Format

Send to

Choose Destination
Bioinformatics. 2018 Oct 10. doi: 10.1093/bioinformatics/bty825. [Epub ahead of print]

MetaOmics: Analysis Pipeline and Browser-based Software Suite for Transcriptomic Meta-Analysis.

Author information

1
Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD, USA.
2
Department of Biostatistics, University of Florida, Gainesville, FL, USA.
3
Departments 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
Departments 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, OH, USA.
12
Department of Pharmacology & 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.

Key words:

gene expression, meta-analysis, omics data integration, Graphical User Interface (GUI), R Shiny.

Availability:

MetaOmics is freely available at https://github.com/metaOmics/metaOmics.

Supplementary information:

Supplementary data are available at Bioinformatics online.

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center