Officially none BUT here in reality ...
Statistics: you should have already taken university level introductory statistics course.
Biology: no requirements, but you are expected to learn things like the difference between a DNA and RNA and a gene and a genome.
R: no experience required but be prepared to do a lot of self-guided learning. Go ahead and start now by installing R and the HIGHLY RECOMMENDED "integrated development environment" (IDE) RStudio! Students are expected to run R on their own computer or a computer they have plenty of access to and control over. The best set-up, if possible, is to bring your own laptop to the computing seminars.
Homework: two assignments worth 20 points each. Homework #1 due Thurs Feb 26. Homework #2 due Fri March 27. Instructions for how to submit your work will be posted when homework is assigned (see calendar below).
Peer review: you will be reviewing, commenting on, and marking other students’ assignments. This is a mandatory part of the course and you will be marked on your peer reviews (5 points for each HW, i.e., 10% of overall course mark). Additional guidelines will be given when homework is assigned (see calendar below)
Group project: groups formed and projects conceived during January/February (see calendar below). Primary deliverable is a poster, presented in last class meeting (Wed April 8). Each student also produces a short report. 40 points. More information will be posted shortly.
Participation: 10 points for "other", including participation in class, seminars, and discussion forum, engagement with small computing exercises.
<style type="text/css"> table { max-width: 70%; } </style>Time : Mon Wed 9:30 - 11am
Location : ESB 4192
Calendar
date notes instructor
jan-05 mon lect01: Intro to course PP
jan-07 wed lect02: Review of probability and statistical inference, 1 of 2 GCF
jan-12 mon lect03: Review of probability and statistical inference, 2 of 2 GCF
jan-14 wed lect04: Exploratory analysis PP
jan-19 mon lect05: Data QC and preprocessing GCF
jan-21 wed lect06: Statistical inference: two group comparisons GCF
jan-23 fri Project groups should be formed
jan-26 mon lect07: Statistical inference: more than two groups GCF
jan-28 wed lect08: Statistical inference: linear models with 2 categorical covariates GCF
jan-30 fri Initial project proposals due.
feb-02 mon lect09: Statistical inference: linear models including a quantitative covariate GCF
feb-04 wed lect10: Large scale inference: Empirical Bayes, limma GCF
feb-06 fri HW 1 posted; due Thurs Feb 28
feb-09 mon no class; Family Day
feb-11 wed lect11: Large scale inference: multiple testing GCF
feb-13 fri feedback to groups re: initial project proposals. Each group will be assigned an instructor or TA + instructor pair for extra support.
feb-16 mon no class; mid-term break
feb-18 wed no class; mid-term break
feb-23 mon lect12: Analysis of RNA-Seq data, 1 of 2 PP
feb-25 wed lect13: Analysis of RNA-Seq data, 2 of 2 PP
feb-28 thu HW 1 due
mar-02 mon lect14: Analysis of epigenetic data, focus on methylation TBA
mar-04 wed lect15: Principal component analysis PP
mar-06 fri HW 2 posted; due Fri Mar 27
mar-09 mon lect16: Cluster analysis SM
mar-11 wed lect17: Classification SM
mar-16 mon lect18: Cross validation. Regularization SM
mar-18 wed lect19: Regularization (cont'd). Missingness SM
mar-23 mon lect20: Analysis of gene function, 1 of 2: Gene set analysis PP
mar-25 wed lect21: Analysis of gene function, 2 of 2 PP
mar-30 mon lect22: Resampling and the bootstrap SM
apr-01 wed lect23: Guest lecture TBA
apr-06 mon no class; Easter Monday
apr-08 wed lect24: Poster session all
Time: Wed 12pm - 1pm (but welcome to come after class around 11am)
Location: ESB 1042 and 1046
Calendar
date notes TA
jan-05 mon sm00: No seminar meeting; visit STAT 545A page to review/learn about R/Rstudio Set Up and basics of R students work on their own
jan-07 wed sm01a: 11am-12pm: Getting ready to use GitHub in STAT540, borrowed from STAT545A students work on their own
jan-07 wed sm01b: 11am-12pm: Git(hub) Intro & Exploring a small gene expression dataset Evan
jan-07 wed sm01c: 12pm-1pm: Molecular biology/genetics 101 Alice
jan-14 wed sm02a: Markdown Evan
jan-14 wed sm02b: Probability and simulations (part I) Evan
jan-14 wed sm02c: Probability and simulations (part II) Alice
jan-21 wed sm03: R graphics AND knitr, R markdown, and git(hub) TBA
jan-28 wed sm04: Two group testing and data aggregation TBA
feb-04 wed sm05: Fitting and interpretting linear models (low volume) TBA
feb-11 wed sm06: Fitting and interpretting linear models (high volume), limma package TBA
feb-18 wed no class; mid-term break na
feb-25 wed sm07: RNA-Seq analysis TBA
mar-04 wed sm08: Methylation analysis TBA
mar-11 wed sm09: Clustering and PCA TBA
mar-18 wed sm10: Supervised learning, cross validation, variable selection TBA
mar-25 wed sm11: TA office hours during seminar time ... group project work TBA
apr-01 wed sm12: TA office hours during seminar time ... group project work TBA