ANALYSIS OF BIOLOGICAL DATA BIOL4062/5062

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ANALYSIS OF BIOLOGICAL DATABIOL4062/5062

• Hal Whitehead

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• Introduction
• Assignments
• Tentative schedule
• Analysis of biological data

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Introduction

• Instructors
• Purpose of class
• Related classes
• Books
• Computer programs

http//myweb.dal.ca/hwhitehe/BIOL4062/handout4062
.htm
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• Instructor Hal Whitehead
• LSC3076 (Ph 3723 email hwhitehe_at_dal.ca)
• Best times 800-900 a.m.
• Teaching Assistant ?
• Other instructors
• Dr David Lusseau

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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

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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

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Some books (on reserve)

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

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Computer programs

• MINITAB
• SPSS
• SYSTAT
• SAS
• MATLAB (Statistics toolbox)
• S-plus
• R

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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

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Assignments

• Type 1
• artificial data sets for trying different
  techniques
• Type 2
• real data set to try a real analysis

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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

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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

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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

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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

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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

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Type 2 assignment

• Minimum sizes of data set (ask Hal for exceptions
  or in case of uncertainty)
• For undergraduates (BIOL4062)
• gt50 units x gt3 variables
• For graduates (BIOL5062)
• gt50 units x gt5 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)

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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 web

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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

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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
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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

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Tentative schedule
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SYSTAT demo. at end of lectures
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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

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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, ...

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Development of biological data analysis

• gt1850 Displays
• gt1900 ANOVA's, regression, correlation
• without computers
• gt1930 Non-parametric methods
• gt1970 Multiple regression and multivariate
  analysis
• matrix algebra using computers
• gt1980 Robust methods bootstraps, jackknives,
  permutations
• need powerful computers

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Garbage in gt Garbage out

• Good data Errors gt Garbage in gt Garbage out
• Check data entry
• Good data Errors in routine gt Garbage out
• Check results, run routines on data with known
  answer,
• run on 2 routines
• Good data Wrong model gt Garbage out
• Think about, read about and discuss model

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Hypothesis Testing Data 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

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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

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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

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Next class

• Inference in ecology and evolution
• Null hypothesis statistical tests
• Effect size statistics
• Bayesian statistics
• Information theoretic model comparisons

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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