title: Method designed to respect molecular heterogeneity can profoundly correct present data interpretations for genome-wide expression analysis.
abstract: 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.
authors: Institute of Systems Biology and Bioinformatics, National Central University, Chungli, Taiwan 32001; Cathay Medical Research Institute, Cathay General Hospital, Taipei, Taiwan 10630.
authors: Chih-Hao
authors: CH
authors: Chen
authors: Institute of Systems Biology and Bioinformatics, National Central University, Chungli, Taiwan 32001.
authors: Chueh-Lin
authors: CL
authors: Hsu
authors: Institute of Systems Biology and Bioinformatics, National Central University, Chungli, Taiwan 32001.
authors: Shih-Hao
authors: SH
authors: Huang
authors: Institute of Systems Biology and Bioinformatics, National Central University, Chungli, Taiwan 32001.
authors: Shih-Yuan
authors: SY
authors: Chen
authors: Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan 30013.
authors: Yi-Lin
authors: YL
authors: Hung
authors: Department of Medicine, Boston University School of Medicine, Boston, MA 02118, United States of America.
authors: Hsiao-Rong
authors: HR
authors: Chen
authors: Division of Thoracic Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan 11217; School of Medicine, National Yang-Ming University, Taipei, Taiwan 11221.
authors: Yu-Chung
authors: YC
authors: Wu
authors: Institute of Systems Biology and Bioinformatics, National Central University, Chungli, Taiwan 32001.
authors: Li-Jen
authors: LJ
authors: Su
authors: Institute of Systems Biology and Bioinformatics, National Central University, Chungli, Taiwan 32001; National Center for Theoretical Sciences, Hsinchu, Taiwan 30043; Department of Physics, National Central University, Chungli, Taiwan 32001; Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli, Taiwan 32001.
authors: H C
authors: HC
authors: Lee
mesh_terms: D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D018740:Genetic Heterogeneity; D015894:Genome, Human; D006801:Humans; D015203:Reproducibility of Results; D011795:Surveys and Questionnaires