Comparing single-cell integration methods - which one should you use?

This is part 2 of my series on single-cell RNAseq integration methods! In this video, we’ll compare single-cell RNAseq integration methods, exploring their performance on different integration tasks. You'll also get some tips on how to choose the best integration method for your data as well as a practical example. This video is mostly based on the results from Luecken et al, 2022 (https://www.nature.com/articles/s4159..., full citation below). Let's dive in! You can also find the blogpost at biostatsquid.com: https://biostatsquid.com/ 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:) -------------------------------------------------------------------------------------------------------------------- For more biostatistics tools and resources, you can visit: https://biostatsquid.com/ 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! Or follow me on Instagram at @biostatsquid:   / biostatsquid   Don’t forget to subscribe if you don’t want to miss another video from me! -------------------------------------------------------------------------------------------------------------------- Luecken, M.D., Büttner, M., Chaichoompu, K. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat Methods 19, 41–50 (2022). https://doi.org/10.1038/s41592-021-01...