Automated, Reproducible Investigation of gene set Differential Enrichment via the AUTO-go framework

Reproducibility in Life Sciences is challenged in the analysis of large multi-omics datasets. One of the final steps of said processes is Gene Set enrichment, where web tools represent a valuable resource but not a reliable surrogate for standardized, high-quality visualizations. The AUTO-go framework proposes standardization of the Gene Functional Enrichment process along with an R framework able to produce high-quality visualization in an automated manner, improving the reproducibility of the whole analytical process. We present three use cases in Cancer Transcriptomics and Epigenomics datasets as a proof-of-concept to visualize Multiple Differential Expression and Single Sample Gene Set Enrichment Analysis. Author SummaryBioinformatics and Data Science are routinely challenged to distill intelligible results from huge amounts of data. These results, in turn, are conveyed through plots and visualizations that should be easily reproducible for scientific soundness and ethical reasons. A specific area in which these analyses are of critical importance is Genomics, where Genes functions need to be enriched when comparing pathological states or treatments. Here we present a software framework that aims at standardizing said differential analyses and visualizations when dealing with genomics data. Finally, we show how it can be employed to shear light on publicly available datasets, even in small casuistry of Rare Cancers.