Journal Article

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

Lin, Bingqing, Zhou, Yan, Wang, Junhui, Zhu, Jiadi, Zhang, Jun, Tong, Tiejun

2019

Overview

Background High-throughput techniques bring novel tools and 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, normalization 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 by using the 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”, which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html .

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Analysis and modelling of complex systems / Animals / Bioinformatics / Biomedical and Life Sciences / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Computer Simulation / Data analysis / Data processing / Differential expression / Empirical analysis / Gene expression / Gene Expression Profiling / Genes / Genetic research / Genomes / Genomics / Humans / Hypertension / HyperText Markup Language / Hypotheses / Hypothesis test / Life Sciences / Methodology / Methodology Article / Methods / Mice / Microarrays / Normalization / Novels / Orthologous genes / Ribonucleic acid / RNA / RNA sequencing / RNA-seq / Scaling / Scaling factors / Sequence Analysis, RNA - methods / Simulation / Species / Species Specificity / Statistical analysis / Statistics / Statistics as Topic / Transcription / Transcription (Genetics)