Dataset Information

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

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.

SUBMITTER: Parker HS

PROVIDER: S-EPMC4173013 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Parker Hilary S HS Leek Jeffrey T JT Favorov Alexander V AV Considine Michael M Xia Xiaoxin X Chavan Sameer S Chung Christine H CH Fertig Elana J EJ

Bioinformatics (Oxford, England) 20140606 19

<h4>Motivation</h4>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 ...[more]

PMID: 24907368

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Dataset Information

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

Motivation

Results

Availability and implementation

Publications

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

Similar Datasets

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