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Novel Data Transformations for RNA-seq Differential Expression Analysis.

Zeyu Zhang1, Danyang Yu2, Minseok Seo3

  • 1Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China.

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New data transformations improve RNA-seq analysis, especially for small or large sample sizes. These methods enhance accuracy and control false discovery rates, offering alternatives to standard voom transformations in differential gene expression studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA-sequencing (RNA-seq) data analysis often requires transformations to meet statistical assumptions like normality.
  • Existing methods, such as voom, may not always be optimal for count data, especially with varying sample sizes.

Purpose of the Study:

  • To introduce and evaluate eight novel data transformations for RNA-seq analysis.
  • To compare the performance of these transformations with the standard voom transformation using simulation and real data.

Main Methods:

  • Proposed eight data transformations: r, r2, rv, rv2, l, l2, lv, and lv2.
  • Utilized simulation studies with small, moderate, and large sample sizes (n=3, 30, 50, 100).
  • Applied the limma package for differential expression analysis and compared performance metrics (accuracy, FDR, FNR).
  • Also evaluated the Wilcoxon rank sum test on raw data for large sample sizes.

Main Results:

  • For small and large sample sizes, limma with r, l, and r2 transformations outperformed voom in accuracy, FDR, and FNR.
  • For moderate sample sizes, limma with rv and rv2 transformations showed performance comparable to voom.
  • In large sample size datasets, Wilcoxon rank sum test on raw data surpassed limma with transformed data for gene selection.

Conclusions:

  • The proposed transformations, particularly r, l, and r2, offer improved performance over voom for RNA-seq analysis in small or large sample size scenarios.
  • The choice of transformation can significantly impact differential gene expression analysis results, highlighting the need for tailored approaches.
  • For large sample sizes, non-parametric tests on raw data may be a competitive alternative for gene selection.