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
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All Visualizations

Exploring Gene Expression Effects on Linear and Nonlinear Dimensionality Reduction

What’s the difference if I perform linear or nonlinear dimensionality reduction to visualize my cells in 2D? When visualizing the cells in 2D, nonlinear dimensionality reduction showed a more well-defined...

Comparison of Linear and Nonliner Dimensionality Reduction

What’s the difference if I perform linear or nonlinear dimensionality reduction to visualize my cells in 2D? Linear dimensionality reductions, such as principle component analysis, works best with data that...

Classmate Visualization: Visualizing Locations of Different Gene Expressions

Whose code are you applying? https://jef.works/genomic-data-visualization-2024/blog/2024/01/29/dvelazq5/

Most Expressed Genes in Breast Cancer Tissue Single Cell Spatial Transcriptomic Data

What data types are you visualizing? The spatial data regarding the x,y centroid positions for each cell are visualized, and the categorical data representing the most expressed gene in each...

FOXA1 and GATA3 Impact on Cell and Nucleus Area

Whose code are you applying? JHED: kbowden5

Visualization Critique

Caleb’s (challin1) visualization seeks to make more salient which genes have high mean or variance in expression. I believe this visualization is very effective because it uses position to encode...

Centroid positions, cell and nucleus areas of each cell

Whose code are you applying? Provide a JHED kbowden5

Characterization of Spatial ACAP3 Gene Expression Relative to Total and Single Gene Expression

Whose code are you applying? Provide a JHED I am applying April Yan’s code. Her JHED is yyan67.

The relationship between CCND1 Expression and ERBB2 Expression

Whose code are you applying? Provide a JHED I am applying Shaili Tripathi’s (jhed: stripat9) code to eevee dataset.

Applying the Spatial Distribution of Gene Expression visualization to the CXCR4 gene

Whose code are you applying? Provide a JHED. I am applying the code by Kiki Zhang (szhan128) for the eevee dataset to my pikachu dataset. There are two changes that...

Spatial Localization of Total Number of Distinct Genes Expressed - Pikachu

Whose code are you applying? Provide a JHED Jonathan Wang - jwang428

Critique of Wenyu Yang's 'A Spatial Plot of POSTN Levels in Breast Cancer Tissue'

Whose code are you applying? Provide a JHED I am critiquing Wenyu Yang’s (wyang51) “A Spatial Plot of POSTN Levels in Breast Cancer Tissue” https://jef.works/genomic-data-visualization-2024/blog/2024/01/28/wyang51/.