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

Joint Authors

Berglund, Anders E.
Welsh, Eric A.
Eschrich, Steven A.

Source

International Journal of Genomics

Issue

Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2017-02-06

Country of Publication

Egypt

No. of Pages

13

Main Subjects

Biology

Abstract EN

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.

American Psychological Association (APA)

Berglund, Anders E.& Welsh, Eric A.& Eschrich, Steven A.. 2017. Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures. International Journal of Genomics،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1167003

Modern Language Association (MLA)

Berglund, Anders E.…[et al.]. Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures. International Journal of Genomics No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1167003

American Medical Association (AMA)

Berglund, Anders E.& Welsh, Eric A.& Eschrich, Steven A.. Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures. International Journal of Genomics. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1167003

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1167003