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
☞ see Course tabFeatured Visualizations
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...
Identification of a Cluster Associated with CD8+ T cells
Description of my multi-panel plot Here, I identified a cluster that seems to be CD8+ T cells. In order to generate the plot above, I...
Homework 5
Description
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...
Running tSNE analysis on genes or PCs
What data types are you visualizing? In this multi-panel plot, I am visualizing various quantitative and categorical data. For the PCA plot on the upper...
AQP1 Expression for Contrasting Principle Component Numbers
What data types are you visualizing? I am using categorical data (zero and nonzero expression) as well as quantitative (color gradient of expression).
Comparison of Dimensionality Reduction Methods for Representation of Clustered Data
What data are you visualizing
The effect of Count Per Million normalization on Dimensionality Reduction
What data types are you visualizing? I am visualizing quantitative data of cells’ position on tSNE embedded 2-dimensional space. I am also visualizing the total...
Cells Clustered By Gene Expression
What data are you visualizing
Comparative detection of KRT7 and PTPRC gene expression
What data types are you visualizing? I am visualizing categorical data of the comparative gene expression detection of two genes KRT7 and PTPRC. There are...
Spatial position of cells with low gene count
What data types are you visualizing? I am visualizing the categorical data of the presence of 3 or fewer genes in a cell together with...
Comparing the relationship between Growth Factor Receptors Expression and Cell Area
What data types are you visualizing? I am visualizing quantitative data of the expression count of the EGFR, PDGFRA and PDGFRB genes for each cell,...
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...
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...
