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A statistical normalization method and differential expression analysis for RNA-seq data between different species.

Yan Zhou1, Jiadi Zhu1, Tiejun Tong2

  • 1College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, 518060, China.

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|March 31, 2019
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Summary
This summary is machine-generated.

A new scale-based normalization (SCBN) method effectively identifies conserved genes across species by minimizing statistical errors. This approach improves cross-species genomic comparisons and offers a practical R package for researchers.

Keywords:
Differential expressionHypothesis testNormalizationOrthologous genesRNA-seq

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Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • High-throughput sequencing generates vast genomic data, presenting statistical challenges.
  • Differential gene expression analysis between species aids in discovering conserved transcriptional responses.
  • Normalization is essential for adjusting technical variations like sequencing depth in cross-species comparisons.

Purpose of the Study:

  • To develop a novel normalization method for cross-species genomic analysis.
  • To address challenges posed by varying gene lengths and unmapped genes.
  • To improve the accuracy of identifying conserved genes.

Main Methods:

  • Proposed a scale-based normalization (SCBN) method utilizing conserved orthologous genes.
  • Employed a hypothesis testing framework to find optimal scaling factors.
  • Minimized deviation between empirical and nominal Type I errors.

Main Results:

  • SCBN demonstrated superior performance compared to existing methods in simulations.
  • Analysis of an RNA-seq dataset confirmed SCBN's outperformance.
  • The method effectively accounts for gene length and unmapped gene variations.

Conclusions:

  • SCBN provides a robust and accurate normalization strategy for cross-species transcriptomic studies.
  • The method is validated through simulations and real-world data.
  • An R package 'SCBN' is available for practical implementation.