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

TL;DR

This paper introduces a novel normalization method for RNA-seq data across different species, improving the accuracy of differential gene expression analysis by accounting for conserved orthologous genes and technical variations.

Contribution

The paper presents the SCBN normalization method that incorporates conserved orthologous gene knowledge and hypothesis testing to enhance cross-species RNA-seq analysis.

Findings

SCBN outperforms existing normalization methods in simulations.

The method effectively accounts for gene length and unmapped genes.

Application to real data confirms improved differential expression detection.

Abstract

Background: High-throughput techniques bring novel tools but also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, the normalization procedure serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN" and the software is available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html.