Two-variate phenotype-targeted tests for detecting phenotypic biomarkers in cancers
by Jinxiong Lv; Shikui Tu; Lei Xu
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 24, No. 1, 2020

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.

Online publication date: Thu, 10-Sep-2020

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