Multivariate Statistics in Ecology and Quantitative Genetics SS 2017

Lecture with Exercises (Vorlesung mit Übung)

Instructors

Time and rooms

10. July to 28. July
Lecture: Every day (Mo to Fr) from 9:00 to 10:30
Exercises: Plan the rest of each Tuesday to Friday for working on the exercises. Exercise sessions on these days will usually be from 11 to 11:30 am and from 3:30 to 5:00 pm.
Rooms for the lecture: Mondays, Wednesdays and Fridays: C00.013
Tuesdays and Thursdays: D00.013
Exception: Tuesday, 18. July: E02.023
Exercises: While working on the exercise assignments you can sit in lab room C00.027.

Exam

The make-up exam will be on 14. September between 8:00 and 10:00 am in the grand lecture hall N 00.001 of the biomedical center (green building, Großhaderner Str. 9; together with Bachelor's students who write their statistics make-up exam). To take the make-up exam you need to register by e-mail to Dirk Metzler before 4. September. You will have 90 minutes to answer the questions. Participants can bring non-programmable pocket calculators an their personal assortment of formulas on an A4 sheet. This A4 can have any contents on both sides BUT ONLY IN YOUR OWN HANDWRITING! (no copy, not print-out, not another person's handwriting) Please note: If you fully attend the course (which is required to get the ECTS points) you will need the time between the 11 a.m. and 5 p.m. to solve exercises, which will be given in the morning lectures and discussed in the afternoon courses.

Requirements

Basic knowledge of statistics. If you are not already familiar with the free R software, it may be a good idea to install it on your computer and get a bit familiar with it before the course starts. Some examples (data file FinchesSulloway.txt), R tutorial, Martin's introductory R course.

In the exercises you will practice to analyse data with the methods listed below using the R software. We will discuss the interpretations of the results and problems that may arise during the analysis. In the lectures we will explain the theoretical concepts behind the methods and show how these methods can be applied.

If you would like to discuss analyses of your own datasets during the course, please contact D. Metzler as soon as possible.

Fundamental methods: Multivariate linear regression, Model Selection, Analysis of Variance (ANOVA), Experimental designs (nested, balanced, unbalanced,...), Graphical Inspection of Fitted Models, Generalized Linear Models (GLM), Mixed Effects Models
R-package: lme4

Special Methods for complex ecological datasets:
ANCOVA, Redundancy Analysis, Principal Component Analysis (PCA), Canonical Correspondence Analysis,....
R-package: vegan (see also the ecology task view)

Analysis of Gene Expression Data
Multiple-Testing Problems, Variance-Stabilizing Normalization, Regularization, reconciliation with gene ontologies (GO)...
R-packages: DESeq, vsn, limma, multtest, GOstats

Special Methods in Genetics: Mapping Quantitative Traits Loci (QTLs).
Keywords: Haley-Knott regression, Composite Interval Mapping, Multiple-QTL models,...
R-package: qtl (see also the genetics task view)

Language: English

Course material

Basics on anova: slides
Balanced design ANOVAs, parameter transformations: slides, R-script, exercises sheet 1, abcdx.txt, bp.txt
Linear Models: slides, R commands for Darwin finches, Darwin finches data, exercises sheet 2, bacteria_trainig.txt, bacteria_predict.txt
Generalized Linear Models: slides, exercise sheet 3, simulate_data4glm.R, TbDeerAndBoar.txt
Hotellings T2-test slides, R-script, raspberry.csv, exercise sheet 4, grapes.txt
Principal Component Analysis (PCA): slides, R-script, exercise sheet 5, HeightShoeWeight.txt, Countries.txt, EWU.txt
PCA Regression: slides, R-script, RIKZGroups.txt
Redundancy Analysis (RDA): slides, R-script, exercise sheet 6, HSWoutlier.txt, artificialFishes.txt,
Correspondence Analysis (CA): slides, R-script, MexicanPlants.txt
Canonical Correspondence Analysis (CCA): slides, R-script, exercise sheet 7
Mixed-effects models slides, lme4a.R, mcmcglmm.R, exercise sheet 8, fruits.txt
QTL Mapping slides, R-script, exercise sheet 9

Material will be added during the course. Here is the course material from 2016.

Literature

Introductory statistics with R / Peter Dalgaard (Springer 2002)
Biometry: The Principles and Practice of Statistics in Biological Research (3rd Ed.) / Sokal, Rohlf, (Palgrave Macmillan 1995)
Mixed effects models and extensions in ecology with R / Alain F. Zuur, Elena N. Ieno, N. J. Walker, Anatoly A. Saveliev, Graham M. Smith. (Springer 2009)
Modern applied statistics with S / W. N. Venables ; B. D. Ripley. - 4. ed. (Springer 2002)
A Guide to QTL Mapping with R/qtl / Karl W. Broman & Saunak Sen (Springer 2009)
Analysing ecological data / Alain F. Zuur ; Elena N. Ieno ; Graham M. Smith. (Springer 2007)
Genetics and analysis of quantitative traits / Michael Lynch ; Bruce Walsh. (Sinauer 1998)
Introduction to quantitative genetics / D. S. Falconer and Trudy F. C. Mackay. - 4. ed. (Pearson 1996)
Numerical ecology / Legendre & Legendre (Elsevier 1998)
Statistical Genetics of Quantitative Traits / Wu, Ma & Casella. (Springer 2007)
Bioinformatics and Computational Biology Solutions Using R and Bioconductor / R. Gentleman, V.J. Carey, W. Huber, R.A. Irizarry, S. Dudoit (Eds.). (Springer 2005)
Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics / D. Sorensen, D. Gianola (Springer 2002)
Mixed effects models in S and S-PLUS / Jose C. Pinheiro ; Douglas M. Bates. (Springer, 2004)

Last update: 25. July 2017