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Biol."],"published-print":{"date-parts":[[2018,3]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Gene co\u2010expression and differential co\u2010expression analysis has been increasingly used to study co\u2010functional and co\u2010regulatory biological mechanisms from large scale transcriptomics data sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we develop a nonparametric approach to identify hub genes and modules in a large co\u2010expression network with low computational and memory cost, namely MRHCA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co\u2010expression modules. With applying MRHCA and differential co\u2010expression analysis to <jats:italic>E. coli<\/jats:italic> and TCGA cancer data, we have identified significant condition specific activated genes in <jats:italic>E. coli<\/jats:italic> and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our analysis has demonstrated MRHCA can (i) deal with large association networks, (ii) rigorously assess statistical significance for hubs and module sizes, (iii) identify co\u2010expression modules with low associations, (iv) detect small and significant modules, and (v) allow genes to be present in more than one modules, compared with existing methods.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s40484-018-0131-z","type":"journal-article","created":{"date-parts":[[2018,2,13]],"date-time":"2018-02-13T06:11:02Z","timestamp":1518502262000},"page":"40-55","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MRHCA: a nonparametric statistics based method for hub and co\u2010expression module identification in large gene co\u2010expression network"],"prefix":"10.1002","volume":"6","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[{"name":"<!--1--> Colleges of Computer Science and Technology Jilin University Changchun 130012 China"}]},{"given":"Sha","family":"Cao","sequence":"additional","affiliation":[{"name":"<!--2--> Department of Biostatistics Indiana University School of Medicine Indianapolis IN 46202 USA"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"<!--3--> Center for Health Outcomes and Population Research Sanford Research Sioux Falls SD 57104 USA"}]},{"given":"Burair","family":"Alsaihati","sequence":"additional","affiliation":[{"name":"<!--4--> Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of Bioinformatics University of Georgia Athens GA 30602 USA"}]},{"given":"Qin","family":"Ma","sequence":"additional","affiliation":[{"name":"<!--5--> Bioinformatics and Mathematical Biosciences Lab Department of Agronomy, Horticulture, and Plant Science South Dakota State University Brookings SD 57007 USA"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"<!--6--> Center for Computational Biology and Bioinformatics and Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis IN 46202 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