BIOS 338/538 Syllabus: Analysis and Visualization of Biological Data, Spring 2024

Course Description

This course addresses how to analyze, visualize and draw conclusions from biological data. It introduces basic concepts in statistics and the intuition behind them interwoven with training in data analysis using the R computing environment. Students will learn to identify underlying data structures and wrangle data. Students will also learn to effectively convey results using statistical graphics. Topics include basic R programming, data wrangling using modern tidyverse packages, t-tests, statistical modeling (linear regression, dose response curves). This class is targeted towards biologists, hence focuses on biological data for examples and omits rigorous statistical formulas. No prior experience with R required.

There will be a series of problem sets posted on the course website with instructions a week before they are due. You are encouraged to work with other students or use any outside resources when completing problem sets, but each student must submit their own written responses unless otherwise explicitly indicated in the problem set. Any group submissions or work should include attribution section detailing who did what. Students enrolled at the graduate level will produce a final project report detailing a specific biological research question, statistical methods and results and will present their final project to the class at the end of the semester.

Course Logistics

Instructor: Dr. Prashant Kalvapalle (he/him), Lecturer + Postdoctoral researcher, Biosciences, Rice University

Location: BRC 282 (or via zoom with prior permission)

Time: Tuesdays and Thursdays. 2:30-3:45 pm

Email: pbk1 [at] rice.edu

Office: Keck 205

Office hours: Friday, 3-4 pm, Keck 308 (in person + zoom)

Note: You will find the zoom links on the canvas website for the course

TAS

Annie Finneran

PhD student in Ecology & Evolutionary Biology (website)

Email: af58 [at] rice.edu.

Office hours: Tuesdays 11:30 am – 12:30 pm, ABL 100 (in person + zoom)

Samuel Schwartz

PhD student in Systems, Synthetic and Physical Biology (website)

Email: Sam.Schwartz [at] rice.edu

Office hours: Wednesdays, 4 - 5 pm, tables outside Keck 308 (in person + zoom)

Course schedule and supporting material

• Find the schedule here

• You will find the class-worksheets here on Github

• Assignments are located in this Github repository

Assessment

Assignment	Total grade	Grade per instance	Details
Attendance	10%	0.4% / class	on zoom with permission. 25 lectures
Participation	10%	1% / week	online discussion board (ask/answer), office hours, ~in-class participation
Problem sets	40%	6.7% / assignment	6 problem sets total
Midterm	20%		Take home, open book, collaborate
Final / project	20%		For BIOS 538 : Final project presentation : 10% and report 10% ; Undegrads/338: I will survey y’all to decide between a final project vs final exam

Grading Scale

• A 93-100
• A- 90-92
• B+ 87-89
• B 83-86
• B- 80-82
• C+ 77-79
• C 73-76
• C- 70-72
• D 60-69
• F <60

Assignment timelines

• Friday: Assignment uploaded

• Get help in office hours during the 2 weeks

• Friday 7 pm: submission. Can ask the instructor for extension into the weekend with explanation

• Next Saturday: receive your grades and feedback from TAs

Again, if you need any accommodations for particular submissions please email the instructor.

Participation rubric

Each week, these activities will count as participation credits. 1 credit x 9 weeks (starting 17/Feb) + 1 point for prior contributions = 10 points which goes to 10% of the grade allocated to participation. Bonuses can be earned by asking or answering more questions upto 15 points in total credit (implies that a max of 5% bonus from participation can offset other grades).

(1 point) Activity on the discussion board

Asking questions

• Post one good question on the discussion board (anonymous is ok too). The question itself can be of any complexity so there are no silly questions!. Questions should be clear and concise including these things below -
  • Detail what you intended to do and how it didn’t work
  • The minimal code that achieves the particular step formatted as Code snippet
    • The coded section should already include data by defining an R object. You can use dput() command to give out a small tibble (ex: 5 rows, 3-5 columns..)
  • What was the error you got? formatted as code
  • Provide a small subset of data to reproduce the error
  • show effort that you did to search the internet and a couple of links that you barked up on but maybe didn’t understand how to use. All these along with formatting the question properly would make it as easy as possible for someone to answer these questions
• Partial credit for the questions: only for the first 2 weeks as a training so everybody learns the correct way to ask questions
  • (0.5 point) incomplete question without any reproducible data but showing effort
  • (0.25 point) just for attempting: (not showing effort) posting a question without showing evidence of searching up first

Answering questions

• Answering a question satisfactorily with both code and explanation to enable anyone to reproduce. Enough explanation should be provided for a third party to understand (not just the person asking the question), and references should definitely be provided to backup your answer. Even the cause of the error message above should be explained if possible. Validity judged by the question asker + TAs, and validated by endorsement or commenting in the thread. Multiple answers eligible for credit if different enough/ teach something new (you should highlight and explain what is unique about your method and why it is better than previous answers)
• (bonus/1 point) Audience choice - questions and answers with >= 10x ❤️’s get extra credit
  • Adding a good resource such as a link or dataset with explanation of what subsets of it are most useful and for what purpose. Needs to be liked by >= 10 people over the duration of the course to get credit.

Office hour attendance

• (1 point) for showing up to office hours with any technical questions on zoom or in-person (one or more/week)

In-class question and answers

• (0.5 point) Asking a question in class or answering instructors questions are encouraged. Points will be provided in a subjective manner for questions that are useful in general or insightful

Special contributions

If you are looking for opportunities to get extra credit to up your grade towards to end of the semester, try these avenues and email the instructor pitching your contribution and you will hear back on how to show the proof of work and how much credit to expect!

• You will get 3% extra credit if two or more of your classmates/group members mention you to be an invaluable collaborator who taught them a lot during group work (end of course survey)

• Add valuable edits, such as to clarify, emphasize or add examples to Wikipedia pages of statistical concepts that are relevant / even slightly adjacent to the course

• Ask any R related question on stackoverflow relevant to the course that gets 1/2 upvotes (who are not class members 😛)

Should I take this course without any coding background?

How challenging will it be if I am very new to R?

I believe that if you have one undergraduate level course where you learnt to code in any programming language, it should not be too challenging to pickup R syntax with 3 weeks of practice. That said, the examples we walk through in class and working on the assignments (with help from very enthusiastic TAs) will be great resources to guide the learning process.

This classroom is a safe space, so no question is too simple or too “silly” to ask. And you would definitely have to put in the work outside of class to pickup the basics of programming quickly and attend office hours/use the discussion board to get help when you are stuck for too long on one thing.

There are many assignments, seems like a lot of work?

Practicing by working through code at your own time is the only way to actually learn coding. If you are lagging behind in class, the office hours and discussion board will be quite valuable

Textbooks/Reference material

There is no single textbook for the course. There will be multiple references, mostly from free textbooks or materials available online. All required readings and supplementary materials will be posted on the course website. All code and data files will also be posted on the course canvas site. Code and data files will typically be made available before class meetings; slides will be posted afterwards.

Core textbooks or references

• Textbook to skim: Hands on programming with R: For non-programmers, friendly intro to R with consistent examples of dice rolls throughout the book. Load data, navigate R, write functions.
• Use the search button to lookup data types and functions: Advanced R: (Hadley Wickham): Improvement for R users/other language programmers. Covers Basic R stuff + Functional programming, meta-programming, debugging, performance
  • The book says advanced but it is great for new R learners looking for specific concepts that are nicely explained with some illustrations.

• Use the search button for looking up stuff: R for Data Science : nice guide to load, transform and visualize data. From Hadley Wickham, one of the R/Tidyverse pioneers

• Lookup stuff: R cookbook: *Contains recipes for solving specific problems and gives insights into how they were solved. Data input, output, general stats, graphics, linear regression. “for* beginners: book will get you started faster”

• Tidyverse documentations for functions and packages such as dplyr. These are really easy to read, and have examples too.

Textbooks on general R / data science topics

• R manuals: Nice manual detailing lot of R usage in a nice website format

• Fundamentals of data visualization: guide to use visuals to accurately reflect data & tell a story. Choosing the right visualization, tailoring figure to audience.

• Happy Git and GitHub for the useR: Instructions to install git, integrate with github and Rstudio, usage for daily work

• Other uses of R for graphics, reports, general automation in R without Statistics by David Keyes

• datavis with R: Cookbook for creating popular graphs. Suggests graphs for each scenario, high customization of graphs, best practices. Skim particular sections for best use. by Robert Kabacoff

Open textbooks on R and statistics

• Modern statistics with R

• Statistical Inference via Data Science : A ModernDive into R and the Tidyverse - moderndive

  > We have intentionally minimized the number of mathematical formulas used. Instead, you’ll develop a conceptual understanding of statistics using data visualization and computer simulations. We hope this is a more intuitive experience than the way statistics has traditionally been taught in the past and how it is commonly perceived.

• Tidy Modeling with R -Tmwr : how to use tidymodels packages ; develop good statistical practice

• An introduction to data analysis: R-data, plots; general logic of statistical inference, assess statistical analyses from literature

• Foundations in Statistical Reasoning (Kaslik) - libretexts

• Statistical Thinking for the 21st Century (Poldrack) : Libre text - Simulation, bayesian?

• Introduction to Statistics with R : Libre text – lot of basics, hypothesis testing?

Open online courses

• Reproducible research in R: Beginner 3 day workshop. R, version control, data wrangling, reproducible documents, visualization
  • Also has: Intermediate: focus on speed, functional programming, join data, pivots;
  • And advanced modules: collaboration, automatic analysis pipelines, publicizing via a website
• Allison horst’s Intro data analysis and stats : Google slides and exercise material available
• Intro to R for biologists : Check out syllabus, _how to get course material?
• Computational biology foundations - syllabus
  • Check out any courses from this nice faculty teaching R for 10 years, interested in incorporating LLMs into teaching from this question on posit. Faculty : boris steipe
• Rfun/duke: Topics covered
  • Getting started: import data, data wrangling
  • Data wrangling with dplyr
  • Visualization with ggplot2
  • Coding with ChatGPT
  • Tidy data, pivot, join, and iteration (part 1)
  • Functions & {purrr}; iteration part2
  • Regression and tidymodels

Nice slides for basics of R - tinystats/teacups-giraffes-and-statistics course/module 1

Other resources

• Another ggplot worksheet - https://ourcodingclub.github.io/tutorials/datavis/: includes good visualization tips

• Git resources - https://missing.csail.mit.edu/2020/version-control/ ; Saving broken git: ohshitgit

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Classroom policies

Attendance

Since this course requires hands-on R use in class, and values participation, in-person attendance is highly encouraged. However, if you are unable to attend class in-person, please contact me in advance to avail the zoom option. There is no penalty for mussing upto 3 classes, additional absences will proportionally reduce the attendance part of the final grade.

Aside from excused absences, you are permitted to absences for religious holidays that are not included in the list of Federal and Academic holidays. Please let me know if you will be absent due to a religious holiday.

Late submissions

You are automatically entitled to two late submissions throughout the course. Any additional late submissions have a penalty of 10% of the grade for that assignment. If you need any accommodations please email the instructor.

In addition, the policy for religious holidays under Attendance extends into late submissions - if you have a religious holiday that occurs on the day of or the day before an assignment is due, please let me know and I can provide an additional extension.

Communication policy

You are welcome to email me at any time with your questions, concerns, and appointment requests. To ensure that I do not miss your email, please include [course ID]-[short summary of the query] in the email subject. For example, if you are requesting an additional appointment, the email subject should be: BIOS 538: scheduling appointment.

Names and pronouns

If you prefer a different name or gender pronoun than the one displayed on Canvas, please let me know. All students are expected to refer to each other by their correct names and pronouns during class. When addressing groups of people, use gender-neutral language. Here is a resource to assist you with inclusive, respectful language: Pronouns.

Etiquette.

In addition, no racist vocabulary will be permitted in class in any capacity. Please refer to each other with respect.

Needs

If you face any challenges in securing personal or educational resources, such as a safe space and uninterrupted access to computing or the internet, and believe that these challenges may impact your performance in this class, please notify me. I will work with you to ensure that I can assist in any way possible.

Inclement weather policy

In the event of extreme weather conditions that impact your ability to get to class in person, please inform me and I will announce opening the zoom to everyone for that day/class

Academic honesty

Rice Honor Code

In this course, all students will be held to the standards of the Rice Honor Code, a code that you pledged to honor when you matriculated at this institution. If you are unfamiliar with the details of this code and how it is administered, you should consult the Honor System Handbook. This handbook outlines the University’s expectations for the integrity of your academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process.

Attribution policy

Any collaborative work or help received should be duly acknowledge in the report under attributions section. For group submissions, please detail who did what for the assignment/report.

It is ideal to pre-determine how the work will be split by writing down the attribution section initially to prevent any confusion among the group.

Accessibility

Disability resource center

If you have a documented disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with the Disability Resource Center (Allen Center, Room 111, adarice@rice.edu, x5841) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.

Furthermore, if you require course materials to be delivered in an alternate way to online .pdfs, please let me know.

Attributions

This course was taught by Prof. Lydia Beadrout for 6 years and materials from the spring 2023 course were heavily relied upon to create the current course. Classroom policies for this page were verbatim borrowed from my wife, Swetha Sridhar.