Filming space-time changes of gene expression with expressyouRcell

Abstract

The last decade has witnessed massive advancements in high-throughput techniques capable of producing quantifications of transcript and protein levels across time and space, and at high resolution. Yet, the large volume of big data available and the complexity of experimental designs hamper an easy understanding and effective communication of the results. Here we present expressyouRcell, a unique and easy-to-use R package to map the multi-dimensional variations of transcript and protein levels in cell-pictographs. These variations are outcomes of differential and gene set enrichment analysis across space and time. Our tool directly associates these results with up to twenty specific cellular compartments, visualising them as pictographic representations of four different cellular thematic maps. expressyouRcell visually reduces the complexity of displaying gene expression and protein level changes across multiple time-points by generating dynamic representations of cellular pictographs. We applied expressyouRcell to six datasets, demonstrating its flexibility and usability in the visualization of simple and highly effective static and dynamic representations of time course variations in gene expression. Our approach complements classical plot-based methods for visualization and exploitation of biological data, improving the standard quantitative interpretation and communication of relevant results.