Super-delta: a new differential gene expression analysis procedure with robust data normalization

BMC Bioinformatics

Methodology Article

Xing Qiu¹  Yuhang Liu²  Jinfeng Zhang² 

[1] Department of Biostatistics and Computational Biology, University of Rochester, 14624, Rochester, NY, USA;Department of Statistics, Florida State University, 32306, Tallahassee, FL, USA;

关键词: Gene expression;    Differential expression analysis;    Super-delta;    Robust normalization;    t;   

DOI  :  10.1186/s12859-017-1992-2

 received in 2017-04-28, accepted in 2017-12-06,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundNormalization is an important data preparation step in gene expression analyses, designed to remove various systematic noise. Sample variance is greatly reduced after normalization, hence the power of subsequent statistical analyses is likely to increase. On the other hand, variance reduction is made possible by borrowing information across all genes, including differentially expressed genes (DEGs) and outliers, which will inevitably introduce some bias. This bias typically inflates type I error; and can reduce statistical power in certain situations. In this study we propose a new differential expression analysis pipeline, dubbed as super-delta, that consists of a multivariate extension of the global normalization and a modified t-test. A robust procedure is designed to minimize the bias introduced by DEGs in the normalization step. The modified t-test is derived based on asymptotic theory for hypothesis testing that suitably pairs with the proposed robust normalization.ResultsWe first compared super-delta with four commonly used normalization methods: global, median-IQR, quantile, and cyclic loess normalization in simulation studies. Super-delta was shown to have better statistical power with tighter control of type I error rate than its competitors. In many cases, the performance of super-delta is close to that of an oracle test in which datasets without technical noise were used. We then applied all methods to a collection of gene expression datasets on breast cancer patients who received neoadjuvant chemotherapy. While there is a substantial overlap of the DEGs identified by all of them, super-delta were able to identify comparatively more DEGs than its competitors. Downstream gene set enrichment analysis confirmed that all these methods selected largely consistent pathways. Detailed investigations on the relatively small differences showed that pathways identified by super-delta have better connections to breast cancer than other methods.ConclusionsAs a new pipeline, super-delta provides new insights to the area of differential gene expression analysis. Solid theoretical foundation supports its asymptotic unbiasedness and technical noise-free properties. Implementation on real and simulated datasets demonstrates its decent performance compared with state-of-art procedures. It also has the potential of expansion to be incorporated with other data type and/or more general between-group comparison problems.

【 授权许可】

CC BY   
© The Author(s) 2017

【 图 表 】

12864_2015_1854_Article_IEq1.gif

12864_2017_3781_Article_IEq9.gif

12864_2017_3521_Article_IEq4.gif

12864_2017_3777_Article_IEq11.gif

12864_2017_3521_Article_IEq3.gif

12864_2017_3487_Article_IEq55.gif

12877_2015_20_Article_IEq1.gif

12864_2017_4131_Article_IEq1.gif

12864_2015_2073_Article_IEq8.gif

12864_2016_3097_Article_IEq6.gif

12864_2015_1933_Article_IEq3.gif

12864_2015_2199_Article_IEq13.gif

12870_2017_1068_Article_IEq24.gif

12864_2017_3623_Article_IEq1.gif

12864_2016_3440_Article_IEq69.gif

12864_2015_2129_Article_IEq31.gif

12864_2017_3645_Article_IEq4.gif

12711_2017_331_Article_IEq65.gif

12864_2017_3492_Article_IEq4.gif

12864_2017_4359_Article_IEq1.gif

12864_2017_3605_Article_IEq4.gif

12888_2017_1559_Article_IEq1.gif

12864_2017_4132_Article_IEq31.gif

12888_2017_1528_Article_IEq1.gif

12864_2017_3990_Article_IEq10.gif

12864_2017_4132_Article_IEq30.gif

12864_2017_3670_Article_IEq15.gif

12864_2017_3670_Article_IEq13.gif

12864_2015_2304_Article_IEq10.gif

12864_2017_4071_Article_IEq4.gif

12864_2017_3487_Article_IEq34.gif

12864_2017_4132_Article_IEq22.gif

12864_2017_4071_Article_IEq1.gif

12864_2017_4132_Article_IEq20.gif

12914_2017_113_Article_IEq1.gif

12864_2017_3487_Article_IEq28.gif

12864_2015_1944_Article_IEq3.gif

12864_2017_4030_Article_IEq35.gif

12864_2017_4133_Article_IEq3.gif

12864_2017_3487_Article_IEq25.gif

12864_2017_4132_Article_IEq14.gif

12864_2017_4132_Article_IEq12.gif

12864_2017_4132_Article_IEq11.gif

12864_2017_4132_Article_IEq10.gif

12864_2017_4226_Article_IEq1.gif

12864_2017_4132_Article_IEq7.gif

12864_2016_2756_Article_IEq2.gif