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 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 Suki
Lectures: 8:00am-9:50am Monday, Wednesday, and Friday. See Canvas for location details.
Office Hours: 10:00am-10:50am Monday, Wednesday, and by request. See Canvas for location details.
Course Details
☞ see Course tabFeatured Visualizations
Using clustering and deconvolution to visualize cell types and upregulated genes in different data sets
1. Figure description This multi-panel data visualization uses principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), k-means clustering, deconvolution, and differential expression analysis to...
tSNE on varying PC numbers
Description This animation adresses the question: “If I perform non-linear dimensionality reduction on PCs, what happens when I vary how many PCs I use?”
Effect of Varying PC Count on tSNE Space - Visium
Write a a brief description of your figure so we know what you are visualizing.
Deconvolution and Multi-Modal Comparison of the Renal S3 Segment
Note, the png is named “EC2_ooni5.png”, as a desired name was not specified in the HW powerpoint.
HW5
1. Figure Description I created a multipanel figure to show the distribution of B cells and T cells in thhe spleen. Throughout, I used the...
Identification of CODEX data as White Pulp
Perform a full analysis (quality control, dimensionality reduction, kmeans clustering, differential expression analysis) on your data. Your goal is to figure out what tissue structure...
HW 5
###Summary To identify the tissue structure represented in this CODEX dataset, I performed quality control, dimensionality reduction, k means clustering, differential expression analysis, and cell-type...
Identification of kidney collecting duct principal cells through dimensionality reduction, k-means clustering, and differential expression analysis
1. Figure description This multi-panel data visualization uses principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), k-means clustering, and differential expression analysis to characterize...
hw4: cortical tubule area in Xenium data
I’ve been analyzing Visium data so far, and this time I switched to Xenium data to try to identify the same cell type I found...
HW3: Multi-Panel Data Visualization of a Transcriptionally Distinct Proximal Tubule Epithelial Cell Cluster in the Xenium Dataset
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...
A multipanel data visualization distinguishing the ascending loop of henle in mouse kidney tissue
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...
Visualization of Proximal Tubule Cells in Kidney Tissue Sample
Description of Data Visualization: The raw Xenium dataset was normalized according to library size and log normalization before having its dimensionality reduced using principal component...
HW2
Question explored: “How do tSNE coordinates change as you increase or decrease the perplexity?”
Comparing high vs. low PC1 loading genes
Aim: How do the genes with high versus low loadings relate to each other? How are they patterned relative to each other in the tissue?...
Spatial Organization of Genes with Extreme PCA Loadings
1. What data types are you visualizing? I’m visualizing both quantitative and categorical data. The dataset has quantitative spatial information of x and y coordinates...
Spatial Expression of Avpr2, Inmt, and Rnf24
1. What data types are you visualizing? I am visualizing 3 data types. First, categorical data of 3 genes: Avpr2, Inmt, and Rnf24. Second, spatial...
HW1 Submission
1. What data types are you visualizing? I am visualizing quantitative data of the gene expression counts of the Cyp2e1, Cyp4b1, and Slc22a6 genes for...
HW1
1. What about the data would you like to make salient through this data visualization? Since I am working with Visium 10x geneomics data, every...
All Visualizations
HW4
Changes made to code from HW3: Switched from Visium to Xenium and had to cut down data frame to first 10,000 cells in order to make the dataset more manageable...
Validating Identity of Proximal Convoluted Tubule Segments
For this visualization, the Visium dataset is analyzed. The previous visualization with the Xenium dataset analysis brought to light the PCT cell-type, particularly the early segments of proximal tubules located...
Llucero3
In HW3, I identified a transcriptionally distinct cell type using an unsupervised workflow consisting of library-size normalization (CP10K + log1p), highly variable gene selection, PCA, tSNE embedding, and k-means clustering....
HW4: Identifying Straight Proximal Tubule in Visium and Xenium Datasets
Description To identify the Straight Proximal Tubule (SPT) in the Xenium dataset using my HW3 Visium code, I increased the number of k-means clusters from 5 to 7 centers =...
Identification of kidney collecting duct principal cells through dimensionality reduction, k-means clustering, and differential expression analysis
1. Figure description This multi-panel data visualization uses principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), k-means clustering, and differential expression analysis to characterize a cluster of interest based...
Identifying a cluster of Proximal Tubule Epithelial Cells
Description To identify the same group of cells in the Visium dataset, I adapted the same analysis I used previously for the Xenium dataset. Like before, I normalized the raw...
Identifying Proximal Tubule Cell Populations in Spot-Resolution Spatial Transcriptomics
For these visualizations, I switched over to the spot-resolution Visium dataset from the single-cell Xenium dataset. I was able to successfully apply the same pre-processing pipeline and maintain parameters from...
Identification of Thick Ascending Limb Cells in Xenium Dataset
Write a description to convince me you found the same cell-type. You will likely need to change your code from HW3. Write a description of what you changed and why...
Adapting Cell Type Characterization from Visium Dataset to Xenium Dataset
Visium –> Xenium I made three main code changes when switching from Visium to Xenium. First, I updated the data source to Xenium-IRI-ShamR_matrix.csv.gz. Second, I reduced point size from 0.6...
HW 4
Write a description to convince me you found the same cell-type. Cluster 2 most likely represents proximal tubule S3 epithelial cells. This is supported by its inner stripe/outer medulla location...
HW4: Multi-Panel Data Visualization of a Transcriptionally Distinct Proximal Tubule Epithelial Cell Cluster in the Visium Dataset
Use/adapt your code from HW3 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...
