Welcome
Welcome to the Course Website for EN.580.428 Genomic Data Visualization!
As the primary mode through which analysts and audience members alike consume data, data visualization remains an important hypothesis generating and analytical technique in data-driven research to facilitate new discoveries. However, if done poorly, data visualization can also mislead, bias, and slow down progress. This hands-on course will cover the principles of perception and cognition relevant for data visualization and apply these principles to genomic data, including large-scale single-cell and spatially-resolved omics datasets, using the R statistical programming language. Students will be expected to complete class readings, create weekly data visualizations as homework assignments, and make a major class presentation.
Course Information
Course Staff: Prof. Jean Fan and Caleb Hallinan
Lectures: 8:00am-9:50am Monday, Wednesday, and Friday. See Canvas for location details.
Office Hours: 10:00am-10:50am Monday, Wednesday, and by request. See Canvas for location details.
Course Details
☞ see Course tabFeatured Visualizations
Interrogating Spatial Spot Cluster Differential Gene Expression with 10x Visium
In these panels, I am depicting the representation of a 10x visium dataset in latent tSNE-embedded space and over the original spatial slide coordinates. I...
Identifying a Cluster of Breast Granular Cells
In the top left of my figure, I am depicting both my clusters made by kmeans clustering with k=7 in PCA space (with my cluster...
Homework 3: Differentially Expressed Genes analysis
[description] Those panels present a comprehensive visualization of Cluster 0 and its association with the gene SFRP4 through a combination of UMAP, spatial, and gene...
Spatial Transcriptomics Reveals a Distinct Epithelial Cell Population Defined by ELF3 Expression: A Multi-Dimensional Analysis of the Cluster in Interest
1. Describe your figure briefly so we know what you are depicting. Write a description to convince me that your cluster interpretation is correct.
Homework 2 submission
[description] In my visualization, I use points as the geometric primitive, angle and color for visual channel. The x-axis represents the PCA loadings for each...
Comparison of Scaled and Unscaled PCA: Gene Mean Expression, Variance, and PC1 Loadings
1. What data types are you visualizing? I am visualizing quantitative data, which includes log-transformed mean expression (x-axis), log-transformed variance (y-axis), and PC1 loading values...
Dimensionality Reduction using PCA
Homework 2
HW1: Gene expression pattern for GNB1 and HES4
1. What data types are you visualizing? I am visualizing HES4 and GNB1’s spatial gene expression patterns for eevee.
10 Genes with the Highest Counts are Expressed Across Most Spots
1. What data types are you visualizing? For this data visualization of the Eevee spatial transcriptomic data, I visualized both categorical data, the 10 genes...
Generation of Heatmap Expressing Top 20 Genes Within Pikachu Dataset
1. What data types are you visualizing? Within the Pikachu dataset that was visualized, gene expression levels across multiple individual cells proved to be a...
HW1: gene expression scatterplot
1. What data types are you visualizing? Spatial data of each cell, i.e the location of the cell within the section of the image, which...
All Visualizations
PCA Dimensionality Reduction vs Physical Space
Write a description explaining why you believe your data visualization is effective using vocabulary terms from Lesson 1
Visualization of Cellular PCA Space and Gene Feature Correlations
1. What data types are you visualizing? This data visualization is trying to show insights into the spatial transcriptomics dataset using two panels that focus on distinct yet complementary aspects...
Associations between cell localization and PC1 and 2 expression
Description I have chosen to focus on how cells relate in the gene expression vs. physical space. I analyzed this by creating two visualizations- one which looks at the x...
Using PCA to continue visualizing tumor cells
Just for future reference, this is how I will address each graph in my data visualization: Graph 1 the one in the top left, Graph 2 is top right, Graph...
X and Y position correlations with PCs in the Eevee dataset
I believe that my data visualization is effective because the 3 panels connect by both visualizing the positional information of the data in gene expression space and quantifying the correlations,...
Relationship Between Gene Expression in Cells vs. their Physical Position
1. What data types are you visualizing? I am visualizing quantitative data of the X and Y position of the cells. I am also visualizing quantitative data of the gene...
Determining the relationship between gene expression and physical spaces
1. Write a description explaining why you believe your data visualization is effective using vocabulary terms from Lesson 1.
Relationship between gene features and PC1 loading
1. What data types are you visualizing? I am visualizing the quantitative data of PC1 loading from each gene, the quantitative data of variance of gene expressions of each gene,...
Impact of Gene Expression Mean and Variance on PCA Loadings: Scaled vs. Unscaled Data
1. What data types are you visualizing? I am visualizing quantitative data for gene expression statistics. Specifically, I compare gene mean expression and variance (log-transformed) against PC1 loading values from...
