Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment

BMC Bioinformatics

Methodology Article

David Causeur¹  Anne-Laure Boulesteix²  Roman Hornung² 

[1] Applied Mathematics Department, Agrocampus Ouest, 65 rue de St. Brieuc, 35042, Rennes, France;Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, D-81377, Munich, Germany;

关键词: Batch effects;    High-dimensional data;    Data preparation;    Prediction;    Latent factors;   

DOI  :  10.1186/s12859-015-0870-z

 received in 2015-09-24, accepted in 2015-12-22,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundIn the context of high-throughput molecular data analysis it is common that the observations included in a dataset form distinct groups; for example, measured at different times, under different conditions or even in different labs. These groups are generally denoted as batches. Systematic differences between these batches not attributable to the biological signal of interest are denoted as batch effects. If ignored when conducting analyses on the combined data, batch effects can lead to distortions in the results. In this paper we present FAbatch, a general, model-based method for correcting for such batch effects in the case of an analysis involving a binary target variable. It is a combination of two commonly used approaches: location-and-scale adjustment and data cleaning by adjustment for distortions due to latent factors. We compare FAbatch extensively to the most commonly applied competitors on the basis of several performance metrics. FAbatch can also be used in the context of prediction modelling to eliminate batch effects from new test data. This important application is illustrated using real and simulated data. We implemented FAbatch and various other functionalities in the R package bapred available online from CRAN.ResultsFAbatch is seen to be competitive in many cases and above average in others. In our analyses, the only cases where it failed to adequately preserve the biological signal were when there were extremely outlying batches and when the batch effects were very weak compared to the biological signal.ConclusionsAs seen in this paper batch effect structures found in real datasets are diverse. Current batch effect adjustment methods are often either too simplistic or make restrictive assumptions, which can be violated in real datasets. Due to the generality of its underlying model and its ability to perform well FAbatch represents a reliable tool for batch effect adjustment for most situations found in practice.

【 授权许可】

CC BY   
© Hornung et al. 2016

【 图 表 】

12864_2016_2789_Article_IEq42.gif

12864_2017_3733_Article_IEq62.gif

12864_2017_3733_Article_IEq61.gif

12864_2015_2137_Article_IEq7.gif

12870_2017_1151_Article_IEq3.gif

12864_2017_3777_Article_IEq20.gif

12864_2017_4020_Article_IEq20.gif

12864_2017_4132_Article_IEq44.gif

12864_2017_4133_Article_IEq43.gif

12864_2017_4133_Article_IEq42.gif

12864_2017_4269_Article_IEq4.gif

12864_2017_4269_Article_IEq3.gif

12864_2017_4269_Article_IEq2.gif

12864_2017_3492_Article_IEq18.gif

12864_2017_3609_Article_IEq14.gif

12864_2017_3492_Article_IEq17.gif

12864_2016_2789_Article_IEq41.gif

12864_2016_2682_Article_IEq23.gif

12864_2017_3733_Article_IEq60.gif

12864_2016_3098_Article_IEq29.gif

12864_2017_3733_Article_IEq59.gif

12864_2016_2463_Article_IEq1.gif

12864_2017_3777_Article_IEq18.gif

12864_2017_3487_Article_IEq63.gif

12864_2017_3777_Article_IEq17.gif

12864_2017_3487_Article_IEq62.gif

12894_2015_Article_5_TeX2GIF_IEq1.gif

12864_2017_3487_Article_IEq60.gif

12864_2015_2137_Article_IEq5.gif

12864_2017_3777_Article_IEq13.gif

12864_2017_3777_Article_IEq11.gif

12864_2017_3487_Article_IEq57.gif

12864_2015_2198_Article_IEq11.gif

12864_2017_3487_Article_IEq56.gif

12864_2017_3487_Article_IEq55.gif

12864_2017_3487_Article_IEq54.gif

12864_2015_2073_Article_IEq8.gif

12864_2015_2199_Article_IEq13.gif

12864_2016_3440_Article_IEq72.gif

12864_2016_3440_Article_IEq71.gif