Workshop Description

Spatial transcriptomics is an emerging field that bridges molecular biology and anatomy. Over the last decade, a battery of assays have been developed that profile gene expression in-situ, i.e, measuring the abundances of mRNA molecules in cells and tissues while retaining information about their spatial locations. This workshop will introduce the basic concepts, major techniques, and typical analysis workflows in spatial transcriptomics. It will also offer guided hands-on coding exercises for the analysis of public spatial transcriptomic datasets. 
Given the field’s rapid development, this workshop will try not to position itself as an authoritative guide of the subject. Rather, it will serve as a means of encouragement and support. It aims to help people jump into the field, plan experiments, play with data analysis, and contemplate about the future potential of this powerful technology.

Workshop Materials

Day 1
  • 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.

Day 2 
  • Exploratory data visualization and analysis.
  • Dimensionality reduction and clustering.
  • Spatial neighborhood analysis and enrichment test.
  • Canned analysis tool: Squidpy.
  • Discussion: opportunities and challenges.

Technical Requirements

The workshop does not require any prior knowledge, but participants are assumed to feel comfortable programming using Python for basic data science tasks, or to feel comfortable learning by extensive exploration in class.
It is highly recommended, though not required, that students bring their own laptops and have a working python environment pre-installed, including the following packages:
It is also encouraged that students explore the following public spatial transcriptomics datasets:
  • The Vizgen MERscopy mouse brain dataset:
  • The 10X Visium mouse brain dataset:

Instructor

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.
Email: fmxie@ucla.edu

Videos

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Workshop Details

Prerequisites: Intro to Python (W9) is highly recommended.
Length: 2 days, 3 hrs per day
Level: Introductory
Location: Boyer 529
Seats Available: 28

Fall 2022 Dates

Nov. 22, and 23
1:30 PM – 4:30 PM

REGISTRATION IS OPEN!