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

Identification of the Breast Glandular Cells

The visualization presented above comprises eight panels, all of which provide evidence to support the hypothesis that cluster 1 corresponds to breast glandular cells, a type of epithelial cell (1)....

Validation of cell type clustering via differential gene expression

The purpose of this visualization to present the usage of differential gene expression to validate cell type identification in k-means and tsne analysis of the dataset. The quantitative data of...

Comparison of Dimensionality Reduction on Normal, Log10 Transformed and ScaleD Gene Expression

Should I normalize and/or transform the gene expression data (e.g. log and/or scale) prior to dimensionality reduction?

Comparison of using normalized and unnormalized data on gene expression clustering in t-SNE graphs

What data types are you visualizing? I am visualizing quantitative data of the comparative gene expression of two genes KRT7 and PTPRC. I am also visualizing the quantitative data of...

Effects of normalizing by gene count in the reduced dimension visualization

What data types are you visualizing? I present quantitative data of the PCA and tSNE reduced dimension applied to the raw gene expression data and the normalized by gene count...

The effects of log transformation, scaling and normalization prior to PCA and non-linear dimensionality reduction (tSNE) on PDGFRB gene expression data

What data types are you visualizing? I am visualizing quantitative data of 2 dimensional reduction through PCA and tSNE of original PDGFRB expression for each cell, quantitative data of 2...

Dimensionality Reduction approach for spatial transcriptomics in genes ZEB1 and ZEB2

1. Should I normalize and/or transform the gene expression data (e.g. log and/or scale) prior to dimensionality reduction? It is recommended to normalize the data with scale to help take...

Clusters of Genes Expressing ERBB2 using t-SNE

What data types are you visualizing? I am visualizing the similarities in levels of overall gene expression in cells that have non-zero expression of ERBB2 and the level of expression...

Running tSNE analysis on genes or PCs

What data types are you visualizing? In this multi-panel plot, I am visualizing various quantitative and categorical data. For the PCA plot on the upper left, I am visualizing quantitative...

AQP1 Expression for Contrasting Principle Component Numbers

What data types are you visualizing? I am using categorical data (zero and nonzero expression) as well as quantitative (color gradient of expression).

Homework 3 Submission: Comparing Pre-processing Methods Prior to PCA

This series of plots demonstrates the effects of preprocessing steps taken prior to utilizing PCA for dimensionality reduction of multi-dimensional gene expression data. Some pre-processing steps include but is not...