Building Skills in Quantitative Biology
28 February, 2022
Quantitative skills are essential for biological research. We consider such skills to include the computational, statistical and mathematical techniques used to study life and living organisms, including aspects of big data, transparency and reproducibility in science. However, the breadth of quantitative techniques now employed in biology make it very likely that there may be no suitable local expertise within a student’s home institution or a biologist’s workplace.
This e-book consists of five independent chapters or “modules” designed to teach different quantitative skills to graduate students and biologists working in academia, government agencies and private organisations. A key theme is that while the techniques presented are from various disciplines (such as computer science, statistics and mathematics), they are presented in a way that is suitable for a biological audience. Our examples and approach reflect our personal use of these tools as researchers in biology.
Each module is designed to quickly get you up and running on the topic of interest in 3-5 hours. As such, these materials should be thought of a basic introduction. We provide pointers to more advanced materials, but our aim is to “jump-start” your use of these tools. On the other hand, these modules are not designed for absolute beginners. With the exception of the module on Git and Github, the materials are written assuming that learners have some basic familiarity with R, and a standard undergraduate background in calculus and univariate statistics.
Because the modules are designed to be independent (i.e., you don’t have read the whole e-book!), you can select subtopics that are directly relevant to your research. Our hope is that this approach will simultaneously provide more targeted training and reduce the time commitment that would be involved in more generalized formal courses. Pick and choose what you need!
The five modules are:
Git and GitHub (Andrew Edwards) – covers the tools widely used to share code, collaborate with colleagues and create a transparent record of research.
R Markdown (Andrew Edwards) – demonstrates how to create dynamic documents that are fully reproducible, with a clear link between the underlying R code and the resulting figures, tables and results.
Multivariate analysis: Clustering and Ordination (Kim Cuddington) – discusses multivariate quantitative methods that are used to understand data when there is more than one response (or measurement)
Machine learning and classification (Kim Cuddington) – continues explorations of multivariate data and in particular the topic of classification but using machine learning approaches.
Optimization (Brian Ingalls) – optimization is the act of identifying the extreme (cheapest, tallest, fastest, …) over a collection of possibilities. Applications include the manipulation (e.g. optimal harvesting or optimal drug dosing) and construction (e.g. robust synthetic genetic circuits) of biological systems, and experimental design.
1.1 Open science
In the spirit of open science (and using the tools introduced in the first two modules), this e-book was written collaboratively and openly in R Markdown, with files shared via GitHub here.
1.2 Accessibility Statement
We developed Building Skills in Quantitative Biology with a commitment to accessibility and usability for all learners.
The accessibility of these materials was assessed by the Centre for Extended Learning, University of Waterloo. This review was based on the WCAG 2.0 Guidelines at success criteria Level AA. The authors have addressed all known accessibility issues to the best of their abilities.
The following known accessibility issues persist and may cause difficulties for some persons with disabilities:
Code output is only distinguished from code input by 1. output has a black border box and 2. input is colour shaded
Internal links to figures and tables are only linked by the numbering (e.g., 5.1) of the object, rather than the full text (e.g., Figure 5.1)
Please let us know if you discover other issues on the issues tab for the github repository
Despite extensive feedback from our student guinea pigs, we anticipate further revisions based on feedback, since we can consider this work as a living document. If you use these materials please take some time to let us know how they work for you, using this survey.
We acknowledge support from the Government of Ontario through a grant from eCampusOntario, and the support of DFO and the Faculty of Science, University of Waterloo. The grant and the Faculty of Science each funded a student to help with creating the project. We thank Luwen Chang and Matthew Zhou for their amazing learning curves, and subsequent help coding up the modules. The eCampusOntario grant also funded several students to evaluate an early version of the materials. Lina Aragon Baquero, Lauren Banks, Madison Brook, Jacob Burbank, Nicole Gauvreau and Aranksha Dilip Thakor provided valuable feedback.
The Git and GitHub module builds upon workshop materials that were originally developed with Chris Grandin (DFO), who AME also thanks for assistance with the module.
This project is made possible with funding by the Government of Ontario and through eCampusOntario’s support of the Virtual Learning Strategy. To learn more about the Virtual Learning Strategy visit: https://vls.ecampusontario.ca.
Please cite this work as:
Cuddington, K, Edwards, A.M., and Ingalls, B. (2022). Building Skills in Quantitative Biology. https://www.quantitative-biology.ca
Kim Cuddington, Andrew M. Edwards, and Brian Ingalls. Building Skills in Quantitative Biology (https://www.quantitative-biology.ca) is available under an Ontario Commons License and a CC BY-NC-SA 4.0. . Contact kcudding AT uwaterloo DOT ca for more information
All materials licensed under the Ontario Commons License (Version 1.0) unless otherwise specified. The license deed is available to read at https://vls.ecampusontario.ca/wp-content/uploads/2021/01/Ontario-Commons-License-1.0.pdf.
All materials are also licensed under the Creative Commons BY-NC-SA 4.0 license unless otherwise specified. The license deed is available to read at https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.