Title: Two-variate phenotype-targeted tests for detecting phenotypic biomarkers in cancers

Authors: Jinxiong Lv; Shikui Tu; Lei Xu

Addresses: Centre for Cognitive Machines and Computational Health (CMaCH), Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China ' Centre for Cognitive Machines and Computational Health (CMaCH), Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China ' Centre for Cognitive Machines and Computational Health (CMaCH), Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Abstract: Detection of cancer-related phenotypic biomarkers is crucial for clinical research. Traditional pipeline consists of two stages, i.e., candidates are first selected to be significantly differentially expressed between tumour-adjacent and tumour conditions, and then later are filtered by Phenotype-Targeted tests (PT tests). Such two-phase process has low-detection power. In this paper, two-variate PT test, which jointly considers tumour-adjacent data and tumour data, is adopted to strengthen the detection power. We conduct a systematic investigation on the three implementations of two-variate PT tests for detecting phenotypic biomarkers in three types of cancers, and provide a practical guideline for the usage of the two-variate PT tests. Experimental analysis indicates that the two-variate PT tests achieve stronger detection power than traditional methods. The tumour-adjacent data provides complementary information to the discriminant analysis, and Fisher discriminant analysis is able to best implement two-variate PT test for detecting phenotypic biomarkers in cancers.

Keywords: two-variate phenotype-targeted test; phenotypic biomarkers; breast cancer; lung cancer; thyroid cancer; body mass index; overall survival time; pathologic stage; microarray expression data; RNA-seq expression data.

DOI: 10.1504/IJDMB.2020.109501

International Journal of Data Mining and Bioinformatics, 2020 Vol.24 No.1, pp.38 - 57

Received: 18 Mar 2020
Accepted: 18 Mar 2020
Published online: 10 Sep 2020 *

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