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
Visualization of cells in physical space vs gene expression space (HW2 for Yi Yang)
1. Description It uses quantitative data (x and y coordinates of cells and gene expression data) to visualize how cells relates in gene expression space versus physical space. Original data...
Relationship between transcriptomic and spatial coordinate using PCA
1. What data types are you visualizing? I am visualizing quantitative data of the first two PCs of the gene expression of each cell, as well as the quantitative data...
Exploring the Relationship Between Gene Expression and Physical Space Using PCA in Spatial Transcriptomics
1. What data types are you visualizing? In “PCA Scree Plot,” the data type visualized is quantitative (the variance explained by each principal component, a continuous numerical value).
Gene Expression Level of GZMA in coordinate
1. What data types are you visualizing? I am trying to visualize aligned_x, aligned_y which is spatial data as it provides location information of the cell. And the gene expression...
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.
Spatial Distribution of Cells by Nucleus-to-Cell Ratio Write Up
1. What data types are you visualizing? I am visualizing quantitative data representing the nucleus-to-cell area ratio for each cell, quantitative data of ERBB2 expression levels to indicate gene activity,...
Mean Gene Expression of Top Genes in COL Gene Group
1. What data types are you visualizing? I wanted to visualize the expression of genes related to collagen in the sequencing dataset. More specifically, I looked at the mean expression...
The Top 10 Genes Expressed in the Eevee Dataset
1. What data types are you visualizing? I am visualizing gene expression data obtained through sequencing of the top 10 genes expressed in the Eevee dataset.
Visualizing Spatial Correlation Between Helper T Cell Markers CCL5 and CD4
1. What data types are you visualizing?
Scatter Plot of POSTN vs LUM Expression
1. What data types are you visualizing? I am visualizing quantitative data for the expression levels of the POSTN and LUM genes in all the individual cells and I am...
