Leading the Way in Life Science Technologies

GEN Exclusives

More »


More »
March 01, 2012 (Vol. 32, No. 5)

Improving Gene-Expression Data Analysis

Statistical and Biological Review of Transcriptome Data Made Possible by Interactive Report

  • Visualization Tools and Workflows for Exploration and Interpretation of Results

    Click Image To Enlarge +
    Figure 2. An interactive volcano plot of 300 DEGs, with 10 DEGs associated with the biological process antiviral response highlighted in red.

    iReport removes data-analysis challenges by providing novel tools and workflows for visualizing, querying, and filtering the results. These tools help researchers perform downstream analysis based on biological criteria, interrogate their data from multiple angles, and quickly identify compelling genes for further study.

    The Summary Page in iReport features “Top Results by Experimental Keyword” and an interactive volcano plot (Figure 2) that enables the user to quickly identify expected, biologically relevant sets of gene-expression changes. Clicking on one of the top results (for example, “Antiviral Response”) lets the user drill down in the volcano plot to see which DEGs are associated with that process and takes the user directly to the experimentally demonstrated evidence that implicates those DEGs in that biological process.

    These features accomplish two critical goals of microarray data interpretation: 1) biologists can quickly find a set of DEGs that anchor on a relevant biological process or phenotype, and 2) biologists can clarify the strength of this association by accessing the literature evidence and experimental context substantiating that association in the Publications and Findings panel in each chapter of iReport.

    By reducing the effort required to find an expected, relevant result, iReport Summary Page enables researchers to move on to explore unexpected, potentially novel insights about their samples.

    To facilitate this exploration, we built The Wheel, which enables biologists to focus on individual or sets of DEGs based on their biological properties (such as molecular function, fold change, subcellular location, etc.) and known biological associations (pathways, diseases, cellular functions, etc.). The Wheel uses a biologist-friendly hierarchy of topics to quickly expand the investigation from a very specific result (e.g., antiviral response, 10 DEGs) to a broader topic within that same theme (e.g., regulation of immune response, 32 DEGs) (Figure 3).

  • Click Image To Enlarge +
    Figure 3. The Wheel, one of many organizational options in iReport, structures biological results into a hierarchy of topics, enabling users to quickly expand their view from a set of DEGs associated with a very specific result to a broader but related set of 32 DEGs involved in regulation of immune response. Here 32 DEGs involved in regulation of immune response are visually grouped by their molecular function, and sized by the number of molecular interactions with other DEGs.

    iReport was designed to enable researchers to find a compelling set of results and then follow that lead to understand whether a set of DEGs holds together in other biological contexts (pathways, interactions, etc). For example, iReport easily transitions this set of immune response genes into the molecular interactions chapter to understand whether, or how, these genes affect each other directly, either physically (e.g., protein interactions) or functionally (e.g., activation, inhibition, phosphorylation, etc.). This provides an opportunity to identify key regulatory points in the gene set.

  • Conclusions

    By integrating an automated statistical and quality control pipeline, biological knowledge from the Ingenuity Knowledge Base, and novel workflow and user interface tools to interpret that knowledge, Ingenuity iReport for gene expression data makes best practices in statistical and biological analysis of microarray data accessible to bench scientists.

    iReport identifies sets of gene-expression changes that are significantly different between samples, and maps those changes to cellular functions, phenotypes, pathways, and molecular interactions. This helps bench scientists find the biological stories that typically remain obscured when simply viewing lists of genes and expression changes, rapidly expand their knowledge of an experimental model, and identify a promising set of genes to interrogate with further experimentation.

Related content