Lesson 2: Spatially Resolved Transcriptomic Data
Table of ContentsLecture
2.0 Lesson learning objectives
By the end of this lesson, we should understand what is spatially resolved transcriptomic data, how the data is generated, and how we can begin visualizing and interacting with the data.
2.1 Why spatially resolved transcriptomics?
2.2 Low-throughput approaches for spatially resolved transcriptomic profiling
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smFISH
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microdissection
2.3 High-throughput approaches for spatially resolved transcriptomic profiling
- Spatially resolved highly multiplexed transcriptomic imaging
MERFISH is an imaging method capable of simultaneously measuring the copy number and spatial distribution of hundreds to thousands of RNA species in fixed cells. This technique was introduced in the paper Chen et al. Spatially resolved, highly multiplexed RNA profiling in single cells. Science (2015)
- Spatially resolved transcriptomic capture and sequencing
Visium is a commercial platform that uses glass slides with arrayed oligonucleotides containing positional barcodes to generate cDNA libraries with positional information for RNA sequencing. The technique was introduced in the paper Stahl et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science (2016) and later licensed to 10X Genomics for commercialization.
Hands-on Component
Our in-class hands-on component will involve forming teams, downloading, and beginning to analyze spatial transcriptomics data.
Class Notes
Prof. Fan’s notes from class: genomic-data-visualization-Lesson_2.pptx (click to download)
Prof. Fan’s code from class: code-lesson-2.R (click to download)
Homework Assignment
HW_1
genomic-data-visualization-HW_1.pptx (click to download)
Make a new data visualization of your spatial transcriptomics dataset using ggplot (do not make the same visualizations that were made in class). Write a description of what you did using vocabulary terms from Lesson 1. See slides for more details on what is expected and how to submit your homework.
Make a pull request to submit your homework.