All materials of the course are
licensed by the course instructors under the Attribution-NonCommercial
4.0 International license.
STAT 540: Statistical Methods for High Dimensional Biology
2024-2025 Winter Term 2 (January 7, 2025 - April 8, 2025)
STAT 540 is a 3 credit course with a mandatory computing seminar
Cross-listed as STAT 540, BIOF 540, GSAT 540
We respectfully recognize that the University of British Columbia Vancouver campus is located on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people. Please take a moment to learn about the territory you are occupying by visiting this interactive indigenous land map.
For more info on the Teaching team, including brief bios, see the ‘People’ pages on this website (linked below). Links for connecting to recurring virtual office hours will be shared via Canvas.
Keegan Korthauer, PhD (She/Her/Hers)
Email: keegan@stat.ubc.ca
Virtual office hours: By appointment
Yongjin Park, PhD (He/Him/His)
Email: ypp@stat.ubc.ca
Virtual office hours: By appointment
Asfar Lathif (He/Him/His)
Email: asfar.lathif@ubc.ca
Virtual office hours: By appointment
JJ Hum (They/Them/Theirs)
Email: jj.hum@ubc.ca
Virtual office hours: By appointment
Course announcements will be posted on Canvas. You are responsible for checking it regularly.
Piazza (in Canvas) for posting questions (e.g. topics discussed in class, questions about course organization, assignment clarifications, if you are stuck on an assignment and need help). This ensures the message can be seen by the entire teaching team, and that others in the class who might have the same question can learn from responses. You are also welcome to share your input on questions posted by others.
For private matters (e.g. requesting an extension, scheduling appointment for office hours), please contact the Teaching team by email (listed above). Do not use email to ask general questions described above.
In your final project groups, we expect you to (1) arrange regular meetings either in person or virtually and (2) make use of your team’s Piazza group.
This interdisciplinary course requires a broad range of skills at the interface of statistics, molecular biology / genomics, and computing. While you may have some background in one or more of the following areas, you are not expected to be an expert in all. That said, to be successful in the course, you may need to spend a bit more time in the areas that you have less experience in. Although there are no official prerequisites for the course, here is a list of useful skills to bring into the course and/or learn along the way.
You will have three individual assignments, six seminar submissions (one divided into two parts), and one group project. Deadlines are all by 11:59 pm (Pacific time) on the due date. Any submission or modification after the due date will not be graded unless you have requested an extension. If you anticipate having trouble meeting a deadline and need to request an extension/academic concession please reach out in advance via email to the instructors.
For more detail on each of these assignments, see the assignments page (the header of each assignment on this page points to the relevant section of the assignments page). Also refer to this visual overview of the timeline.
For detailed instructions on how to work and submit assignments for this course, please see the Submission Guide.
| Assignment | Due Date |
|---|---|
| Intro Assignment | Thu 23 January 2025 |
| Assignment | Due Date |
|---|---|
| Seminar 1 | Fri 10 January 2025 |
| Seminar 2a & 2b | Fri 17 January 2025 |
| Seminar 3 | Fri 24 January 2025 |
| Seminar 4 | Fri 31 January 2025 |
| Seminar 5 | Fri 07 February 2025 |
| Seminar 6 | Fri 14 February 2025 |
| Seminar 7 | Fri 28 February 2025 |
| Seminar 8 | Fri 14 March 2025 |
| Seminar 9 | Fri 21 March 2025 |
| Seminar 10 | Fri 28 March 2025 |
| Seminar 11 | Fri 11 April 2025 |
See here for detailed instructions and rubric.
| Assignment | Due Date |
|---|---|
| Paper Critique | Thu 20 February 2025 |
| Assignment | Due Date |
|---|---|
| Analysis Assignment | Thu 06 March 2025 |
| Assignment | Due Date |
|---|---|
| Initial Proposal | Mon 27 January 2025 |
| Final Proposal | Tue 11 February 2025 |
| Progress Report | Tue 11 March 2025 |
| Final Report | Tue 01 April 2025 |
| Presentation Day 1 | Tue 01 April 2025 |
| Presentation Day 2 | Thu 03 April 2025 |
| Presentation Day 3 | Tue 08 April 2025 |
| Individual & Group Evaluation | Fri 11 April 2025 |
If you anticipate having trouble meeting a deadline and need an academic concession, please reach out in advance via email to the instructors. Here is a template you can use for a self-declaration.
If you miss class, we suggest you to:
Not only is academic integrity is essential to the successful functioning of our course, but adopting best practices will benefit you in your research practice. Make sure you understand UBC’s definitions of academic misconduct and its consequences. Policy guidelines can be found here.
The academic enterprise is founded on honesty, civility, and integrity. As members of this enterprise, all students are expected to know, understand, and follow the codes of conduct regarding academic integrity. At the most basic level, this means submitting only original work done by you and acknowledging all sources of information or ideas and attributing them to others as required. This also means you should not cheat, copy, or mislead others about what is your work; nor should you help others to do the same. For example, it is prohibited to: share your past assignments and answers with other students; work with other students on an assignment when an instructor has not expressly given permission; or spread information through word of mouth, social media, websites, or other channels that subverts the fair evaluation of a class exercise, or assessment.
Do your own work. All individual work that you submit should be completed by you and submitted by you. Do not receive or share completed coursework with students who take the course in another term.
Acknowledge others’ ideas. Scholars build on the work of others, and give credit accordingly. You are expected to acknowledge the use of any outside sources (e.g. from the literature, or AI tools) or inside sources (e.g from your peers).
Learn to avoid unintentional plagiarism. Visit the Learning Commons’ guide to academic integrity to help you organize your writing as well as understand how to prevent unintentional plagiarism.
At any time: if you are unsure if a certain type of assistance is authorized, please ask us.
Tools that use artificial intelligence algorithms trained on large datasets to generate content, such as ChatGPT, have become widely accessible (these are often referred to as “generative AI tools”). If you choose to use generative AI tools to complete coursework, you must disclose your use of them. This disclosure must be included at the top of the submission file for the assignment in which the generative AI tool was used. The disclosure should include the name of the tool and a brief description of how it was used.
Further, if you choose to use generative AI tools for coursework, you should be aware of the following:
Privacy implications: Information you input into some tools, along with some device data such as your IP address, may be sent to servers outside of Canada, which may put your intellectual property and/or personal information at risk. For more information, please refer to UBC’s Privacy Impact Assessment for generative AI tools.
False or misleading information: Generative AI tools can produce biased, false or misleading content, because of the nature of the training datasets. They are designed to produce the most statistically plausible text result, not necessarily the correct one, and they cannot take responsibility for what they produce. You, on the other hand, must take responsibility for the accuracy and integrity of your written work: therefore, you should not consider the output of a generative AI tool to be reliable unless verified by information in scholarly sources, and the information included in your assignments must be found in and attributed to scholarly sources.
Ultimately, this course is designed to help students practice analytical skills, and investigate a well-reasoned scientific question. Using generative AI tools to produce entire written and coding assignments will stifle independent thinking and undermine development of these valuable skills. If you have questions around the acceptable or unacceptable use of generative AI tools, we encourage you to speak to the instructors.
Violations of academic integrity (i.e., misconduct) lead to the breakdown of the academic enterprise, and therefore serious consequences arise and harsh sanctions are imposed. For example, incidences of plagiarism or cheating may result in a mark of zero on the assignment and more serious consequences may apply if the matter is referred for consideration for academic discipline. Careful records are kept to monitor and prevent recurrences. Any instance of cheating or taking credit for someone else’s work, whether intentionally or unintentionally, can and often will result in at minimum a grade of zero for the assignment, and these cases will be reported to the Head of the Department of Statistics and Associate Dean Students of the Faculty of Science.
This course requires students to work on github.com and submit work through the Gradescope platform. Please be advised that the material and information you put on GitHub will be stored on servers outside of Canada. Data used for these tools may not be protected, as they have not gone through a Privacy Impact Assessment (PIA) and been identified as FIPPA compliant. When you access GitHub, you will be required to create an account. While this tool has a privacy policy, UBC cannot guarantee security of your private details on servers outside of Canada. Please exercise caution whenever providing personal information. Please feel free to contact UBC (access.and.privacy@ubc.ca) or the support team for GitHub if you have any questions. Gradescope has been verified by UBC’s Privacy Impact Assessment (PIA) process, and you can view the Gradescope privacy policy here.
We strive to provide a learning environment where all students can succeed. Please join me in contributing to a classroom culture where everyone feels valued. If you encounter an issue that presents a barrier to your learning, please reach out to us. You can also contact the Ombudsperson for help: https://ombudsoffice.ubc.ca. If you have a documented disability that affects your learning, you may contact the UBC Centre for Acessibility: https://students.ubc.ca/about-student-services/centre-for-accessibility, and contact us as soon as possible if you think you may require accommodation options for course work. Use of class time Discussions during our scheduled class time are intended to relate to the learning outcomes for the course.
Your mental health and wellbeing impacts your academic performance. Sometimes it is possible to manage challenges on your own, while other times you may need support. UBC is committed to providing student mental health and wellbeing resources, strategies, and services that help you achieve your goals. Visit https://students.ubc.ca/health for more information.
Please follow the current UBC Communicable Disease Prevention Guidelines regarding self-monitoring, and staying home if you are sick. Although masks are no longer required on campus, please respect the choices of your fellow students and the instructional team who may continue to wear masks.
In-person, on campus activities may need to be cancelled due to issues such as weather conditions (e.g., snow). The most up-to-date information about cancellations will be posted on ubc.ca. Please check ubc.ca often during times when an extreme weather event could disrupt our course activities. Here is what you can expect in the event an in-person lecture or lab session is cancelled:
Depending on the nature of the planned in-class activities, class may take place over Zoom (in this case, Zoom link will be posted on Canvas), or an alternate activity may be posted on Canvas for you to complete before the next scheduled class. We will communicate via Canvas to announce the specifics for each case that arises as soon as we can.
All materials of the course are
licensed by the course instructors under the Attribution-NonCommercial
4.0 International license.