Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction

Hilary S Parker; Jeffrey T Leek; Alexander V Favorov; Michael Considine; Xiaoxin Xia; Sameer Chavan; Christine H Chung; Elana J Fertig

doi:10.1093/bioinformatics/btu375

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction

Bioinformatics. 2014 Oct;30(19):2757-63. doi: 10.1093/bioinformatics/btu375. Epub 2014 Jun 6.

Authors

Hilary S Parker¹, Jeffrey T Leek¹, Alexander V Favorov², Michael Considine¹, Xiaoxin Xia¹, Sameer Chavan¹, Christine H Chung¹, Elana J Fertig¹

Affiliations

¹ Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms "GosNIIGenetika", Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.
² Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms "GosNIIGenetika", Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms "GosNIIGenetika", Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21205, USA, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow 119333, Russia, Research Institute for Genetics and Selection of Industrial Microorganisms "GosNIIGenetika", Moscow 117545, Russia, Department of Statistics and Biostatistics, Rutgers University, NJ 08854, USA and Division of Allergy & Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA.

Abstract

Motivation: Sample source, procurement process and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intragroup biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch-corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori.

Results: Therefore, we assess the extent to which various batch correction algorithms remove true biological heterogeneity. We also introduce an algorithm, permuted-SVA (pSVA), using a new statistical model that is blind to biological covariates to correct for technical artifacts while retaining biological heterogeneity in genomic data. This algorithm facilitated accurate subtype identification in head and neck cancer from gene expression data in both formalin-fixed and frozen samples. When applied to predict Human Papillomavirus (HPV) status, pSVA improved cross-study validation even if the sample batches were highly confounded with HPV status in the training set.

Availability and implementation: All analyses were performed using R version 2.15.0. The code and data used to generate the results of this manuscript is available from https://sourceforge.net/projects/psva.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Artifacts
Computational Biology / methods
Genomics / methods*
Head and Neck Neoplasms / genetics*
Head and Neck Neoplasms / virology
Humans
Models, Statistical
Papillomavirus Infections / diagnosis*
Reproducibility of Results
Software

Abstract

Publication types

MeSH terms

Grants and funding