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
Comparing PCA and t-SNE Dimensionality Reduction on Spatial Transcriptomics Dataset
In many tissues, cells with similar gene expression patterns tend to cluster together both in a dimensionality-reduced “gene expression space” (like the PCA or t-SNE plots) and in their actual...
HW2: Exploring PC1 Loading Vs. Gene Expression Variance Before and After Normalization
1. How do the gene loadings on the first PC relate to features of the genes such as its variance? Using the raw data, when the gene expression variance is...
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 gene, while the y-axis shows...
Making a Multi-Panel Data Visualization
The visualization effectively conveys relationships between gene expression and spatial organization by utilizing dimensionality reduction (PCA) to simplify high-dimensional gene expression data. The PCA scatter plot helps distinguish patterns in...
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 (color hue).
PC1 values (unscaled vs. scaled variances) as a function of spatial coordinates
1. What data types are you visualizing? I wanted to visualize spatial data (locations of spots on tissue sample) and quantitative data (PC1 values for each spot). I looked at...
Analyzing the Relationship between Cell Gene Expression and Position in the Eevee Dataset
1. What data types are you visualizing? I am visualizing quantitative data, specifically cell gene expression and positional data (x and y coordinates).
Visualizing Relationship Between Spatial Proximity and Gene Expression Profile Similarity
1. What data types are you visualizing?
Top 5 Genes with Highest PC1 Loads
This visualization addressed the second aim, specifically how gene loadings on the first PC relate to features of the gene when scaled and unscaled. Particularly, I used a violin plot...
Comparison of Pikachu Cells Within Gene Expression Space and Physical Space
This data visualization utilizes the Pikachu dataset to investigate how cells are related in the gene expression space as compared to their physical space distribution. Even more, the visualization uncovers...
