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
Health
Data Expeditions, Genomics and the Life Sciences
Year
2017
Contact
C. Ryan Campbell
Biology
c.ryan.campbell@duke.edu

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

Neural Networks and Photogrammetry for Analysis of Marine Remote Sensing Data

KC and Patrick led two hands-on data workshops for ENVIRON 335: Drones in Marine Biology, Ecology, and Conservation. These labs were intended to introduce students to examples of how drones are currently being used as a remote sensing tool to monitor marine megafauna and their environments, and how machine learning can be used to efficiently analyze remote sensing datasets. The first lab specifically focused on how drones are being used to collect aerial images of whales to measure changes in body condition to help monitor populations. Students were introduced to the methods for making accurate measurements and then received an opportunity to measure whales themselves. The second lab then introduced analysis methods using computer vision and deep neural networks to detect, count, and measure objects of interest in remote sensing data. This work provided students in the environmental sciences an introduction to new techniques in machine learning and remote sensing that can be powerful multipliers of effort when analyzing large environmental datasets.

Visualizing Twitter: Public Discourse and the 2018 Florida Midterm Election Recounts

This two-week teaching module in an introductory-level undergraduate course invites students to explore the power of Twitter in shaping public discourse. The project supplements the close-reading methods that are central to the humanities with large-scale social media analysis. This exercise challenges students to consider how applying visualization techniques to a dataset too vast for manual apprehension might enable them to identify for granular inspection smaller subsets of data and individual tweets—as well as to determine what factors do not lend themselves to close-reading at all. Employing an original dataset of almost one million tweets focused on the contested 2018 Florida midterm elections, students develop skills in using visualization software, generating research questions, and creating novel visualizations to answer those questions. They then evaluate and compare the affordances of large-scale data analytics with investigation of individual tweets, and draw on their findings to debate the role of social media in shaping public conversations surrounding major national events. This project was developed as a collaboration among the English Department (Emma Davenport and Astrid Giugni), Math Department (Hubert Bray), Duke University Library (Eric Monson), and Trinity Technology Services (Brian Norberg).

Queens of Antiquity

Understanding how to generate, analyze, and work with datasets in the humanities is often a difficult task without learning how to code or program. In humanities centered courses, we often privilege close reading or qualitative analysis over other methods of knowing, but by learning some new quantitative techniques we better prepare the students to tackle new forms of reading. This class will work with the data from the HathiTrust to develop ideas for thinking about how large groups and different discourse communities thought of queens of antiquity like Cleopatra and Dido.

Please refer to https://sites.duke.edu/queensofantiquity/ for more information.