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
Hw4: Finding the same cell cluster in the other dataset
This panel shows that the cell cluster that I found in the EEVEE dataset is the same that I had found in the Pikachu dataset for the previous homework. The...
Identifying the same cluster of cells within the Eevee dataset
1. Write a description explaining why you believe your data visualization is effective using vocabulary terms from Lesson 1.
Identifying Epithelial cells in both datasets
Description Notes: I want to change the cluster identified in HW3. Originally, it is most likely a fibroblast-like stromal cell because the top 20 highly expressed genes include SFRP4, WIF1,...
“Epithelial cell discovery in eevee dataset”
###1. Description Figures A and B share a common legend and analyze the dataset at the cluster level, where green highlights the cluster of interest and gray represents all other...
Reanlaysis of another dataset
We previously performed k-means clustering with k=8, assuming that a higher number of clusters would better capture transcriptional heterogeneity. However, after applying the elbow method, we found that the optimal...
Re-Identify Fibroblast-Related Cell Cluster through Imaging-Based SRT Data
1. Description of the Figure I used similar dimensionality reduction techniques and differential gene expression analysis on the imaging-based pikachu dataset. The figure consists of 7 plots. K-means clustering is...
Locating fibroblasts in breast tissue using spatial transcriptomics 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). There are six plots...
Rediscovering the Same Epithelial Cell Type in Spatial Transcriptomics: Leveraging ELF3 Expression Across Datasets
Validation of Same Cell Type
Differential Gene Expression Analysis- TACSTD2
Description I created 5 visualizations of a particular cluster from a KNN clustering process. I chose the cluster which corresponded to a circle of cells in the upper left corner...
HW3: Exploring Cell Type with Differentially upregulated CD52
1. Figure Description. Figure A: Cluster 1 is highlighted in orange in PCA space, while the remaining six clusters are shown in grey. The axes represent PC1 and PC2. Figure...
