Single-cell and spatial transcriptomics data analysis with Seurat in R


Date
Nov 10, 2021 12:00 PM — 1:00 PM
Location
177 Stanley

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)
Yutong Wang (王雨桐)
PhD candidate in Biostatistics

I am a PhD candidate in the Graduate Group in Biostatistics at University of California, Berkeley, advised by Prof. Yun S. Song. Committed to advancing our understanding of biology through rigorous computational and statistical frameworks, I specialize in distilling insights from high-dimensional and noisy biomedical data. My methodological focus encompasses statistical machine learning and deep learning techniques, which I apply to a wide array of biological questions spanning cell biology, cancer immunology, and neuroscience. I am particularly captivated by the untapped potential of emerging technologies such as spatial transcriptomics and CRISPR-screening, which I believe are key to unraveling the complex molecular landscapes underlying tumor microenvironments and cellular interactions.