RNA sequencingzero inflation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Cellular interactions are dynamic, leading to observable changes in gene coexpression patterns.
  • Next-generation sequencing, particularly single-cell RNA sequencing (scRNA-seq), presents statistical challenges due to count-based data with overdispersion and zero inflation.

Purpose of the Study:

  • To develop a novel statistical approach for analyzing dynamic gene coexpression in zero-inflated count data.
  • To address overdispersion and zero inflation inherent in scRNA-seq data.

Main Methods:

  • Proposed the ZEro-inflated negative binomial dynamic COrrelation (ZENCO) model.
  • Modeled count data as a mixture of success amplifications and dropout events.
  • Incorporated a latent variable to capture covariate-dependent correlation structures.

Main Results:

  • Simulation studies demonstrated the performance of ZENCO.
  • ZENCO was compared favorably against existing methods.
  • The model's utility was illustrated using scRNA-seq data from melanoma research.

Conclusions:

  • ZENCO provides a robust method for exploring dynamic dependence structures in scRNA-seq and other zero-inflated count data.
  • The model effectively handles overdispersion and zero inflation, crucial for accurate analysis of gene coexpression.