Announcements
To contact me (24/24):
email: darlene.goldstein@epfl.ch
tel/sms/whatsapp/signal: 079 427 2501
skype: darlenegoldstein
Course format:
Although we are currently allowed to attend the course on campus, ALL LECTURES WILL ALSO BE PRERECORDED (from a different year) , since some of you may be unable to attend in person from time to time. Please follow the lecture before the office hours or lab time so that you can ask any additional questions then.
Course / Lab times:
Most weeks we will have lecture Thursday 8.00 - 10.00 and lab 10.15 - 12.00, both in MA B1 11. Some weeks we will have a shorter lab time and start lecture later.
Office hours:
I will be available for your questions each week Thursday 12.00-13.00 (after class) in my office MA B1 477 and also by appointment (in person or zoom).
Course language:
This course is given in English, but feel free to speak in either English or French.
Organization: Your course note will be based on 2 short TP reports (1/2 point each) and an individual report (up to AT MOST 10 pages, not including references or large figures, 5 points). You will report on an analysis of genomic (microarray) data where there will be 2 tasks: to identify genes that are differentially expressed between 2 conditions and to carry out a cluster analysis to identify (potentially novel) subgroups.
The purpose of this course is to help you to learn something without too much stress!! The TP reports get credit as long as you turn in a reasonable effort. You can also do the individual report twice: a preliminary version, which will be commented according to the posted criteria, then a final version, where you can incorporate the comments, due at the end of the semester. Only the final version will count towards your course note. The deadlines will be posted on the course moodle page.
email: darlene.goldstein@epfl.ch
tel/sms/whatsapp/signal: 079 427 2501
skype: darlenegoldstein
Course format:
Although we are currently allowed to attend the course on campus, ALL LECTURES WILL ALSO BE PRERECORDED (from a different year) , since some of you may be unable to attend in person from time to time. Please follow the lecture before the office hours or lab time so that you can ask any additional questions then.
Course / Lab times:
Most weeks we will have lecture Thursday 8.00 - 10.00 and lab 10.15 - 12.00, both in MA B1 11. Some weeks we will have a shorter lab time and start lecture later.
Office hours:
I will be available for your questions each week Thursday 12.00-13.00 (after class) in my office MA B1 477 and also by appointment (in person or zoom).
Course language:
This course is given in English, but feel free to speak in either English or French.
Organization: Your course note will be based on 2 short TP reports (1/2 point each) and an individual report (up to AT MOST 10 pages, not including references or large figures, 5 points). You will report on an analysis of genomic (microarray) data where there will be 2 tasks: to identify genes that are differentially expressed between 2 conditions and to carry out a cluster analysis to identify (potentially novel) subgroups.
The purpose of this course is to help you to learn something without too much stress!! The TP reports get credit as long as you turn in a reasonable effort. You can also do the individual report twice: a preliminary version, which will be commented according to the posted criteria, then a final version, where you can incorporate the comments, due at the end of the semester. Only the final version will count towards your course note. The deadlines will be posted on the course moodle page.
In order to give you time to work on your reports, there will be no in-person lectures and mainly optional topics toward the end of the course. These 'extra' topics are NOT required, there are slides (and possibly videos) in case you are interested. There is no penalty associated with not following them.
Resources:
A useful book for both statistics and R:
- A Handbook of Statistical Analyses Using R, 3rd edition. Torsten Hothorn and Brian S. Everitt. CRC Press.
Some resources to get you started with R, R Studio and R Markdown:
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