{tidytof}: Predicting Patient Outcomes from Single-cell Data using Tidy Data Principles
Channel: R Consortium
Date: 31/08/2021

Timothy Keyes, MD, Ph.D. Candidate, at Stanford University, presents on the {tidytof}, an R Package to predict patient outcomes from single-cell data using Tidy data principles. Keyes gives some background information on how single-cell databases can be analyzed for flexibility and reproducibility. Keyes then digs into {tidytof}, a grammar for single-cell data analysis, and then follows with some lessons learned from building tidy tools to answer clinical questions with single-cell data.  

Originally presented at R/Medicine 2021 by Timothy Keyes
https://r-medicine.org

Main Sections

0:00 Introduction
0:40 Background 
1:35 Single-cell databases exist on multiple simultaneous levels of analysis 
3:05 The problem: how do we move seamlessly between these levels of analysis? 
3:43 The solution: Tidy data! 
4:55 {tidytof}: A grammar for single-cell data analysis
6:21 The eye-in-the-sky picture: verbs acts on data
7:53 The world's fastest PhD with {tidytof}
9:11 The world's fastest PhD with {tidytof} - clustering 
12:09 The world's fastest PhD with {tidytof} - modeling 
14:15 Lessons learned: Why building tidy tools can help you to answer clinical questions with single-cell datasets 
18:06 Thank you! 
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