Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

作者：James Bullard, Elizabeth Purdom, Kasper D. Hansen, Sandrine Dudoit · 发表于：BMC Bioinformatics · 年份：2010 · DOI：10.1186/1471-2105-11-94 · 被引用次数：1795 · 研究领域：Gene expression and cancer classification、Genomics and Phylogenetic Studies、Single-cell and spatial transcriptomics

BACKGROUND: High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data. RESULTS: We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection. CONCLUSIONS: Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, t...