PřF:Bi6590 Analysis of biosystematic data - Course Information

Bi6590 Analysis of biosystematic data

Extent and Intensity

2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).

Teacher(s)

Mgr. Petr Šmarda, Ph.D. (lecturer)

Guaranteed by

Mgr. Petr Šmarda, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: Mgr. Petr Šmarda, Ph.D.
Supplier department: Department of Botany and Zoology – Biology Section – Faculty of Science

Timetable

Mon 14:00–16:50 D31/238

Prerequisites (in Czech)

( Bi2030 High. plant phylog. & divers. && Bi2030c High. plant phyl. & div. - pr. )

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 15 student(s).
Current registration and enrolment status: enrolled: 0/15, only registered: 0/15, only registered with preference (fields directly associated with the programme): 0/15

fields of study / plans the course is directly associated with

• Plant Biosystematics (programme PřF, N-BOT)
• Phycology and Mycology (programme PřF, N-BOT)

Course objectives

In this course you can learn main statistical methods (mainly those multidimensional) needed by you biosystematical, taxonomical and phylogenetical research. The main objective is learn you (without redundant mathematics) to understand and to actively use these methods by designing of your experiments, analyses, and sampling. You will train each method yourself in practicals following the theoretical lectures. You should learn how to correctly and effectively form hypotheses, ask research questions; how many, why, from where, and which one samples to collect; how many times and what to measure. You will learn to inspect multidimensional data by ordination diagrams (PCA, PCoA, CVA, NMDS), to test mutually various multidimensional data sets (Mantel test, Procrustes analysis), to construct and to test classifications of your samples, and to find out the most proper identification characters, e.g. useful for identification keys (discriminant analysis). You will get knowledge how to build phenetic and evolutionary trees from morphological and molecular/sequence data (UPGMA, NJ trees, LS trees, minimum evolution trees, maximum parsimony), how to understand and interpret trees.You will learn how to test the phylogenetic dependence of characters, how to correctly calculate correlation and regression with phylogenetically dependent data, and how to model the evolution of characters on trees.

Learning outcomes

After completing the course the student will be able to:
- Plan an effective data collection design
- Analyse own data using appropriate statistical methods (classical linear, multidimensional, phylogenetic)
– Construct phylogenetic (evolution) trees based on DNA sequence or morphological data
- Properly interpret the results of individual analyzes and discuss their possible weaknesses

Syllabus

• 1. Basic classification of methods and data types; basic descriptive statistics
• 2. Simple statistical tests I, probability, significance
• 3. Simple statistical tests II, correlation, regression, experimental design, pseudoreplication, hypotheses formulation
• 4. Similarity coefficients, similarity matrices, testing of matrix data
• 5. Ordination methods I – basic classification of methods, construction of ordination diagrams
• 6. Ordination methods II – understanding of ordination diagrams, group testing, comparison of different ordinations
• 7. Cluster analysis I – classification of methods, agglomerative algorithms, tree building
• 8. Cluster analysis II – understanding dendrograms, testing of dendrograms reliability, comparison of different dendrograms
• 9. Discriminant analysis, selection of proper determination characters
• 10. Evolutionary trees I – phylogenetic approach, phylogenetic terms and the tree description, alignment
• 11. Evolutionary trees II – tree building methods and criteria (maximum likelihood, parsimony), testing or tree quality and reliability, understanding of trees
• 12. Evolutionary trees III – testing of character evolution, molecular clocks
• 13. Statistics on web, graphic presentation of results
• 14. Phylogeny comparative methods - testing of phylogenetic data dependence, phylogenetic signal, phylogeneny corrected correlation and regression (PIC, pgls), reconstruction of ancestral character states on a tree

Literature

recommended literature
• MARHOLD, Karol and Jan SUDA. Statistické zpracování mnohorozměrných dat v taxonomii : (fenetické metody). 1. vyd. Praha: Karolinum, 2002, 159 s. ISBN 8024604388. info
• PODANI, János. Introduction to the exploration of multivariate biological data. Leiden: Backhyus Publishers, 2000, vi, 407. ISBN 9057820676. info
• Sneath PHA, Sokal RR (1973): Numerical taxonomy. W.H. Freeman, San Francisco
• LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 3rd engl. ed. Amsterdam: Elsevier, 2012, xvi, 990. ISBN 9780444538680. info
• Garland T. et al. (1992): Procedures for the analysis of comparative data using phylogenetically independant contrasts. Systematic Biology 41: 18-32.
• Modern phylogenetic comparative methods and their application in evolutionary biology : concepts and practice. Edited by László Zsolt Garamszegi. Berlin: Springer, 2014, xv, 552. ISBN 9783662435496. info
• Webb CO., Ackerly DD., Kembel SW. (2008): Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24: 2098-2100.

Teaching methods

lectures, individual practical analysis of own and model data

Assessment methods

To get credits, students should prepare an analysis of own or literature data.

Language of instruction

Czech

Further comments (probably available only in Czech)

The course is taught once in two years.
Information on the per-term frequency of the course: jaro lichých let (případně dle zájmu studentů).

• Enrolment Statistics (Spring 2023, recent)
  
• Permalink: https://is.muni.cz/course/sci/spring2023/Bi6590