Alternative empirical Bayes models for adjusting for batch effects in genomic studies

Alternative empirical Bayes models for adjusting for batch effects in genomic studies

Abstract Background Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across m...

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Bibliographic Details
Main Authors: Yuqing Zhang, David F. Jenkins, Solaiappan Manimaran, W. Evan Johnson
Format: Article
Language:English
Published: BMC 2018-07-01
Series:BMC Bioinformatics
Subjects:
Batch effects
Empirical Bayes models
Data integration
Biomarker development
Online Access:http://link.springer.com/article/10.1186/s12859-018-2263-6

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http://link.springer.com/article/10.1186/s12859-018-2263-6

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