Tips in Data Visualization for Genetic Mapping

Project Summary

The aim of this Data Expedition was for students to learn hands-on data visualization techniques using a variety of data types. Students first discussed how data visualization is useful, and tips to make graphs both visually appealing and easy to understand. 

Themes and Categories
C. Ryan Campbell

Graduate Students: Jenn Coughlan, Ryan Campbell

Course: Biology 490s - Methods in Comp Bio & Genomics

Over two 70-minute class periods, the students worked through two tutorials; the first introducing them to the basics of ggplot2, a data visualization package in the free statistical interface R. Students were then given a homework assignment to visualize a simple genotype-phenotype dataset, ‘Coughlan_inversiongenopheno.csv’. In the second class, we began by discussing the homework assignment, thinking of challenges and next steps. Students were then given a much more complicated dataset, involving reduced representation whole genome data from the wildflower Senecio (from Roda et al. 2017, dataset ‘Fst_BSA_wLinkagegrp.csv’). Students used this data to associate survival with allele frequencies across different habitats to determine regions of the genome which are associated with adaptation to edaphic conditions. 

Download the course slides (PDF).


Related Projects

This Data Expedition introduced hypothesis-driven data analysis in R and the concept of circular data, while providing some tools for importing it and analyzing it in R.

The aim of this data expedition was to give students an introduction to stable isotopes and how the data can be used to understand trophic dynamics. 

A team of students led by a computational biologist and a cell biologist will develop methods to identify cell subsets and their developmental, maturation and activation lineage relationships using deep learning approaches. Students will learn to process single cell RNA sequencing data and use the Python programming language and TensorFlow to characterize lung stem cells involved in wound healing. This work will help Duke researchers establish a deep learning pipeline for single cell analysis with applications in immunology, cell biology and cancer.