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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

Hilary S Parker1, Jeffrey T Leek1, Alexander V Favorov2

  • 1Department 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.

Bioinformatics (Oxford, England)
|June 8, 2014
PubMed
Summary

Batch effects in genomics data can obscure biological insights. A new algorithm, permuted-SVA (pSVA), corrects technical artifacts while preserving crucial biological heterogeneity for accurate subtype discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Technical variations in sample processing introduce batch effects in genomics data.
  • Standard batch correction methods may remove genuine biological heterogeneity, hindering subtype identification.
  • Accurate correction is challenging for future clinical tests with unknown biological groups.

Purpose of the Study:

  • To assess the impact of batch correction algorithms on biological heterogeneity.
  • To introduce a novel algorithm, permuted-SVA (pSVA), for artifact correction that preserves biological variation.
  • To enable accurate subtype identification from corrected genomics data.

Main Methods:

  • Evaluation of existing batch correction algorithms for their effect on biological heterogeneity.
  • Development of permuted-SVA (pSVA), a novel statistical model blind to biological covariates.
  • Application of pSVA to gene expression data for head and neck cancer subtype identification.

Main Results:

  • pSVA successfully corrected technical artifacts while retaining biological heterogeneity in genomics data.
  • Accurate subtype identification was achieved in head and neck cancer using formalin-fixed and frozen samples.
  • pSVA improved cross-study validation for predicting Human Papillomavirus (HPV) status, even with confounded sample batches.

Conclusions:

  • The permuted-SVA (pSVA) algorithm offers a robust approach to batch effect correction in genomics.
  • pSVA facilitates the discovery of novel biological subtypes and enhances the reliability of clinical genomic tests.
  • This method preserves essential biological heterogeneity, crucial for personalized genomic signatures.