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

Determination of Cell Cluster 5 as Natural Killer Cells

What cell type is the cluster you picked and what went in to your determination? I have identified cell cluster 5 as being Natural Killer cells due the differential expression...

Analysis of Spleen CODEX data

Cell-type annotation After reading, cleaning and normalizing the data, I performed kmeans clustering and observed that cluster 2 presented an interesting pattern in space. The most significant proteins (low p-value...

EC

Comparing the gene expressions between clusters revealed that cluster 6 had a higher expression value for Podoplanin. To determine the optimal cluster, the highest gene expression value for a specific...

Cells Clustered By Protein Levels

I suspect that cluster 3 represents follciular dendritic cells (FDCs). A number of proteins are significantly upregulated in cluster 3, including SMActin, Podoplanin, and CD21/CD35. SMActin is found in a...

Identification of HER2+ Breast Cancer Cells

A. PCA and tSNE projection of spatial transcriptomics data. B. Cell cluster overlayed on spatial plot of cell patches. C. DE analysis of Cell Cluster 1 against all others; 20...

Identification of the Breast Cancer Cells

The visualization presented above comprises six panels, all of which provide evidence to support the hypothesis that cluster 8 corresponds to breast cancer cells. The top row panels are referred...

Attempts of a cell type identification

A general idea about the exploration

Looking at the cluster I found that the MUCL1 gene was heavily upregulated in the cluster. Looking at ProteinAtlas (https://www.proteinatlas.org/ENSG00000172551-MUCL1) this gene is heavily expressed in mammary glands. Considering the...

Cell type identification based on clustering and gene expression on the Visium breast cancer dataset

Given the differential gene expression analysis, the cell type equivalent to cluster 1 in the data file is most likely a macrophage. Having identified the overexpressed genes in this cluster...

Multimodal analysis of cell-type using gene expression patterns

The raw gene expression data set was normalized by dividing each gene of each cell by the total number of genes for that cell. This amount is then multiplied by...