Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures. (6th February 2017)

• Record Type: Journal Article
• Authors: Berglund, Anders E.; Welsh, Eric A.; Eschrich, Steven A.
• Other Names: Wolf Bethany Academic Editor.
• Is Part Of: International journal of genomics. Volume 2017(2017)
• Publication Date: 2017-02-06
• Subjects: Genomes -- Periodicals; Genomics -- Periodicals; Cytogenetics -- Periodicals; Genomics; Genome; Molecular Biology; Cytogenetics; Genomes; Genomics; Periodicals; 572.86
• DOI: 10.1155/2017/2354564
• Languages: English
• ISSNs: 2314-436X

Abstract

Abstract : 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.