Discovering monotonic stemness marker genes from time-series stem cell microarray data

BMC Genomics

Proceedings

George C Tseng¹  Chin-Teng Lin²  Shing-Jyh Chang³  Nikhil Ranjan Pal⁴  Hsing-Jen Sun⁵  I-Fang Chung⁶  Hsei-Wei Wang⁷  Ting-Yu Chang⁸  Wei-Chung Cheng⁹  Hung-Hao Lo^1,10 

[1] Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA;Department of Electrical Engineering, National Chiao-Tung University, Hsinchu, Taiwan;Brain Research Center, National Chiao-Tung University, Hsinchu, Taiwan;Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsinchu, Taiwan;Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta, India;Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan;Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan;Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan;Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan;Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan;Institute of Microbiology and Immunology, National Yang-Ming University, Taipei, Taiwan;VGH-Yang-Ming Genome Research Center, National Yang-Ming University, Taipei, Taiwan;Institute of Microbiology and Immunology, National Yang-Ming University, Taipei, Taiwan;Research Center for Tumor Medical Science, China Medical University, Taichung, Taiwan;The Molecular Biology Institute, University of California Los Angeles, LA, CA, USA;

关键词: Gene List;    Human Embryonic Stem Cell;    Embryoid Body;    Stem Cell Differentiation;    Monotonic Trend;   

DOI  :  10.1186/1471-2164-16-S2-S2

来源: Springer

【 摘 要 】

BackgroundIdentification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics.ResultsWe have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.ConclusionsWe have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/.

【 授权许可】

Unknown   
© Wang et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.