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

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

Boris Steipe

 

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 the R_Exercises-BasicSetup project 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):
  • Data
  • Sequence
  • Structure
  • Phylogeny
  • Function

 

Notes