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
Validating Efficacy of Spatial Capture Spot Transcriptomic Deconvolution in Interrogating Cell Type
In the previous analysis (HW3), I depicted a visium dataset in embedded space and clustered to identify a B-cell-related cell type. Here, I perform STdeconvolution to parse the cell type...
Exploring t-SNE Embeddings with Varying Numbers of Principal Components
Write a brief description of your figure so we know what you are visualizing.
Using Clustering and Deconvolution to Identify B-Cell Populations in Spatial Transcriptomics
In HW4, I identified a B-cell population within the Eevee dataset, based on the upregulation of CD79A gene expression. I used K-means clustering with K=4 to classify spatial transcriptomics spots...
Linear vs Nonlinear Dimension Reduction
I used the Pikachu dataset, an imaging based dataset. Therefore, I did not normalize the gene expression, as it was already for each cell. I then used a linear technique...
Deconvolution
The data was normalized by counts and then log transformed. Deconvolution was performed on the raw data.
PCA, tSNE, and Spatial Distribution Animation of Pikachu Dataset
This visualization explores the differences between linear and nonlinear dimensionality reduction techniques for analyzing spatial transcriptomics data. Specifically, the animation compares the spatial distribution of cells, their representation in t-SNE...
Effect of Varying Number of Principal Components on t-SNE Visualization of Spatial Transcriptomics Data
Description visualization This figure visualizes the effect of varying the number of principal components (PCs) used in t-SNE for dimensionality reduction on a spatial transcriptomics dataset. The animation transitions smoothly...
Visualizing the Impact of the Number of PCs used to perform Nonlinear Dimensionality Reduction using tSNE
Write a brief description of your figure so we know what you are visualizing.
