Gene expression classification using L1 norm PCA

Zachary Walker; Colleen P. Bailey; Hae Jin Kim

doi:10.1117/12.2560883

24 April 2020 Gene expression classification using L1 norm PCA

Zachary Walker, Colleen P. Bailey, Hae Jin Kim

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 113950I (2020) https://doi.org/10.1117/12.2560883
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Gene microarray data generally includes high dimension, small sample datasets prone to noise. Analyzing this data using supervised and non-supervised learning algorithms is extremely useful for gene characterization, disease diagnosis, and genetic therapy in the medical field. For many years, principal component analysis (PCA) has been used as a tool in algorithms for gene expression classification. Previous solutions utilize L² norm based PCA, however with its superior resistance to outlier data, L₁ norm PCA offers improved results. Both methods are compared using support vector machines (SVM) to classify genetic mutations and co-regulation in several publicly available datasets. Methods utilizing L¹ PCA result in improved accuracy compared to L₂ PCA when used as a pre-processing step to SVM classification for gene microarray data.

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Citation Download Citation

Zachary Walker, Colleen P. Bailey, and Hae Jin Kim "Gene expression classification using L1 norm PCA", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950I (24 April 2020); https://doi.org/10.1117/12.2560883

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