Tianzhou Ma1, Zhiguang Huo2, Anche Kuo3, Li Zhu3, Zhou Fang3, Xiangrui Zeng4, Chien-Wei Lin5, Silvia Liu6, Lin Wang7, Peng Liu3, Tanbin Rahman3, Lun-Ching Chang8, Sunghwan Kim9, Jia Li10, Yongseok Park3, Chi Song11, Steffi Oesterreich12, Etienne Sibille13, George C Tseng3. 1. Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD, USA. 2. Department of Biostatistics, University of Florida, Gainesville, FL, USA. 3. Department of Biostatistics, Graduate School of Public Health University of Pittsburgh, Pittsburgh, PA, USA. 4. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. 5. Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA. 6. Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA. 7. School of Statistics, Capital University of Economics and Business, China. 8. Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA. 9. Department of Statistics, Keimyung University, Korea. 10. Henry Ford Health System, USA. 11. Division of Biostatistics, Ohio State University, Columbus, OH, USA. 12. Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA. 13. Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada.
Abstract
SUMMARY: The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions. AVAILABILITY AND IMPLEMENTATION: MetaOmics is freely available at https://github.com/metaOmics/metaOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions. AVAILABILITY AND IMPLEMENTATION: MetaOmics is freely available at https://github.com/metaOmics/metaOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Funmilayo L Makinde; Milaine S S Tchamga; James Jafali; Segun Fatumo; Emile R Chimusa; Nicola Mulder; Gaston K Mazandu Journal: Brief Bioinform Date: 2021-11-05 Impact factor: 13.994
Authors: Xiangrui Zeng; Wei Zong; Chien-Wei Lin; Zhou Fang; Tianzhou Ma; David A Lewis; John F Enwright; George C Tseng Journal: Genes (Basel) Date: 2020-06-24 Impact factor: 4.096