syllabus.Rmd

---
title: "Syllabus"
description: |
  STAT 540: Statistical Methods for High Dimensional Biology
output:
  distill::distill_article:
    toc: true
    toc_depth: 2
---

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

## Land acknowledgement

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](https://native-land.ca/).

## Course-level learning objectives

  * Perform exploratory data analysis and visualize genomics data
  * Apply tailored statistical methods to answer questions using high dimensional biological data
  * Make your work reproducible, reusable, and shareable
  * Work with real data in a collaborative model

## Selected topics 

  * Basics of molecular genetics/genomic and data collection assays (methods)
  * Basic probability and math foundations
  * Exploratory data analysis and data quality control
  * Normalization, batch correction
  * Causal inference and confounding effects
  * Basic statistical inference ("one gene at a time") – linear models
  * Large-scale inference ("genome-wide") – multiple testing
  * Analysis of microarray, RNASeq, and epigenetics data
  * Principal Component Analysis and clustering (unsupervised machine learning)
  * Classification and cross validation (supervised machine learning)
  * Gene set analysis and gene networks
  * Genome-wide association analysis (GWAS) 
  * Single-cell genomics

## Teaching Team

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.

### Instructors

* [Keegan Korthauer, PhD](about_keegan.html) (She/Her/Hers)  
Email: <keegan@stat.ubc.ca>  
Virtual office hours: By appointment

* [Yongjin Park, PhD](about_yongjin.html) (He/Him/His)  
Email: <ypp@stat.ubc.ca>  
Virtual office hours: By appointment

### Teaching Assistants

* [Asfar Lathif](about_asfar.html) (He/Him/His)  
Email: <asfar.lathif@ubc.ca>  
Virtual office hours: By appointment  

* [JJ Hum](about_JJ.html) (They/Them/Theirs)  
Email: <jj.hum@ubc.ca>  
Virtual office hours: By appointment

## Schedule  

### Lectures (Sec 201)

- Time: Tues Thurs 9:00 - 10:30am
- Location: Building and room number listed on Canvas
- See [Lectures](lectures.html) for lecture materials and schedule 

### Seminars (Sec S2B) 

- Time: Tues 12:30pm - 1:30pm
- Location: Building and room number listed on Canvas
- See [Seminars](seminars.html) for schedule and seminar materials 

## Course communication

#### Announcements 

Course announcements will be posted on Canvas. You are responsible for checking it regularly.

#### General questions

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. 

#### Private matters

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.

#### Group work

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. 

## Prerequisites and Resources

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.

### Statistics: 

- You should have already taken a university level introductory statistics course. 
- [This free online book "Modern Statistics for Modern Biology" by Susan Holmes and Wolfgang Huber](http://web.stanford.edu/class/bios221/book/) is a great reference for introduction or review of many of the statistical concepts that are relevant for this course.
- [This free online book "Data Analysis for the Life Sciences" by Rafael Irizarry and Michael Love](http://genomicsclass.github.io/book/) is another great resource for introduction or review of many of the statistical concepts relevant in this course, with an emphasis on implementation in R.

### Biology: 

- No requirements, but you are expected to learn things like, e.g. the difference between DNA and RNA, and the difference between a gene and a genome.
- See [this video](https://www.youtube.com/watch?v=lSqUDu4zb5k) and this [slideset](https://github.com/STAT540-UBC/resources/blob/main/biology-intro-2017.pdf) for some basic introductory material. 
- Go through the (optional, not for a grade) molecular biology quiz on Canvas
- [This free online book "Concepts of Biology" by Fowler, Roush & Wise](https://openstax.org/books/concepts-biology/pages/1-introduction) is a great resource for biological concepts, in particular chapters 6 and 9
- [This free online book "Biology" by Clark, Douglas & Choi](https://openstax.org/books/biology-2e/pages/1-introduction) goes more in-depth, see Chapters 14, 15, and 16 for material on genetics that is particularly relevant for this course.
- No matter your prior experience, when you come across a new biological concept during the course or in your research, you might need to spend a bit of time 'learning as you go' - this is expected and something I still do regularly in my day-to-day research!

### R: 

- No experience required but be prepared to do a lot of self-guided learning if you haven't taken other courses on R or used it in your research.
- Start now by installing [R](https://cran.r-project.org/) and the HIGHLY RECOMMENDED "integrated development environment" (IDE) [RStudio](https://rstudio.com/products/rstudio/download/) - both are free and open source. 
- You should be able to run R on your own computer during each seminar session.
- If you are new to R, check out [this blog post on getting started with R](http://santina.me/Get-started-with-R/).
- [This free online book "Introduction to Data Science" by Rafael Irizarry](https://rafalab.github.io/dsbook/) is also a great resource for getting more in-depth with R, programming basics, and the tidyverse. In particular see Chapters 1-5:
  - [Chapter 1: Getting Started with R and R Studio](https://rafalab.github.io/dsbook/getting-started.html)
  - [Chapter 2: R Basics](https://rafalab.github.io/dsbook/r-basics.html)
  - [Chapter 3: Programming Basics](https://rafalab.github.io/dsbook/programming-basics.html)
  - [Chapter 4: The tidyverse](https://rafalab.github.io/dsbook/tidyverse.html)
  - [Chapter 5: Importing data](https://rafalab.github.io/dsbook/importing-data.html)
  
### Git/GitHub and R Markdown:

- In this course we'll be using the version control software [Git](https://en.wikipedia.org/wiki/Git) and its web-based hosting and collaborative platform [GitHub](https://en.wikipedia.org/wiki/GitHub). 
- [The online resource "Happy Git and GitHub for the useR" from Jenny Bryan](https://happygitwithr.com/) is a great reference for these tools as we learn them.
- Another helpful git resource is [Hadley Wickham's webinar "Collaboration and time travel- version control with git, github and RStudio"](https://rstudio.com/resources/webinars/collaboration-and-time-travel-version-control-with-git-github-and-rstudio/)
- We'll learn about using [R markdown](https://rmarkdown.rstudio.com/) to generate readable and reproducible reports with code and text, and you'll be using that a lot in this course - see [Chapter 18 of the 'Happy Git' resource: "Test drive R markdown"](https://happygitwithr.com/rmd-test-drive.html).

## Evaluation

You will have three individual assignments, six seminar submissions (one divided into two parts), and one group project. <font color="red">**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**</font>. 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](assignments.html)** (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](timeline.html).

**For detailed instructions on how to work and submit assignments for this course, please see the [Submission Guide](submission_guide.html).**

### [Intro Assignment (5%)](assignments.html#intro-assignment-5)

- An introductory assignment designed to assess basic knowledge of GitHub, R and Rmarkdown

```{r, echo = FALSE, warning =  FALSE}
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(knitr))
assn <- read.csv("course-admin/assn.csv")
assn <- assn %>% 
  select(content,start,Category) %>%
  rename(Assignment = content, `Due Date` = start) %>%
  mutate(`Due Date` = as.Date(`Due Date`, "%Y-%m-%d")) %>%
  arrange(`Due Date`) %>% 
  mutate(`Due Date` = format(`Due Date`, "%a %d %B %Y"))
kable(assn %>% filter(Assignment == "Intro Assignment") %>% select(-Category))
```

### [Seminar completion (30%)](assignments.html#seminar-completion-30)

- You will submit short "deliverables" for each seminar session
- Each seminar session is weighted equally, but the lowest score will be dropped (so that the 10 seminars with highest score will each count for 3% of the final grade). 
- These deliverables give practical experience applying the knowledge that will be helpful on assignments, the project, and your future research.
- Each deliverable is due on the Friday following the TA-led session for that seminar


```{r, echo = FALSE, warning =  FALSE}
kable(assn %>% filter(Category == "Seminar") %>% select(-Category))
```

### [Paper critique (5%)](assignments.html#paper-critique-5)

See [here](critique.html) for detailed instructions and rubric.

```{r, echo = FALSE, warning =  FALSE}
kable(assn %>% filter(Category == "Paper Critique") %>% select(-Category))
```

### [Analysis assignment (20%)](assignments.html#analysis-assignment-20)

- Involves detailed analysis of real data using R
- This assignment will assess your ability to understand and apply the methods learned in class 

```{r, echo = FALSE, warning =  FALSE}
kable(assn %>% filter(Category == "Individual Assignment", Assignment != "Intro Assignment") %>% select(-Category))
```

### [Group project (40%)](assignments.html#final-group-project-40) 

- A semester-long data analysis group project where you will apply the techniques covered in class to a research question of your choosing
- Groups of target size of 4 students will be formed at the beginning of the course
- Important checkpoints during the term (with deliverables): 
    - Initial Proposal
    - Final Proposal 
    - Progress Report
    - Oral presentation 
    - Written Report & GitHub repository
    - Individual and Group Evaluation 
- For more details on the project components and how groups are selected see the [assignments page](assignments.html#final-group-project-40)
- Participation in group projects is assessed via a contribution multiplier - see the [multiplier rubric for details](group_project_rubrics.html#project-contribution-multiplier).
- For detailed grading rubrics of the final project components, see the [final project rubric page](group_project_rubrics.html) and the [final project presentation rubric](course-admin/ProjectPresentationRubric.pdf)


```{r, echo = FALSE, warning = FALSE}
kable(assn %>% filter(Category == "Group Project") %>% select(-Category))
``` 

## Academic Concession {#concession}

If you anticipate having trouble meeting a deadline and need an [academic concession](https://students.ubc.ca/enrolment/academic-learning-resources/academic-concessions), please reach out in advance via email to the instructors. [Here](course-admin/concession_template.pdf) is a template you can use for a self-declaration.

If you miss class, we suggest you to:

- Consult the class resources on the course website
- Use the class Piazza discussion board to discuss missed material
- Come to virtual office hours
- Seek academic concessions, if applicable

## Academic Integrity

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](http://www.calendar.ubc.ca/Vancouver/index.cfm?tree=3,54,111,958).

#### What is academic integrity?

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.

#### What does academic integrity look like in this course? 

* **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](http://learningcommons.ubc.ca/resource-guides/avoiding-plagiarism/) 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.  

#### Use of generative artificial intelligence (AI) tools

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](https://ai.ctlt.ubc.ca/privacy-impact-assessments-for-generativeai-instructional-use-at-ubc/).

* 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.

#### What happens when academic integrity is breached?

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.

## Privacy

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](https://docs.github.com/en/site-policy/privacy-policies/github-privacy-statement), 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](https://www.gradescope.com/privacy).

## Learning Environment & Support

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.

## Health and Safety in the Classroom

Please follow the current [UBC Communicable Disease Prevention Guidelines](https://srs.ubc.ca/health-safety/safety-programs/communicable-disease-prevention-framework/) 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. 

## Extreme Environmental Conditions Contingency Plan

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](https://www.ubc.ca/). Please check [ubc.ca](https://www.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.