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
Identification of White Pulp Tissue Structures within CODEX Dataset
1. Create a data visualization and write a description to convince me that your interpretation is correct.
CODEX dataset analysis
We conducted normalization, standardization, dimensionality reduction, k-means clustering, and differential expression analysis to reveal two distinct cell clusters within the CODEX data. tSNE plots and marker expression heatmaps is plotted...
Interpreting CODEX data
Describe your figure briefly so we know what you are depicting (you no longer need to use precise data visualization terms as you have been doing). The data was normalized...
Exploring differences between linear and non-linear dimensionality reduction methods
Description The visualization compares three different dimensionality reduction techniques—PCA (Principal Component Analysis), t-SNE (t-Distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection)—to visualize high-dimensional gene expression data from...
Question 4: Exploring the Effect of Varying Principal Components for Non-Linear Dimensionality Reduction
For this project, I created an animation using gganimate to visualize the effect of varying the number of principal components before applying t-SNE. I used the Pikachu dataset. First, I...
EC1: Comparing PCA and tSNE clustering methods with gganimate
What’s vizualized? A gif visualizing the cluster derived with kmeans in reduced dimensional space using linear vs non-linear methods (PCA and tSNE), as well as in the original physical space....
hw 4 DEG analysis
Description of analysis The modification from the HW3 using eevee data set is that I changed the selection of cluster based on the overall cluster visualization in physical space. Method-wise,...
HW4: Exploring Cell Type with Differentially upregulated CD4
1. Figure Description. Figure A: Total within-cluster sum of squares using different value of k. Figure B: Cluster 2 is highlighted in red in PCA space, while the remaining three...
Identifying B cell markers in imaging dataset
To begin analyzing the imaging dataset, I decided to normalize by cells’ areas, rather than use count-based normalization. Afterwards, I clustered my normalized gene expression data using k-means and determined...
Analyzing PDGFRB Gene Expression in Pikachu Dataset
Visualization Summary In this visualization, I analyzed a cluster within the Pikachu dataset responsible for cell growth, and likely cancer. This was a major change from the Eevee sequencing dataset...
