Important Note: This repository is still under development and the information contained within it may change. If you have any feedback or suggestions, feel free to open an issue or reach out to us.
This repository contains a collection of bioinformatics data analysis notebooks focused on providing examples of current best practices in single-cell analysis. The notebooks include both data and code.
This repository is intended for anyone who wants to:
- Apply current best practices in single-cell analysis using real data.
- Generate comprehensive reports to share with biological collaborators.
- Find code snippets to quickly produce results and figures.
This repository is NOT an introduction to data analysis. There are many available courses and resources, such as Bioinformatics Training at the Harvard Chan Bioinformatics Core and Single-cell best practices from Theis lab, that provide such introductions. The focus of this repository is on the practical aspects of analysis and assumes that users already have a basic understanding of:
- Programming in R, Python, and Unix.
- R markdown.
- Assays such as RNA-seq, single-cell RNA-seq, and Spatial transcriptomics.
- Single-cell RNA-seq general preprocessing workflow:
/scRNAseq_preprocess_main - Analysis of immune system data with scRNA-seq:
/scRNAseq_immune_branch - Code for generating publication-ready figures:
/figure_code
If you have any questions or encounter any problems, please don't hesitate to reach out by creating an issue in this repository or contacting Cankun Wang at cankun.wang@osumc.edu.
Maintainer: Cankun Wang
Contributors: