Скачать с ютуб How to interpret a heatmap for differential gene expression analysis - simply explained! в хорошем качестве

How to interpret a heatmap for differential gene expression analysis - simply explained!

In this video, I will focus on how to interpret a heatmap for differential gene expression analysis. Learn why heatmaps are a great visualisation tool for our DEG or gene set enrichment analysis results, why clustered heatmaps help us find patterns in our genes and/or samples and how to interpret a heatmap from a real-life published example! We will go through heatmaps from the very basics and then apply the main points to interpret a heatmap from a scientific paper. 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: https://biostatsquid.com/ Follow me on Instagram at @biostatsquid:   / 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 heatmaps: Some additional slides and tips when creating a heatmap: https://acikders.ankara.edu.tr/plugin... Don't want to code? You can easily create your publication-ready heatmaps with this RShiny App: http://shinyheatmap.com/ or if you want to program your way through it, I recommend the pheatmap packages (planning on a tutorial soon, but Dave Tang has an amazing step by step guide!): https://davetang.org/muse/2018/05/15/... https://bioconductor.org/packages/rel...