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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2263-6 |
| _version_ | 1828480867880665088 |
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| author | Yuqing Zhang David F. Jenkins Solaiappan Manimaran W. Evan Johnson |
| author_facet | Yuqing Zhang David F. Jenkins Solaiappan Manimaran W. Evan Johnson |
| author_sort | Yuqing Zhang |
| collection | DOAJ |
| description | 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 multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies. Results Here we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios. Conclusions We demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations. |
| first_indexed | 2024-12-11T07:46:43Z |
| format | Article |
| id | doaj.art-11c13fcbc012456e85f49cae87e51a9f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T07:46:43Z |
| publishDate | 2018-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-11c13fcbc012456e85f49cae87e51a9f2022-12-22T01:15:27ZengBMCBMC Bioinformatics1471-21052018-07-0119111510.1186/s12859-018-2263-6Alternative empirical Bayes models for adjusting for batch effects in genomic studiesYuqing Zhang0David F. Jenkins1Solaiappan Manimaran2W. Evan Johnson3Division of Computational Biomedicine, Boston University School of MedicineDivision of Computational Biomedicine, Boston University School of MedicineDivision of Computational Biomedicine, Boston University School of MedicineDivision of Computational Biomedicine, Boston University School of MedicineAbstract 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 multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies. Results Here we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios. Conclusions We demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.http://link.springer.com/article/10.1186/s12859-018-2263-6Batch effectsEmpirical Bayes modelsData integrationBiomarker development |
| spellingShingle | Yuqing Zhang David F. Jenkins Solaiappan Manimaran W. Evan Johnson Alternative empirical Bayes models for adjusting for batch effects in genomic studies BMC Bioinformatics Batch effects Empirical Bayes models Data integration Biomarker development |
| title | Alternative empirical Bayes models for adjusting for batch effects in genomic studies |
| title_full | Alternative empirical Bayes models for adjusting for batch effects in genomic studies |
| title_fullStr | Alternative empirical Bayes models for adjusting for batch effects in genomic studies |
| title_full_unstemmed | Alternative empirical Bayes models for adjusting for batch effects in genomic studies |
| title_short | Alternative empirical Bayes models for adjusting for batch effects in genomic studies |
| title_sort | alternative empirical bayes models for adjusting for batch effects in genomic studies |
| topic | Batch effects Empirical Bayes models Data integration Biomarker development |
| url | http://link.springer.com/article/10.1186/s12859-018-2263-6 |
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