Live Online Course – 10th Edition
May 2nd-10th, 2024
Concepts in geometric morphometrics will be taught using a series of original data sets and working in R for solving a series of tasks. The course will start with an introduction to R and will rapidly go into shape analysis with measurements, landmark data and outlines. The participants are welcome to bring their own data and problems so that we may find R solutions.
This is not an introductory course to Geometric Morphometrics, therefore basic knowledge of Multivariate Statistics, R and Geometric Morphometric is recommended in order to take this course.
Click here for Course Programme
1. An Introduction to R / Image Processing / Organizing Morphometric Data.
1.1. Some Basics in R.
1.1.1. The R Environment.
1.1.2. R objects, Assigning, Indexing.
1.1.3. Generating Data in R.
1.1.4. 2D and 3D Plots in R; Interacting with the Graphs.
1.2. Organizing Data for Morphometrics.
1.2.1. Data-frame, Array and List.
1.2.2. Converting and Coercing Objects.
1.2.3. Read and Write Morphometric Data in R.
1.3. Image Processing in R.
1.3.1. Reading Various Image Files.
1.3.2. Obtaining Image Properties.
1.3.3. Modifying Image Properties: Contrast, Channels, Saturation Directly from R or by Interfacing R with Imagemagick.
1.4. Simple Tests, Simple Linear Modelling, Alternatives to Linear Modelling, an example using traditional morphometrics.
1.4.1. Defining size and shape using PCA and log-shape ratio approaches.
1.4.2. Getting stats and test outputs.
1.4.3. Testing assumptions of linear modelling.
1.4.4. Testing for allometry and isometry.
1.4.5. Solutions when assumptions of linear modelling are not met.
2. Landmark data.
2.1. Acquiring Landmark Data in R.
2.2. Plotting Landmark Configurations in 2 and in 3D.
2.2.1. Using Different Symbols and Setting the Graphical Parameters.
2.2.2. Labeling Landmarks.
2.3. Geometric Transformation with Landmark Configurations.
2.3.1. Translation.
2.3.2. Scaling using Baseline or Centroid Size.
2.3.3. Rotation.
2.4. Superimposing and Comparing Two Shapes.
2.4.1. Baseline Superimposition.
2.4.2. Ordinary Least Squares Superimposition.
2.4.3. Resistant Fit.
2.5. Representing Shape Differences.
2.5.1. Plotting Superimposed Shape with Wireframe.
2.5.2. Lollipop Diagrams and Vector Fields.
2.5.3. Thin Plate Splines and Warped Shapes.
2.6. Superimposing More Than Two Shapes.
2.6.1. Baseline Registration.
2.6.2. Full Generalized Procrustes Analysis.
2.6.3. Partial Generalized Procrustes Analysis.
2.6.4. Dimensionality of Superimposed Coordinates.
2.7. Exploring Shape Variation and Testing Hypotheses.
2.7.1. PCA.
2.7.2. Multivariate Linear Modelling (Multivariate Regression and MANOVA).
2.7.3. Allometry free approaches (Burnaby correction).
2.7.4. Linear discriminant and Canonical Analysis.
3. Outline Data.
3.1. Acquiring outline Data in R.
3.2. Fourier Analysis.
3.2.1. Principles.
3.2.2. Fourier Analysis of the Tangent Angle.
3.2.3. Radius Fourier Analysis.
3.2.4. Elliptic Fourier Analysis.
3.2.5. Reduction of Shape Variables.
3.2.6. Statistical Analysis of Shape Variation with Fourier Analysis.
3.2.6.1. Exploring Shape Variation and Testing Hypotheses.
3.2.6.2. PCA.
3.2.6.3. Multivariate Linear Modelling (Multivariate Regression and MANOVA).
3.2.6.4. Canonical Analysis.
3.3. Combining Landmarks and Curves.
3.3.1. Hybrid Methods between Fourier and Procrustes Analysis.
3.3.2. Sliding Semi Landmarks.
3.4. Solutions for Open Curves.
4. Specific Applications.
4.1. Testing Measurement Error.
4.2. Partitional Clustering.
4.2.1. K-means, Partition Around Medoids.
4.2.2. Mclust.
4.2.3. Combining Genetic, Geographic and Morphometric Data.
4.3. Modularity / Integration Studies.
4.3.1. Two-block Partial Least Squares.
4.3.2. Testing Among Various Sets of Modules.
4.4.Fluctuating Asymmetry and Directional Asymmetry.
4.4.1. Inter-Individual and Intra-Individual Variation.
4.4.2. Object and Matching Symmetry.
4.5.Bending Energy, Uniform and Non-uniform Shape Variation.
Click here for Course Requirements
Graduate or postgraduate degree in any Life Sciences discipline.
Knowledge of Multivariate Statistics, R and Geometric Morphometrics. Participants that are not familiar with the R environment are strongly recommended to read the book R for beginners and practice before the course, or to take the course Introduction to R. Participants who are not familiar with Geometric Morphometrics are recommended to take first the course Introduction to Geometric Morphometrics.
All participants must bring their own personal laptop (Windows, Macintosh, Linux). A good internet connection is required to follow the course.
Click here for Course Literature
“Morphometrics with R“, Julien Claude. R. Gentleman, K. Hornik and G. Parmigiani, eds.
Online live sessions on 2nd, 3rd, 6th, 7th, 8th, 9th, and 10th of May
12:00-17:00 (Madrid time zone)
Total course hours: 40
35 hours of online live lessons, plus 5 hours of participants working on their own.
This course is equivalent to 2 ECTS (European Credit Transfer System) at the Life Science Zurich Graduate School.
The recognition of ECTS by other institutions depends on each university or school.
English
This course will be taught using a combination of live (synchronous) sessions on Zoom and tasks to be completed in between live sessions on the Slack platform.
Live sessions will be recorded. Recordings will be made available to participants for a limited period of time. However, attendance to the live sessions is required.
Places are limited to 16 participants and will be occupied by strict registration order.
Participants who have completed the course will receive a certificate at the end.
We offer discounts on the Course Fee.
Discounts are not cumulative. Participants receive the highest appropriate discount.
We also offer the possibility of paying in two instalments. Please contact us to request this.
5% Discount - Former participants
Former participants of Transmitting Science courses receive a 5% discount on the Course Fee.
20% Discount - Ambassador Institutions
20% discount on the Course Fee is offered to members of certain organisations (Ambassador Institutions). If you wish to apply for this discount, please indicate it in the Registration form (proof will be asked later). If you would like your institution to become a Transmitting Science Ambassador Institution, please contact us at communication@transmittingscience.com
40% Discount - Scientists based in Spain without funding
Unemployed scientists, as well as PhD students without any grant or scholarship to develop their PhD, can benefit from a 40% discount on the Course Fee. This applies only to participants based in Spain. If you wish to ask for this discount, please contact us. The discount may apply for a maximum of 2 places, which will be covered by strict registration order.
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