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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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scDEcrypter: Uncertainty-aware differential expression analysis for viral infection in scRNA-seq.

Luer Zhong1, Karl Ensberg2,3,4, Scott Tibbets2,3,4

  • 1Department of Biostatistics, University of Florida, Gainesville, Florida, USA.

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Summary

scDEcrypter improves viral infection studies by accurately identifying infected cells and genes using a novel penalized mixture model. This method enhances differential expression analysis in single-cell RNA sequencing data.

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Area of Science:

  • Computational biology
  • Virology
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNA-seq) faces challenges in viral infection studies.
  • Sparse viral reads and under-labeled cells complicate differential expression (DE) analysis.
  • Bystander cell responses can confound results.

Purpose of the Study:

  • To introduce scDEcrypter, a new computational tool for analyzing viral infections in scRNA-seq data.
  • To improve the identification of infected cell states and associated gene expression.
  • To enable more accurate DE analysis in the presence of confounding factors.

Main Methods:

  • Developed scDEcrypter, a penalized two-way mixture model.
  • Incorporated partial labels for infection status and cell type.
  • Utilized data-splitting for robust inference and likelihood-based DE analysis.

Main Results:

  • scDEcrypter demonstrated improved recovery of infected cell states in simulations and real data.
  • Identified more biologically coherent infection-associated genes.
  • Revealed enriched pathways related to viral infection responses.

Conclusions:

  • scDEcrypter offers a powerful solution for analyzing viral infections using scRNA-seq.
  • The method enhances the accuracy and biological relevance of DE analysis.
  • Facilitates a deeper understanding of host-pathogen interactions at the single-cell level.