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
Taking a closer look at Jonathan Wang's HW1 Visualization
Whose code are you applying? Provide a JHED. I decided to choose Jonathan Wang’s (JHED = jwang428) visualization from HW1.
VCAN Expression versus x and y alignment in spacial-barcode bead capture
Whose code are you applying? I’m applying Kiki Zhang’s code (JHED: szhan128)
Critique of Amanda Kwok's HW1: Relationship between LUM and POSTN Expression
Whose code are you applying? Provide a JHED I am applying the code of Amanda Kwok (originally for the pikachu dataset) to the eevee dataset. Her JHED is akwok1.
Visualizing Mean and Variance of Gene Expression
This visualization applies code written by Caleb Hallinan (challin1) with a few minor modifications to better fit the pikachu data set.
Spatial variation in expression of CD4 gene
Whose code are you applying? I am applying Esther’s code of visualizing spatial variation in CD4 gene expression to the pikachu dataset.
Spatial Localization of Cells for Total Number of Distinct Genes Expressed
Whose code are you applying? Jonathan Wang (jwang428)
Learning from classmates - Visualizing Locations of Different Gene Expressions
Whose code are you applying? Connie C (cchen260)
Spatial Locations of CD4 Expression
Whose code are you applying? Provide a JHED I am applying code by ewhang. The author visualized the eevee dataset. Their goal was to see the specific locations of CD4...
Correlation of Transcription Factor Expression with Cell and Nucleus Area
1) Whose code did you apply? I applied the code jwang428 wrote for the previous homework assignment.
Spatial Plot of POSTN Levels Across Spatial-Barcode Beads
Whose code are you applying? Provide a JHED I am applying Wenyu Yang’s (wyang51) code onto the eevee dataset. The source and modified code can be found below.
Visualizing the relationship between cell spatial position and HOXD8 expression
Whose code are you applying? Provide a JHED I am applying Andrew’s code (aying2). I switched out the gene he used (VCAN), as it was not in the pikachu dataset....