Workshops/BCH2024 2017
BCH2024 - Biological Data Analysis with R
This is a new course module and information on this page is currently under development. If you have questions please contact me.
boris.steipe@utoronto.ca
Contents
The Course
BCH2024 - Biological Data Analysis with R is a module in the Focussed Topics offerings of the Department of Biochemistry. It is an intensive, short-course with a focus on practical data analysis. We will meet in six sessions:
- Thursday, January 12 2017 – Data
- (reading data, subsetting, filtering; designing data models)
- Friday, January 13 2017 – Feature extraction
- (descriptive statistics; dimension reduction)
- Monday, January 16 2017 - Modeling
- (linear and non-linear regression; correlation)
- Thursday, January 19 2017 – Graphs and Networks
- (graph representations; graph metrics)
- Friday, January 20 2017 – Clustering and Classification
- (hierarchical and partition clustering; cluster quality metrics)
- Monday, January 23 2017 – Machine Learning
- (common approaches; cross-validation)
Each class meeting will have substantial, required pre-reading and will be complemented with extensive assignments.
General
The amount and density of material requires a very significant time comment.
- Do not take this course if you can't devote time over the Christmas break to go through a series of introductory tutorials.
- Do not take this course if you can't dedicate the full two weeks from January 13 to 23 to it. You need to free your calendar from conferences, lab-presentations, committee meetings and the like.
- Do not take this course if you can't be present for all six class sessions. We are on a tight schedule for evaluations and there will be no make-up opportunities.
Please realize that all available course spots are booked and there is a waiting list. Do not block space that will prevent others from taking the course if you have any doubt that you will take this course in it's entirety.
I repeat: do not enrol in this course if there is any chance you will drop it. This is going to be a hard course with a heavy workload. Be fair to others.
Coordinator
Contact
- All contact will be via a Google group.
- To send mail, click here: mailto:bch2024_2017.
- To visit this forum on the Web, click here: BCH2024_2017.
- Note that this is a list for technical discussions and I expect everyone to follow the standards of communication spelled out here: Netiquette.
Office hours
(Virtual) face to face meetings are by appointment, if required. However, we will be able to resolve almost all issues by e-mail. You will find that discussions by e-mail are both more efficient and effective than meetings. Moreover e-mail discussions leave you with a document trail of what was discussed, can contain links to information sources, and we can share points of general interest more easily with the class.
Dates & Times
Winter term 2017, M R F 17:00 to 19:00.
First class meeting: Thursday, January 12.
Location
- TBD
Prerequisites
Suitable for students without prior programming experience, however you must be willing to put in the time to learn the R programming language within the course.
You must have access to the Internet via your own computer, preferably set up to work through a wireless connection. If at all possible, get a Linux or Mac OSX computer.
Grading and Activities
| Activity | Weight |
| 4 in-class quizzes | 40 marks (4 x 10) |
| 5 hand-in tasks | 50 marks(5 x 10) |
| Participation | 10 marks |
| Total | 100 marks |
- Quizzes will relate to previous weeks' assignments and current weeks' pre-reading.
- Hand-in tasks will cover preparatory tasks and exploratory perspectives; contents to be discussed in class.
- Participation means active, well-prepared contributions to discussions in-class and on the mailing list.
Preparation
You need to acquire the basics over the holidays through a series of tutorials and preparatory reading.
Task:
- Work through the Introductory R Tutorial – this includes installation of R, RStudio and git.
- Read Netiquette for the course mailing list.
- Read "How to write a reproducible example" and "How to make a great R reproducible example".
- In RStudio, create a New Project, cloned from a GitHub repository. The repository URL is
https://github.com/hyginn/R_Exercise-Bioinformatics. Create this in the same way as you created theR_Exercises-BasicSetupproject for the R-tutorial. The scripts in that project are loosely interleaved with the introductory tutorials to bioinformatics below. I will post the scripts as I develop them and let you know when you can pull updated versions. - Work through the following introductory tutorials to bioinformatics (to be updated):
Notes
