Abstract
BackgroundAnalysis of large-scale omics data has become more and more challenging due to high dimensionality. More complex analysis methods and tools are required to handle such data. While many methods already exist, those methods often produce different results. To help users obtain more appropriate results (i.e. candidate genes), we propose a tool, GRACOMICS that compares numerous analysis results visually in a more systematic way; this enables the users to easily interpret the results more comfortably.ResultsGRACOMICS has the ability to visualize multiple analysis results interactively. We developed GRACOMICS to provide instantaneous results (plots and tables), corresponding to user-defined threshold values, since there are yet no other up-to-date omics data visualization tools that provide such features. In our analysis, we successfully employed two types of omics data: transcriptomic data (microarray and RNA-seq data) and genomic data (SNP chip and NGS data).ConclusionsGRACOMICS is a graphical user interface (GUI)-based program written in Java for cross-platform computing environments, and can be applied to compare analysis results for any type of large-scale omics data. This tool can be useful for biologists to identify genes commonly found by intersected statistical methods, for further experimental validation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1461-0) contains supplementary material, which is available to authorized users.
Highlights
Analysis of large-scale omics data has become more and more challenging due to high dimensionality
Variation of color intensities are used to represent the degree of significance, with more significant markers colored more intensely
A red color represents the significant genes identified by t-test only, a blue color signifies those identified by Wilcoxon rank-sum test only, and purple color indicates those identified by both tests
Summary
Analysis of large-scale omics data has become more and more challenging due to high dimensionality. To help users obtain more appropriate results (i.e. candidate genes), we propose a tool, GRACOMICS that compares numerous analysis results visually in a more systematic way; this enables the users to interpret the results more comfortably. Success in microarray data studies has led to an expansion of large-scale omics data analyses and their data types. In unison with technological advances, many statistical tools were developed for separate types of omics data analyses. We will illustrate the application of our tool for different omics data types. Many microarrays studies aim to detect “gene expression signatures” specific to various human diseases by comparing expression levels between two distinct groups.
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