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 Rafael dos Santos Peixoto
Lectures: 8:00am-9:50am Monday, Wednesday, and Friday. See Canvas for location details.
Office Hours: 10:00am-10:50am Monday, Wednesday, and Friday. See Canvas for location details.
Course Details
☞ see Course tabFeatured Visualizations
Locating a cell type 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...
Identifying Cell-type from Breast Cancer Tissue Spatial Transcriptomics Data using K-means Clustering, tSNE, and Wilcox-test
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...
KRT8 Expression in Breast Cancer
In this visualization, I explore the expression of KRT8, a cancer related gene, in breast cancer tissue. In panel A and E, I use points...
Cell type exploration using differential gene expression analyses
In the above visualization I have identified a cluster that belong to plasma cells or mature B cells. I started with normalizing the gene expression...
The Effects of Normalization & Transformation on Loading Values for PCA
What data types are you visualizing? For the graph titled “Raw Data’s PCA”, I am visualizing the (1) quantitative data of ERBB2 expression, (2) quantitative...
Comparing the effect of tSNE on varying number of PCs:KRT7 expression
I am visualizing the effect of performing non-linear dimensionality reduction (TSNE) on varying number of PCs. The gene expression was normalized (by total gene expression...
Effect of Introducing Principle Components on Non-linear Dimensionality Reduction
What data types are you visualizing? In the multi-panel plot, I am visualizing spatial and quantitative data with diffrerent projection approaches. The visualization contains spatial...
Comparison of Gene Influence on PC1 in Raw and Cell Area-Normalized Data
Write a description describing your data visualization using vocabulary terms from Lesson 1. What data types are you visualizing? What data encodings (geometric primitives and...
Centroid positions, cell and nucleus areas of each cell
What data types are you visualizing? I am visualizing quantitative data of the ratio of nucleus area to cell area for each cell, quantitative data...
CCND1 Expression vs ERBB2 Expression
What data types are you visualizing? I am visualizing quantitative data for the expression counts of ERBB2 and CCND1, as well as the quantitative data...
Spatial Distribution of Gene Expression
What data types are you visualizing? I am visualizing quantitative data of the log-10-transformed expression level of the TP53 gene, quantitative data of the total...
Frequency of Most Expressed Gene in a Spot
What data types are you visualizing? We are visualizing quantitative data, the number of spots in which the gene is most expressed, and categorical data,...
All Visualizations
Visualization of spleen tissue structure
Tissue structure representation in the CODEX data In this visualization, we investigate the tissue structure from spleen protein localization data. For the first row of visualizations, in plots a and...
Multi-Panel Data Visualization for Pikachu Dataset
In this data visualization we are visualizing clusters in the pikachu dataset. We identified cluster 17 as potentially being the same cell type we identified from the Eevee dataset due...
Differentially expressed gene (DSC2) in cell clusters by k-means
Use/adapt your code from HW4 to identify the same cell-type in the other dataset. Create a multi-panel data visualization and write a description to convince me you found the same...
Identification of Myoepithelial Cells in Breast Cancer Tissue: For Eevee Dataset
Figure Description: The workflow from homework 4 was applied with some modifications (as described below) to analyze the eevee dataset and identify a cluster of cells that were of the...
Differential Expression of ERBB2 Gene in Pikachu Data
Figure Description I switched from the Eevee dataset to the Pikachu dataset. As a result of the elbow plot, I judged that the optimal k for k-means clustering of the...
Pikachu dataset: Interpreting cell cluster through dimensionality reduction and differential gene expression analysis
Describe what you changed and why you think you had to change it. I switched from Eevee dataset to Pikachu dataset. In my previous visualization, I performed K means clustering...
Multi-panel visualization of a cluster with differentially expressed genes
Use/adapt your code from HW4 to identify the same cell-type in the other dataset. Create a multi-panel data visualization and write a description to convince me you found the same...
Cell Cluster Identification and Validation in Breast Tumor Tissue
### Plot Description This visualization presents differential gene expression to validate cell type identification by k-means on 2D tSNE space. The spatial-transcriptomics data on breast tumor tissue is preprocessed by...
Exploring cell type using differential gene expression through KMeans clustering
Write a description of what you changed and why you think you had to change it.
Identification of Epithelial Cell Cluster in Breast Cancer Tissue of Pikachu Data Set
Description of Visualization Changes When visualizing the Eevee data set, I had visual analysis to determine the optimal number of clusters to be K=10. In the pikachu data set, I...
Analyzing for Breast Glandular Cells: V2
Figure Description: In applying the workflow adapted from homework 4 (k-means clustering and differential gene expression analysis), I believe I was able to identify breast glandular cells, or more specifically,...
