MetaOmics: analysis pipeline and browser-based software suite for transcriptomic meta-analysis

Abstract

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 coexpression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic metaanalytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions.

Comparative Pathway Integrator: a framework of meta-analytic integration of multiple transcriptomic studies for consensual and differential pathway analysis

“…CPI is implemented in R, as well as R Shiny, an R based graphical user interface. The R Shiny version is disseminated in MetaOmics can be easily handled by users without programming knowledge [16].…”

“…Last, CPI visualize the findings and provide users both text and graphical outputs for intuitive while statistical solid presentation and easy interpretation. An R GUI package CPI has been disseminated into MetaOmics, an analysis pipeline and browser-based software suite for transcriptomic meta-analysis [16]. Figure 1: The work flow of CPI CPI is a comprehensive tool incorporating several widely accepted mature methods as well as some novel algorithms/approaches.…”

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

“…Although the steps of data normalization and integration are not a part of MOG, because the data that is input to MOG is typically from aggregate studies, we present several considerations. Two predominate frameworks for analysis of multiple expression studies are: 1) analyze each study independently and then combine results from independent studies (metaanalysis) (5,9,10,27,(40)(41)(42)(43), or 2) combine data from multiple studies together to create a "pooled" dataset and analyze the pooled data (5,26,27). In meta-analysis, if individual studies show statistically-significant results, then it is likely that the final result combined from the studies will also be significant (5).…”

“…A common goal of analyzing omics data is to infer functional roles of particular features by investigating differential expression and coexpression patterns. A wide variety of R-platform tools can provide specific analyses (9)(10)(11)(12)(13)(14)(15)(16). Such tools are based upon rigorous statistical frameworks and produce accurate results when the model assumptions hold.…”

“…Such tools are based upon rigorous statistical frameworks and produce accurate results when the model assumptions hold. Some tools avoid the need to code by providing "shiny" interfaces (17) to various subsets of R's functionalities (10,(18)(19)(20). They can only apply selected R packages to the data, and have the general limitations that they are not well suited for very large datasets, and have very limited interactivity.…”