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An organized curriculum for the study of the single-cell field

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Single Cell University

Single-cell is a rapid and exciting evolving field with numerous resources and classes/slides scattered online.

The idea of this repository is to act as a tentative curriculum that encompasses different areas where single-cell can serve as an important tool for scientific discovery. Serving as a quick-start guide to those starting.

The intention is not to collect every single tutorial in existence but aggregate the best resources that can give a comprehensive overview of the field.

PRs are extremely welcomed, together we can make a comprehensive and dynamic evolving curriculum for single-cell.

This repository would not exist if not for the awesome people that make their materials publicly available. I tried my best to give credits to authors of the tutorials/videos/slides, if you find anything incorrectly or would like to have it removed from this repository please let me know and it’ll be quickly fixed.

Some of the fields haven’t yet developed dedicated workshops/classes so they will be presented as tutorials of specific tools that are available.

This repository was largely inspired by awesome initiatives such as OSSU and Awesome Single-Cell, go check them out.

Curriculum

Biology

Programming

Topic Author References
R for Data Science Garrett Grolemund & Hadley Wickham
Ggplot2 Thomas Lin Pedersen

Single-cell history

scDNA

Mutation Calling
Copy Number

scRNA

scRNA is the most extensive documented area with a plethora of tutorials.

Reference Book
Link Authors Publication
Orchestrating Single-Cell Analysis Bioconductor Reference
Classes
Topic Author References
Pre-Processing
Normalization
Feature Selection
Dimensionality Reduction
Clustering
Cell Annotation
Pseudotime
RNA Velocity

Seurat

Topic Author References
QC - Dimensional Reduction - Clustering - DE Satija lab 1

Scanpy

Topic Author References
QC - Dimensional Reduction - Clustering - Marker genes Alex Wolf, Fidel Ramirez, Sergei Rybakov

scATAC

Integrating data

Reading

Topic Authors Publication
Integrative single-cell analysis Tim Stuart & Rahul Satija doi: 10.1038/s41576-019-0093-7

Flow Cytometry

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