[Pre-course survey, Piazza, Scribing preference, Logistics, Course schedule and materials, Assignments,Presentation schedule]
With social-economic development, people are increasingly caring about health. Consequently, in the field of genomics and healthcare, especially personalized genomics and precision medicine, we have accumulated a tremendous amount of data, which are waiting to be analyzed. This course is designed to equip students with the ability to analyze such data, which would benefit both the students’ personal development and society. In the course, we will cover high-throughput experimental methods, standard data processing pipelines, sequence alignment and mapping, foundational concepts of data analytics, data exploration and visualization, clustering and classification, dimension reduction, and their applications in personalized genomics and precision medicine. For personalized genomics, we will also cover the integration of heterogeneous sequencing and non-sequencing data, single-cell data analysis, multi-omics analysis methods, and cancer genomics. For precision medicine, we will cover protein-RNA interactions, biological graph analysis, and a gentle introduction to biomedical imaging and electronic health records.
Lecturer:
TA:
Wednesday: 9:30am-11:15am, MMW 703
Friday: 9:30am-10:15am, MMW 703
Friday: 10:30am-11:15am, MMW 703 (Tutorial)
In-person. Slides will be available the day before the lecture.
Blackboard is the main software to manage the course, and grading will be through Blackboard. We will use Piazza (BMEG3105) for discussion. You can ask questions and discuss on Piazza, even anonymously. For personal matters, please use the private post to the instructor and the TA. You are also very welcomed to send emails to the teaching team.
Bonus (up to 6%):
All exams and quizzes are open-book. You are allowed to take any paper-based materials. However, no phone or computer is allowed. Other communication tools are also not allowed. Discussion is not allowed.
You can use AI tools including ChatGPT in the project to polish your report. However, you are required to submit both your own version and the one polished using AI tools. You are required to make it clear how you used AI tools and which part in the report. We will grade on the one you would like us to grade, but if you do not hand in your own version, we would not consider the submission complete.
Python (the TA will prepare a recitation class to introduce it, mainly for the non-grading homework and your project) or any other languages that you are familiar with.
For python, we suggest you to use Colab.
The programming assessments include a non-graded programming assignment (5%) and the implementation in the project (5%). The bonus is sufficient to cover all the programming credit in the project, if you really do not want to try hand-on experiments at all. We do encourage you try.
Please sign up the scribing preference. We should have at least one student for each lecture. We may adjust the assignment if necessary. Notice that your note and scribing will be posted online, for others reference. You can choose to remove your name or not. Deadline for signing the scribing: 11:59 pm on 12th Sep. After that, the Google sheet will be closed. For students assigned to the first two lectures, you have additional one week to submit the scribing.
We will have individual projects. You can propose your project to us and seek our help, or we will predefine some projects for you to choose from. Some potential project topics:
Both a midterm report (1 page) and a final report should be submitted.
Each student will have 6 late days to turn in the assignments, which can be used on A1, A2, A3, PA1, and the project midterm report. They cannot be used on the project final report and the scribing note. A maximum of 2 late days can be used for each assignment. Grades will be deducted by 25% for each additional late day.
Deadline for each survey: 11:59pm on the day before the next lecture. We do this because we could have time to answer the questions you mentioned in the survey. Please enter a “1” in the Google sheet: Survey results, once you have finished one survey. Usually, we will trust the 1s you fill in the Google sheet. But we will check the things in detail if the number of survey forms we received and the number of 1s on the Google sheet is not consistent.
| Lecture | Date | Location | Topic | Slides | Notes | Reading | Important dates (All due at 11:59 pm) |
|---|---|---|---|---|---|---|---|
| 1 | Sep 6 (Wed) | MMW703 | Introduction | Lec-1 | Course outline | ||
| 2 | Sep 8 (Fri) | MMW703 | Data & Python | Lec-2 | Tut-1 | PA0 posted | |
| 3 | Sep 13 (Wed) | MMW703 | Sequence and DP | Lec-3 | note1, note2, note3, note4 | Chapter 2&3 | |
| 4 | Sep 15 (Fri) | MMW703 | DP | Lec-4 | note1, note2, note3, note4 | Chapter 2&3 | A1 posted |
| 5 | Sep 20 (Wed) | MMW703 | Assembly & Mapping | Lec-5 | note1, note2, note3, note4, note5 | Sample code, RNA-seq analysis, intro to python tutorial, anaconda starter guide, conda cheatsheet | PA0 due |
| 6 | Sep 22 (Fri) | MMW703 | Data exploration | Lec-6 | note1, note2, note3, note4 | Python for DA, Sample code, Sample code-2 | |
| 7 | Sep 27 (Wed) | MMW703 | Clustering | Lec-7 | note1, note2, note3, note4 | Data mining book, Sample code, Sample code-2, intro to pandas&numpy tutorial | A1 due |
| 8 | Sep 29 (Fri) | MMW703 | Classification | Lec-8 | note1, note2, note3 | Tut-2, Data mining book, Correlation | |
| 9 | Oct 4 (Wed) | MMW703 | Classification & Perf evaluation | Lec-9 | note1, note2, note3 | Data mining book | A2 posted |
| 10 | Oct 6 (Fri) | MMW703 | Perf evaluation | Lec-10 | note1, note2, note3 | Tut-3, Data mining book | |
| 11 | Oct 11 (Wed) | MMW703 | Dim reduction | Lec-11 | note1, note2, note3 | PML book | |
| 12 | Oct 13 (Fri) | MMW703 | NN | Lec-12 | note1, note2 | D2L book | |
| 13 | Oct 18 (Wed) | MMW703 | Midterm review | Lec-13 | Quiz, A2 due | ||
| 14 | Oct 20 (Fri) | ERB407 | Midterm | 8:30am-11:15am, Midterm exam | |||
| Module 2 start | |||||||
| 15 | Oct 25 (Wed) | MMW703 | Multi-omics overview | Lec-15 | note1, note2, note3 | Intro to cancer, Cancer genomics | PA1 posted |
| 16 | Oct 27 (Fri) | MMW703 | Cancer genomics overview | Lec-16 | note1, note2 | Intro to cancer, Cancer genomics | |
| 17 | Nov 1 (Wed) | MMW703 | Genomics data analysis | Lec-17 | note1, note2, note3 | Cancer genomics, GATK, GWAS, Epigenetics, ENCODE | |
| 18 | Nov 3 (Fri) | MMW703 | Single cell genomics | Lec-18 | note1 | Tut-4, Current best practice, Tutorial-1, Tutorial-2, Tutorial-3, Clustering challenges , PyTorch Tutorial, GATK-related software installation | |
| 19 | Nov 8 (Wed) | MMW703 | Data visualization | Lec-19 | note1, note2, note3, note3 | Sequence motif, PCA-tSNE-UMAP | Project M-report (Proposal) |
| Module 3 start | |||||||
| 20 | Nov 10 (Fri) | MMW703 | Deep learning | Lec-20 | note1, note2, note3 | Tut-5 (optional), Tut-6, Pytorch examples | |
| 21 | Nov 15 (Wed) | MMW703 | CNN | Lec-21 | note1 | Pytorch examples | PA1 due, A3 posted |
| 22 | Nov 17 (Fri) | MMW703 | EHRs & Text | Lec-22 | EHRs processing | ||
| 23 | Nov 22 (Wed) | MMW703 | Drug & Presentation | Lec-23 | |||
| 24 | Nov 24 (Fri) | MMW703 | Project Presentation | A3 due | |||
| 25 | Nov 29 (Wed) | MMW703 | Course review | Lec-25 | Quiz | ||
| 26 | Dec 1 (Fri) | MMW703 | Project Presentation | Project report |