Single-cell and spatial transcriptomics data analysis with Seurat in R
Materials:
The tutorial can be downloaded in both [html] and [Rmd] formats.
Topics:
A thorough walk-through is provided to perform computation and data analysis on single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics data using Seurat and other packages in R. Other topics include the explanation of a general Seurat object, and the conversion of sequencing data formats between R and Python.
Acknowledgements:
The tutorial was inspired by the computational assignments I created together with Dr. Yun S. Song in a graduate course (CMPBIO 290: Algorithms for single-cell genomics) at University of California, Berkeley in Fall 2021. The tutorial material was largely based on many open-source resources, especially the Seurat tutorials from the Satija Lab. I would also like to thank Salwan Butrus for helpful feedback and suggestions.
Yutong Wang (王雨桐)
Postdoctoral Researcher
I am a postdoctoral scholar with Dr. Jimmie Ye at University of California, San Francisco. I recently completed my PhD in Biostatistics at University of California, Berkeley, working with Dr. Yun S. Song. My research is primarily driven by developing and applying machine learning and statistical methods to understand biology. Recently, I am studying how genes interact with each other in the multi-cellular organisms.