This package take into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to get scaling factor and detect differential expression orthologous genes. The methods on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species'[1].
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. In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and the hypothesis testing framework. Considering the different gene lengths and unmapped genes between species, we formulate the problem from the perspective of hypothesis testing and search for an optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors.
Please refer to the "SCBN.pdf" vignetee for detailed function instructions.
- A statistical normalization method and differential expression analysis for RNA-seq data between different species. Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication).
