Lesson 0: Welcome and Introductions
By the end of this lesson, you should understand what to expect from the course and what is expected of you. We will also begin establishing a common vocabulary for discussing data visualizations. Our in-class hands-on component will focus on setting all students up with Github and practice homework submission procedures.
[Lecture][Code][Homework]
Lesson 1: Spatially Resolved Transcriptomics Data
By the end of this lesson, we should understand what is spatially resolved transcriptomics data, how the data is generated, and how we can begin visualizing and interacting with the data. Our in-class hands-on component will focus on beginning programming in R and using ggplot2.
[Lecture][Code][Homework]
Lesson 3: Principal Components Analysis
By the end of this lesson, we should understand what is principal components analysis and how to apply it to our spatial transcriptomic datasets. Our in-class hands-on component will involve analyzing our spatial transcriptomics dataset using principal components analysis to create a data visualization. We will also begin making multi-panel visualizations.
[Lecture][Code]
Lesson 4: T-distributed Stochastic Neighbor Embedding
By the end of this lesson, we should understand what is t-distributed stochastic neighbor embedding and how to apply it to our spatial transcriptomic datasets. Our in-class hands-on component will analyzing our spatial transcriptomics datasets to create a data visualization using tSNE.
[Lecture][Code][Homework]
Lesson 5: Kmeans Clustering
By the end of this lesson, we should understand what is kmeans clustering and how to apply it to our spatial transcriptomic datasets. Our in-class hands-on component will analyze our spatial transcriptomics datasets to create a data visualization using k-means clustering.
[Lecture][Code]
Lesson 6: Differential Gene Expression
By the end of this lesson, we should become more comfortable with interpreting our clusters identified from our analysis pipeline thus far using differential gene expression. We will also perform differential expression analysis, learn how to create different data visualizations to summarize our analysis, and use these visualizations and summaries to evaluate our analysis pipeline. Our in-class hands-on component will analyze our spatial transcriptomics dataset to identify differentially expressed genes and make data visualizations.
[Lecture][Code][Homework]
Lesson 8: RNA Velocity
By the end of this lesson, we should understand what is RNA velocity analysis. Our in-class hands-on component will involve using gganimate to animate our visualization to draw dynamic associations.
[Lecture][Code][Homework]
Lesson 9: Spatially variable genes
By the end of this lesson, we should be able to understand how to identify spatially variable genes in our data using Morans I. We will also be able to use interactive data visualizations to get some sense for the quality of our analysis. Our in-class hands-on component will involve calculating Morans I.
[Lecture][Code][Homework]
Lesson 10: Deconvolution
By the end of this lesson, we should better understand how multi-cellular pixel-resolution spatial transcriptomics data can be deconvolved and how the resulting deconvolved results can be visualized. Our in-class hands-on component will involve performing deconvolution using STdeconvolve and interpreting results.
[Lecture][Code][Homework]
Lesson 11: Spatial Proteomics
By the end of this lesson, we should be able to understand spatial proteomics technologies, how they can work. Our in-class hands-on component will involve analyzing a spatial proteomics dataset.
[Lecture][Code][Homework]
Lesson 12: Other Spatial Omics
By the end of this lesson, we should be able to understand other spatial omics technologies and how they can work. Our in-class hands-on component will involve analyzing a myster dataset together.
[Lecture][Code]