Hongwei Wang1, Qiang Sun1, Wenyuan Zhao1, Lishuang Qi1, Yunyan Gu1, Pengfei Li1, Mengmeng Zhang1, Yang Li1, Shu-Lin Liu2, Zheng Guo2. 1. College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China. 2. College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China.
Abstract
MOTIVATION: The differential expression analysis focusing on inter-group comparison can capture only differentially expressed genes (DE genes) at the population level, which may mask the heterogeneity of differential expression in individuals. Thus, to provide patient-specific information for personalized medicine, it is necessary to conduct differential expression analysis at the individual level. RESULTS: We proposed a method to detect DE genes in individual disease samples by using the disrupted ordering in individual disease samples. In both simulated data and real paired cancer-normal sample data, this method showed excellent performance. It was found to be insensitive to experimental batch effects and data normalization. The landscape of stable gene pairs in a particular type of normal tissue could be predetermined using previously accumulated data, based on which dysregulated genes and pathways for any disease sample can be readily detected. The usefulness of the RankComp method in clinical settings was exemplified by the identification and application of prognostic markers for lung cancer. AVAILABILITY AND IMPLEMENTATION: RankComp is implemented in R script that is freely available from Supplementary Materials.
MOTIVATION: The differential expression analysis focusing on inter-group comparison can capture only differentially expressed genes (DE genes) at the population level, which may mask the heterogeneity of differential expression in individuals. Thus, to provide patient-specific information for personalized medicine, it is necessary to conduct differential expression analysis at the individual level. RESULTS: We proposed a method to detect DE genes in individual disease samples by using the disrupted ordering in individual disease samples. In both simulated data and real paired cancer-normal sample data, this method showed excellent performance. It was found to be insensitive to experimental batch effects and data normalization. The landscape of stable gene pairs in a particular type of normal tissue could be predetermined using previously accumulated data, based on which dysregulated genes and pathways for any disease sample can be readily detected. The usefulness of the RankComp method in clinical settings was exemplified by the identification and application of prognostic markers for lung cancer. AVAILABILITY AND IMPLEMENTATION: RankComp is implemented in R script that is freely available from Supplementary Materials.
Authors: Chao Liu; Sriganesh Srihari; Samir Lal; Benoît Gautier; Peter T Simpson; Kum Kum Khanna; Mark A Ragan; Kim-Anh Lê Cao Journal: Mol Oncol Date: 2015-09-26 Impact factor: 6.603