Method designed to respect molecular heterogeneity can profoundly correct present data interpretations for genome-wide expression analysis

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Title:	Method designed to respect molecular heterogeneity can profoundly correct present data interpretations for genome-wide expression analysis
Authors:	蘇立仁;Chen, Chih-Hao;Hsu, Chueh-Lin;Huang, Shih-Hao;Chen, Shih-Yuan;Hung, Yi-Lin;Chen, Hsiao-Rong;Wu, Yu-Chung;Su, Li-Jen;Lee, H.C.
Contributors:	生醫理工學院生醫科學與工程學系
Keywords:	Analysis;Behavior disorders;Bioinformatics;Biological activity;Biological properties;Biology;Biomarkers;Bipolar disorder;Cell adhesion & migration;Data Interpretation, Statistical;Databases, Genetic;Datasets;Diagnostic software;Diagnostic systems;Gene expression;Gene Expression Regulation;Genetic Heterogeneity;Genome, Human;Genomes;Genomics;Heterogeneity;Humans;Medicine;Mental disorders;Methods;Molecular modelling;Neurobiology;Neurosciences;Parkinson's disease;Reproducibility;Reproducibility of Results;Robustness (mathematics);Schizophrenia;Statistical analysis;Statistical methods;Statistics;Studies;Surveys and Questionnaires;Thoracic surgery;Variance analysis
Date:	2015-03-20
Issue Date:	2026-04-23 11:17:05 (UTC+8)
Publisher:	Public Library of Science;United States: Public Library of Science
Abstract:	摘要： Although genome-wide expression analysis has become a routine tool for gaining insight into molecular mechanisms, extraction of information remains a major challenge. It has been unclear why standard statistical methods, such as the t-test and ANOVA, often lead to low levels of reproducibility, how likely applying fold-change cutoffs to enhance reproducibility is to miss key signals, and how adversely using such methods has affected data interpretations. We broadly examined expression data to investigate the reproducibility problem and discovered that molecular heterogeneity, a biological property of genetically different samples, has been improperly handled by the statistical methods. Here we give a mathematical description of the discovery and report the development of a statistical method, named HTA, for better handling molecular heterogeneity. We broadly demonstrate the improved sensitivity and specificity of HTA over the conventional methods and show that using fold-change cutoffs has lost much information. We illustrate the especial usefulness of HTA for heterogeneous diseases, by applying it to existing data sets of schizophrenia, bipolar disorder and Parkinson's disease, and show it can abundantly and reproducibly uncover disease signatures not previously detectable. Based on 156 biological data sets, we estimate that the methodological issue has affected over 96% of expression studies and that HTA can profoundly correct 86% of the affected data interpretations. The methodological advancement can better facilitate systems understandings of biological processes, render biological inferences that are more reliable than they have hitherto been and engender translational medical applications, such as identifying diagnostic biomarkers and drug prediction, which are more robust. 其他題名： PLoS One 出版者： United States: Public Library of Science 出版日期： 2015-03-20 出處： PloS one, 2015-03, Vol.10 (3), p.e0121154 資源來源： Agricultural & Environmental Science Collection 版權： COPYRIGHT 2015 Public Library of Science 版權： 2015 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 版權： 2015 Chen et al 2015 Chen et al 識別號： ISSN: 1932-6203 識別號： EISSN: 1932-6203 識別號： DOI: 10.1371/journal.pone.0121154 識別號： PMID: 25793610
Appears in Collections:	[Department of Biomedical Sciences and Engineering ] journal & Dissertation

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