VIBE: An R-package for advanced RNA-seq data exploration, disease stratification and therapeutic targeting
BackgroundDevelopment of therapies e.g. antibody-based treatments, rely on several factors, including the specificity of target expression and characterization of downstream signaling pathways. While existing tools for analyzing and visualizing RNA-seq data offer evaluation of individual gene-level expression, they lack a comprehensive assessment of pathway-guided analysis, relevant for single- and dual-targeting therapeutics. Here, we introduce VIBE (VIsualization of Bulk RNA Expression data), an R package which provides a thorough exploration of both individual and combined gene expression, supplemented by pathway-guided analyses. VIBEs versatility proves pivotal for disease stratification and therapeutic targeting in cancer, immune, metabolic, and other disorders. ResultsVIBE offers a wide array of functions that streamline the visualization and analysis of transcriptomics data for single- and dual-targeting therapies such as antibodies. Its intuitive interface allows users to evaluate the expression of target genes and their associated pathways across various indications, aiding in target and disease prioritization. Metadata, such as specific treatment or number of prior lines of therapy, can be easily incorporated to refine the identification of patient cohorts hypothesized to derive benefit from a given drug. Through real-world scenario representations using simulated data, we demonstrate how VIBE can be used to assist in indication selection for several user cases. VIBE integrates statistics in all graphics, enabling data-informed decision-making. Its enhanced user experience features include boxplot sorting and group genes either individually or averaged based on pathways, ensuring custom visuals for insightful decisions. For a deeper dive into its extensive functionalities, please review the vignettes on the GitHub repository (https://github.com/genmab/VIBE). ConclusionsVIBE facilitates detailed visualization of individual and cohort-level summaries such as concordant or discordant expression of two genes or pathways. Such analyses can help to prioritize disease indications that are amenable to treatment strategies like bispecific antibody therapies or pathway-guided monoclonal antibody therapies. By using this tool, researchers can enhance the indication selection and potentially accelerate the development of novel targeted therapies with the end goal of precision, personalization, and ensuring treatments align perfectly with individual patient needs across a spectrum of medical domains.