ArticleBioinformatics (Oxford, England)2014
Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 1 of them a synthesis that pooled it.
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Who cites it
91 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Neurobiology of attention-deficit hyperactivity disorder: historical challenges and emerging frontiers.Nature reviews. Neuroscience · 2024Review
- Assessing and mitigating batch effects in large-scale omics studies.Genome biology · 2024Review
- Thinking points for effective batch correction on biomedical data.Briefings in bioinformatics · 2024Article
- Machine learning-derived immunosenescence index for predicting outcome and drug sensitivity in patients with skin cutaneous melanoma.Genes and immunity · 2024Article
- Identification of HDAC9 and ARRDC4 as potential biomarkers and targets for treatment of type 2 diabetes.Scientific reports · 2024Article
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- Article
31 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
motivationSample 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.
resultsTherefore, 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.