Table of contents:
|What this course is and isn't|
|News about the course|
|A rough syllabus (to be improved)|
|Audio recordings of lectures|
|The R language|
|R in this course (and R exercises to be done in class)|
I inherited this course, and I'm not entirely sure what it is supposed to be. Its title implies that it will tell you about advanced statistical methods in genomics. It won't. It will not tell you about:
What is does seem to be is a statistics refresher, for genomics students who have had a “cookbook” statistics course. For those who have not had any statistics, it is a minimal introduction. But I do not think that it can be considered an adequate substitute for a real statistics course. Genome Sciences graduate students ought to have a full statistics course.
There are three levels of statistics course at most universities. Here is a slightly tongue-in-cheek summary of each of them:
These news items have the newest ones last.
The lecture PDFs will be posted here. One from this year and others from last year are linked here now (the latter are marked here as "old").
The lectures will be recorded and made available as WMA and as MP3 files here. They will be recorded at medium quality, the files about 10 Mb in size. Each file has a name which is the date on which it was recorded, such as 20110503.WMA or 20110503.mp3.
|May 3||May 5||May 10||May 12||May 17||May 19||May 24||May 26||May 31||June 2|
There is no textbook for the course. Josh Akey, in last year's web pages, lists some books and a number of on-line statistics texts available free on the web. They are
Josh's 2008 course web pages are excellent, especially his lecture PDFs. Although the order of material is different, they are very much work looking at. They are here
R is a free interactive computer environment (in old-fashioned terms, an "interpreter") that can be used for many purposes. It was originally designed by statisticians (R is a clone of a language called S, which is now commercial). It has many built-in statistics functions, which is why we will use it. (At the main CRAN-R project site there are links to many other analysis packages that can be loaded into R).
R can be downloaded and installed on Windows, Mac OS X, or Linux machines (and some other types as well). It is available at the CRAN-R site here as executables, source code, and many other resources including a terse PDF introductory manual. When using this manual skip over parts that go too deeply into stuff you don't yet understand as there is valuable stuff after that. Come back to the skipped stuff later.
Here are some R resources that you may find helpful:
We will do an R exercise in each class session. Students are expected to bring a laptop with R loaded on it (kudos to this year's class for doing this successfully). I will have exercise sheets for each class, and we will try to do them. As I make them I will post them here.
Homework assignments will be posted here on Thursdays, to be turned in by email to me by the following Thursday:
Who is this guy who is teaching the course, anyway?