Motivations and milestones: where spatial transcriptomics emerge.
Major technologies: sequencing-based vs In-situ imaging based assays.
Seminal studies: how spatial transcriptomes addressed biological questions.
Data analysis: overview of data-processing workflow and hands-on data exploration using two public datasets from the mouse brain.
Exploratory data visualization and analysis.
Dimensionality reduction and clustering.
Spatial neighborhood analysis and enrichment test.
Canned analysis tool: Squidpy.
Discussion: opportunities and challenges.
Python, jupyter notebook, and other common python packages related to data analysis and machine learning. All of these can be done with one installation through Anaconda. https://www.anaconda.com/products/individual
The Squidpy package (requires Python version 3.8). Follow installation instruction (*this package is under active development, and it might be nontrivial to install it): https://squidpy.readthedocs.io/en/stable/installation.html
The Vizgen MERscopy mouse brain dataset:
The 10X Visium mouse brain dataset:
Dr. Fangming Xie is a postdoctoral fellow from Wollman Lab at UCLA. Before joining UCLA, Fangming developed computational methods integrating and analyzing single-cell transcriptomic and epigenetic data to catalog mamallian brain cell types at Dr. Eran Mukamel’s lab at UCSD. His research interests include developing scalable spatial transcriptomics technologies and analyses to investigate the molecular architecture of the brain. He received his Ph.D. in biophysics and a B.S. degree in physics.
Prerequisites: Intro to Python (W9) is highly recommended.
Length: 2 days, 3 hrs per day
Location: Boyer 529
Seats Available: 28
Nov. 22, and 23
1:30 PM – 4:30 PM
REGISTRATION IS OPEN!