How to interpret GSEA results and plot - simple explanation of ES, NES, leading edge and more!

In this video, I will focus on how to interpret the results from Gene Set Enrichment Analysis (GSEA) and to interpret the plots. Learn what are the main statistics given by GSEA and how to use them to make the most of your pathway enrichment analysis results, including how to interpret the Enrichment Score (ES), Normalised Enrichment Score (NES), p-values, FDR... We will go through basic GSEA terms like the ranking metric, the leading edge subset and more! Hope you like it! -------------------------------------------------------------------------------------------------------------------- Watched it already? If you liked this video or found it useful, please let me know! Your comments and feedback are very much appreciated😊 If you have questions, don’t hesitate to leave me a comment down below, I will answer as soon as I can:) -------------------------------------------------------------------------------------------------------------------- Are you into biostatistics and computational analysis? For more biostatistics tools and resources, you can visit: Follow me on Instagram at @biostatsquid: For more • simple and clear explanations of biostatistics methods • computational biology tools • easy step-by-step tutorials in R and Python to analyse and visualise your biological data! Don’t forget to subscribe if you don’t want to miss another video from me! -------------------------------------------------------------------------------------------------------------------- Other interesting resources for GSEA: Original publication: #sec-2 You can conduct your own Gene Set Enrichment Analysis with GSEA Software: or if you want to program your way through it, I recommend the fgsea or clusterProfiler packages: