A Double-weighted Normalization Method for Identifying Differential Expression of RNA-seq Data
- 信息技术－已发表论文 
The normalization of high-throughput sequencing data from different sequencing conditions is a critical step of the entire high-throughput data analysis and processing. Normalization is important for the identification of gene structures and differentially expressed genes, which has great impact on the accuracy and reliability of downstream analysis procedures. Here, we propose a double-weighted normalization method for high-throughput sequencing data generated by RNA-seq, and present a p-value weighted method to detect differential expression from normalized data. This normalization method not only considers the overall expression level of all genes in a library, but also considers the impact of each individual gene. Experimental results show that our method can effectively normalize high-throughput data under different conditions to provide highly confident data for the downstream analysis of differential expression.