Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures.

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

SUBMITTER: Berglund AE 

PROVIDER: S-EPMC5317117 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature