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Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures
Background. Many gene-expression signatures exist for describing the biological state of profiled tumors. Principal Component Analysis (PCA) can be used to summarize a gene signature into a single score. Our hypothesis is that gene signatures can be validated when applied to new datasets, using inhe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5317117/ https://www.ncbi.nlm.nih.gov/pubmed/28265563 http://dx.doi.org/10.1155/2017/2354564 |
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author | Berglund, Anders E. Welsh, Eric A. Eschrich, Steven A. |
author_facet | Berglund, Anders E. Welsh, Eric A. Eschrich, Steven A. |
author_sort | Berglund, Anders E. |
collection | PubMed |
description | Background. Many gene-expression signatures exist for describing the biological state of profiled tumors. Principal Component Analysis (PCA) can be used to summarize a gene signature into a single score. Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA. Results. This validation is based on four key concepts. Coherence: elements of a gene signature should be correlated beyond chance. Uniqueness: the general direction of the data being examined can drive most of the observed signal. Robustness: if a gene signature is designed to measure a single biological effect, then this signal should be sufficiently strong and distinct compared to other signals within the signature. Transferability: the derived PCA gene signature score should describe the same biology in the target dataset as it does in the training dataset. Conclusions. The proposed validation procedure ensures that PCA-based gene signatures perform as expected when applied to datasets other than those that the signatures were trained upon. Complex signatures, describing multiple independent biological components, are also easily identified. |
format | Online Article Text |
id | pubmed-5317117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-53171172017-03-06 Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures Berglund, Anders E. Welsh, Eric A. Eschrich, Steven A. Int J Genomics Research Article Background. Many gene-expression signatures exist for describing the biological state of profiled tumors. Principal Component Analysis (PCA) can be used to summarize a gene signature into a single score. Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA. Results. This validation is based on four key concepts. Coherence: elements of a gene signature should be correlated beyond chance. Uniqueness: the general direction of the data being examined can drive most of the observed signal. Robustness: if a gene signature is designed to measure a single biological effect, then this signal should be sufficiently strong and distinct compared to other signals within the signature. Transferability: the derived PCA gene signature score should describe the same biology in the target dataset as it does in the training dataset. Conclusions. The proposed validation procedure ensures that PCA-based gene signatures perform as expected when applied to datasets other than those that the signatures were trained upon. Complex signatures, describing multiple independent biological components, are also easily identified. Hindawi Publishing Corporation 2017 2017-02-06 /pmc/articles/PMC5317117/ /pubmed/28265563 http://dx.doi.org/10.1155/2017/2354564 Text en Copyright © 2017 Anders E. Berglund et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Berglund, Anders E. Welsh, Eric A. Eschrich, Steven A. Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures |
title | Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures |
title_full | Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures |
title_fullStr | Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures |
title_full_unstemmed | Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures |
title_short | Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures |
title_sort | characteristics and validation techniques for pca-based gene-expression signatures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5317117/ https://www.ncbi.nlm.nih.gov/pubmed/28265563 http://dx.doi.org/10.1155/2017/2354564 |
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