1. ANALYSIS OF BIOLOGICAL DATA BIOL4062/5062 Hal Whitehead

2. Introduction Assignments Tentative schedule Analysis of biological data

3. Introduction Instructors Purpose of class Related classes Books Computer programs http://myweb.dal.ca/~hwhitehe/BIOL4062/handout4062.htm

4. Instructor: Hal Whitehead –LSC3076 (Ph 3723; email hwhitehe@dal.ca) –Best times: 8:00-9:00 a.m. Teaching Assistant: ? Other instructors –Dr David Lusseau

5. Why “Analysis of Biological Data”? Biologists –increasingly using quantitative techniques –to analyze larger and larger data sets –need skills in data analysis especially in broad area of ecology BIOL4062/5062 –introduce techniques for analysis of biological data –emphasis will be on the practical use and abuse of techniques, not derivations or mathematical formulae –in assignments students explore real and realistic data sets

6. Related classes Design of Biological Experiments (BIOL4061/5061) –most useful for those who work with systems that can be manipulated Courses in Statistics –more emphasis on mathematical sides

7. Some books (on reserve) Legendre, L. and P. Legendre. Numerical Ecology (2 nd edition). Elsevier (1998) Manly, B.F.J. Multivariate statistical methods: a primer (2 nd edition). Chapman & Hall (1994) Other books: –Many, do not need to be right up to date

8. Computer programs MINITAB SPSS SYSTAT SAS MATLAB (Statistics toolbox) S-plus R Good, comprehensive packages, can do analyses for this class More sophisticated and powerful, harder to use

9. Computer programs MINITAB * † SPSS * † SYSTAT † SAS * † MATLAB (Statistics toolbox) S-plus (freely available at Dal.?) R † (freely available on the web) * on GS.DAL.CA † in Biology-Earth Sciences computer lab Support from ? Support from Hal

10. Assignments Type 1 –artificial data sets for trying different techniques Type 2 –real data set to try a real analysis

11. Type 1 assignments Five assignments, sent by email (next few days) Each 10% final mark Artificial but realistic data sets –Different data sets to each student, but structurally similar –More analyses expected for graduate students (BIOL5062) Analyze using a computer statistical package

12. Type 1 assignments Hand in a short write-up, explaining clearly: –what you did –what you found –what you think the results might mean biologically Beware of: –Rubbish! Check the results against patterns in the original data to make sure they make sense. –Over-interpreting the results –Not answering the questions posed

13. Type 1 assignments Five assignments: –Multiple regression 10% –Log-linear models 10% –Principal components analysis 10% –Discriminant function analysis 10% –Cluster analysis, multidimensional scaling, network analysis 10%

14. Type 2 assignment Find a biological data set, and then analyze it The analysis should not be: –part of past, present, or future Honours, MSc or PhD thesis, or used for another class: self-plagiarism –that, or repeat that, done by someone else: plagiarism

15. Type 2 assignment The analysis can –use same data as in thesis or another course, but totally different analysis –use data collected by your supervisor, or someone else, but you should ask them –use a data set that you find on the web, or somewhere else, but you should check that it is OK –be submitted for publication, but you must check that you have all necessary permissions

16. Type 2 assignment Minimum sizes of data set (ask Hal for exceptions or in case of uncertainty): –For undergraduates (BIOL4062): >50 units x >3 variables –For graduates (BIOL5062) >50 units x >5 variables either, two types of variables – e.g. “Dependent; Independent”; “Species; Environment” or, link two data sets with one at least as large as the undergraduate data set Must address at least 3 biological questions (BIOL4062), or 4 questions (BIOL5062)

17. Type 2 assignment (4 steps) a) Short meeting with Hal or *** to discuss your proposed data set and proposed analysis: feedback –bring draft of 2b assignment b) Description of data set and proposed analysis. –where it came from –its structure(s) (number of variables, units, names of variables, types of variables,...) –proposed biological questions –proposed analytical methods –possible problems –Example on webExample on web

18. Type 2 assignment (4 steps) c (i) Presentation of results to the class by graduate students –biological questions being addressed –brief description of the data set –how you analyzed it –conclusions –Example in Class c(ii) Undergraduate students should go to graduate presentations and will be tested on general issues arising from them on last day

19. Type 2 assignment (4 steps) d) Write-up of your analysis as for a scientific journal paper –Max 5 pages (4062) or 7 pages (5062) single-spaced excluding references, tables, figures –Explain biological question, methods in sufficient detail for someone to replicate them, problems, and biological conclusions –Show graphically, or in tables, the major effects Do not just present summaries of ordinations or significance levels of hypotheses tests –Introduction and Discussion can be shorter and less detailed than in published paper sufficient to give a good feel for biological issue being examined and the potential biological significance of the results Example on web

20. Type 2 assignment Marks 2b Description of data set and proposed analysis 5% 2c 15% – (i) Presentation of results by graduate students (BIOL5062) – (ii) Test on general principles from graduate student presentations (BIOL4062) 2d Write-up of results 30%

21. Tentative schedule

22. SYSTAT demo. at end of lectures

23. Analysis of Biological Data Types of biological data History (very abbreviated!) The process of biological data analysis –why garbage may come out Hypothesis testing and data analysis –assumptions –other issues

24. Types of biological data Morphometric Community ecology –organism distribution and environmental variation Genetic data for ecological and evolutionary questions Population data for management, conservation, evolutionary questions Behavioural, physiological,...

25. Development of biological data analysis >~1850 Displays >~1900 ANOVA's, regression, correlation –without computers >~1930 Non-parametric methods >~1970 Multiple regression and multivariate analysis –matrix algebra using computers >~1980 Robust methods: bootstraps, jackknives, permutations –need powerful computers

27. Garbage in => Garbage out Good data + Errors => Garbage in => Garbage out –Check data entry Good data + Errors in routine => Garbage out –Check results, run routines on data with known answer, –run on 2 routines Good data + Wrong model => Garbage out –Think about, read about and discuss model

28. Hypothesis TestingData Analysis Hypothesis Experimental Design Experiment Analysis Conclusion [ANOVA, T-test] Agriculture Experimental ecology Physiology Animal behaviour Data Collection Data Analysis Hypothesis [scatter plots, box plots, most multivariate analyses] Fisheries Community ecology Paleontology

29. Some assumptions Normality –can only be properly examined on large data sets –mainly a problem on small ones –an important issue for hypothesis testing –normality desirable in data analysis Linearity –makes hypothesis testing easier –makes data analysis easier Independence –major problem for hypothesis testing –no problem, or advantage, in data analysis Transform data or use non-linear or non-parametric methods

30. Other issues in data analysis Missing data –Often present in ecological data Outliers –What do we do with apparent outliers? –Remove them? Multiple comparisons –Major issue with hypothesis testing –Not an issue with data analysis although: Patterns appear in random data

31. Next class: Inference in ecology and evolution: –Null hypothesis statistical tests –Effect size statistics –Bayesian statistics –Information theoretic model comparisons

33. Performance in BIOL4062/5062 Graduate students (BIOL5062) –some do well with rather little effort –some do well with a lot of effort Undergraduate students (BIOL4062) –most do well with some effort adequate statistical background –some do poorly inadequate statistical background or effort