Single cell is a challenging technique to work on. It requires a ton of expertise in both computational savy, as well as an understanding of some high level statistics. As such, keeping a list of valuable resources which I’ve found useful is a huge boon to all. Here are some readings that I’ve used to familiarize myself with different packages/methods/ideas implemented consistenly. If you have something you think is great feel free to contact me on twitter at @Pabster212
https://towardsdatascience.com/understanding-singular-value-decomposition-and-its-application-in-data-science-388a54be95d https://towardsdatascience.com/understanding-singular-value-decomposition-and-its-application-in-data-science-388a54be95d https://towardsdatascience.com/svd-8c2f72e264f https://gregorygundersen.com/blog/2018/12/10/svd/
Basics: https://pair-code.github.io/understanding-umap/ Deeper Dive (Worth the read): https://pair-code.github.io/understanding-umap/supplement.html Umap vs Tsne (There is only one right answer): https://towardsdatascience.com/tsne-vs-umap-global-structure-4d8045acba17
TFIID: http://andrewjohnhill.com/blog/2019/05/06/dimensionality-reduction-for-scatac-data/