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 Kalen Clifton
Office Hours: 10:00am-10:50am Monday, Wednesday, and Friday. See Slack for location details.
Lectures: 8:00am-9:50am Monday, Wednesday, and Friday. See Slack for location details.

Course Details
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All Visualizations

Identification of a Cluster Associated with Immune Cells

Description of my multi-panel plot Here, I identified a cluster that seems to include T cells, macrophages, and other immune cells in the Visium breast cancer data. In order to...

Determining Cell Type for Visium Data

I used kmeans clustering to identify different cell types by looking at clusters in my data. I preproceessed my data by normalizing by total gene count and putting everything on...

Identifying an B cell/ Adipocyte Composite Cluster in Visium Dataset

Similar to HW5, I performed kmeans clustering on my normalized dataset. I then went through each of the clusters in spatial representation as well as PC dimensional representation to understand...

Exploring the Cell Types of Breast Cancer Visium Data

The cluster I selected (Cluster 2) corresponds to breast cancer tumor cells. According to my differential expression analysis, the up-regulated DE genes include MAL2, TPD52, and DHCR24. Those are well-known...

Validation of cell type clustering via differential gene expression

The purpose of this visualization to present the usage of differential gene expression to validate cell type identification in k-means and tsne analysis of the dataset. The quantitative data of...

Identification of two Cell Clusters

In my plots, I am looking at cell clusters 1 and 8. These clusters separate strongly from the other cells along PC1 and remain close together on t-SNE projections. In...

Exploration of Spatial Gene Expression

A general idea about the exploration

Cell Type Exploration of Charmander Data Set

The cluster appears to be endothelial cells that make up adipose tissue. When looking at the Wilcox vs log2fc graph three of most significantly upregulated genes are CAV1, VWF, and...

Differentially expressed genes and cell-type annotation for cluster 2

Cell-type annotation For this data visualization, we selected cluster 2 as it presented an interesting pattern. Then, by performing kmeans clustering and differential analysis on the normalized data, we noticed...

Multi-panel data visualization of k-means clustering results and gene expression

The data visualization chooses cluster 3 to be analyzed. It then identifies gene that are differentially expressed in that cluster compared to all the other clusters in the dataset. The...