Differential gene expression (DGE) analysis is a technique to identify statistically significant differences in RNA abundance for genes or arbitrary features between different biological states. The result of a DGE test is typically further analyzed using statistical software, spreadsheets or custom ad hoc algorithms. Researchers from the RIKEN Yokohama Institute identified a need for a web-based system to share DGE statistical test results, and locate and identify genes in DGE statistical test results with a very low barrier of entry.
The researchers have developed DEIVA, a free and open source, browser-based single page application (SPA) with a strong emphasis on being user friendly that enables locating and identifying single or multiple genes in an immediate, interactive, and intuitive manner. By design, DEIVA scales with very large numbers of users and datasets.
a Data set selector, symbol locator, and highlight filters. b The density plot on a field of log2 FC vs log10 baseMean for a DGE statistical test result. Symbols selected in the symbol locator (shown in (a)) are shown as points with matching colors. In this example comparing samples highly enriched for RNA attached to ribosomes of Purkinje neurons (positive fold change) with samples of unspecific RNA in the same brain region (negative fold change). Locating a set of already known markers for Purkinje neurons immediately confirms that the markers are specifically enriched. Hexagonal bins are colored red based on the fraction of features within that region that pass the cut-off filters; currently set at a log10 FDR ≤ −1, at any fold-change. c Sortable table of expression values for the region selected in the density plot (shown in (b)). Twelve highly overrepresented genes are selected (grey rectangle) in the plot and their information is reflected in this table
Compared to existing software, DEIVA offers a unique combination of design decisions that enable inspection and analysis of DGE statistical test results with an emphasis on ease of use.
Availability – Project home page: https://github.com/Hypercubed/DEIVA