How to perform PCA on single-cell RNA-Seq data in three simple steps) (7/6/2020)
A step-by-step guide for a simple yet very effective way of performing PCA on scRNA-Seq data. (30 min)
Tutorial: Installing Monet using Miniconda (on Linux) (6/22/2020)
A brief tutorial on how to install Monet using Miniconda on Linux. (5:29 min)
NYU School of Medicine ABL Single-cell analysis club
These are a series of talks that I was invited to give at the Single-cell Analysis Club, a monthly forum hosted by the Applied Bioinformatics Laboratories (ABL) at NYU School of Medicine. These talks took place in an informal setting, and I’m thankful for the great questions that I was being asked during different parts of each talk. I would also like to thank Itai Yanai for the opportunity to speak at these meetings.
Denoising single-cell RNA-Seq data (6/11/2019, ~58 min)
In this talk, I give an introduction into how single-cell RNA-Seq measurements are affected by noise, and provide a very brief overview of the different denoising methods that have been proposed. I also try to clear up common misconceptions about the characteristics of noise in scRNA-Seq data, for example regarding “dropouts”. Finally, I discuss our recently developed ENHANCE algorithm for denoising scRNA-Seq data. Thank you to Dalia Barkley for providing an R demonstration of our ENHANCE algorithm.
Visualizing single-cell RNA-Seq data (3/11/2019, ~62 min)
In this talk, I give an introduction to the two most popular visualization techniques for single-cell RNA-Seq data, t-SNE and UMAP. I try to answer some of the most commonly asked questions regarding these methods, using examples from real PBMC data. In the first part of the talk, I discuss t-SNE. Then, Dr. Pratip Chattopadhyay discusses the use of t-SNE for flow cytometry data (~38:00-55:00). Afterwards, I continue with a discussion of UMAP. Unfortunately, a minute or so from the beginning of my talk is missing, but the video still captures my entire discussion of t-SNE.
Clustering single-cell RNA-Seq data (2/4/2019, ~38 min)
In this talk, I show some examples of applying standard clustering algorithms, specifically hierarchical clustering and k-means, to single-cell RNA-Seq data. I also show that single-cell RNA-Seq data can be effectively clustered using only a few simple steps. This is the second half of a session on clustering single-cell RNA-Seq data (the first half was presented by Itai Yanai).