Practical Morphometrics Analysis (2D Model)

Name: Practical Morphometrics Analysis (2D Model)
Rating: 3.9 (23 reviews)

A step-by-step approach to morphometrics based on two-dimensional images

Created byOlalekan Agbolade

Last updated 1/2020

English

What you'll learn

Introduction to morphometrics covering definitions, traditional morphometrics and geometric morphometrics
Landmarks and acqusition tools covering landmark types (anatomical or biological, mathematical, pseudo-landmarks), landmark homology, landmark acquisition tools and how to use, and error assessment with Procrustes ANOVA
Landmarks Visualization covering General Procrustes Analysis (GPA), visualization tools, scatter plots of landmark coordinates, Principal Component Analysis (PCA),Thin-Plate Spline (TPS) deformation grids, arrows, lollipops and other visualizations based on landmark displacements
Statistical methods and Analysis covering ANOVA, MANOVA, ANOSIM/PERMANOVA, regression & allometry, discriminant analysis and canonical variates analysis

Course content

4 sections • 19 lectures • 7h 4m total length

Morphometrics concept17:52
Explore the concept of morphometrics, contrasting traditional measurements with landmark-based geometric approaches, and apply 2d shape analysis to biological specimens using landmarks and coordinates.
Landmarks and acquisition19:26
Define biological two-dimensional landmarks on each specimen, ensuring identical point order and correspondence, using semi and geometric landmarks and tools like Mathematica and Morpheus, while addressing acquisition error and repeatability.
Landmarks visualization12:38
Explore two-dimensional morphometric analysis with landmarks visualization, detailing how landmark coordinates are aligned using Procrustes methods to reveal shape differences among taxa and visualize principal components.
Statistical analysis21:34
Explore statistical analysis in practical morphometrics, including tests for differences in means between groups, discriminant and regression analysis, centroid size from landmarks, and permutation-based significance.
Short quiz on introduction to morphometrics

Landmark acquisition with tpsDig24:35
learn to acquire landmarks on 2d images with tpsDig, including preparing and naming images, importing them, and digitizing x and y coordinates to generate lmp landmark data.
Landmark acquisition with StereoMorph27:16
Learn to acquire and digitize 2d morphometric landmarks with stereoMorph, including preparing images, labeling landmarks, exporting coordinates, and evaluating data formats and workflow challenges.
Landmark acquisition with geomorph18:40
Explore landmark acquisition for two-dimensional morphometrics using the geomorph package, and practice digitizing 20 images with predefined landmark descriptions.
Digitizing error assessment14:22
Assess digitizing error in 2d morphometrics by analyzing landmark coordinate variability from imaging, repeat measurements, and differences between operators and devices across specimens.
Quiz on landmark acquisition tools
Quiz on landmark acquisition tools

GPA and PCA18:24
Explore GPA alignment and PCA analysis of 2D landmark morphometrics using Morpheus and Past to derive mean shapes and eigenvalues.
Lollipop graph, TPS and wireframe24:25
Explore the lollipop graph visualization for morphometrics, including threshold changes, TPS and wireframe representations, and comparing male and female specimens while addressing interpretation challenges.
Error assessment (Procrustes ANOVA)47:41
Explore error assessment in procrustes anova by separating subject data, aligning 2d landmark configurations, and computing variance components to evaluate measurement error in morphometrics.
Landmark visualization in PAST 2.7 and 3.028:33
Learn landmark visualization in PAST 2.7 and 3.0, including importing data, configuring plots, and comparing groups for morphometric analysis.
Quiz on landmark visualization

ANOVA8:22
Assess differences in mean morphometric measures between two groups, such as male and female, using ANOVA. Interpret the F value and the p value to determine group differences.
ANOSIM and PERMANOVA9:25
Compare ANOSIM and PERMANOVA as tests of group differences using distance measures like Euclidean; describe permutation-based p-values, power, and sample size effects on significance.
Regression Analysis19:42
Explore regression analysis in 2D morphometrics by using central size as the predictor and shape coordinates as the response, evaluating transformations, plotting results, and comparing size effects across sexes.
MANOVA12:16
Apply MANOVA to explore how size affects ship sites and maneuvers, interpreting lambda and p-values to assess significance and the impact of constraints on morphometric data.
Linear Discriminant Analysis (LDA)10:57
This lecture demonstrates how to apply linear discriminant analysis to two-group morphometric data, interpret group separation, significance values, and classification results, and how choosing variables affects discriminatory power.
Euclidean Distance18:20
Measure euclidean distance between landmarks to quantify metric distances in a 2d morphometrics analysis; this lesson shows computing and presenting distances, with Python support.
Additional: Walk-through implementation of racial discrimination using MR2 DB1:10:24
Explore a practical walk-through of racial discrimination analysis using MR2 DB in 2D morphometrics, with 74 color images of European, African, and East Asian descent.
Quiz on statistical analysis

Requirements

Introductory college-level science, engineering or social science
Basic knowledge of statistics
Knowledge of shape or image analysis will be an added advantage

Description

Morphometrics has experienced a major revolution through the invention of coordinate-based methods, the discovery of the statistical theory of shape, and the computational realization of deformation grids. The ubiquitous application of fast personal computers and modern analytical tools have ushered in a new era of data analysis, permitting the exploration and visualization of large high-dimensional data sets along with exact statistical tests based on resampling procedures. This new morphometric approach has been termed geometric morphometrics as it preserves the geometry of the landmark configurations throughout the analysis and thus permits to represent statistical results as actual shapes or forms. Therefore, these lectures aim at teaching practically, the concept of statistical shape analysis from 2D images. To encourage learning by exploration; images, annotations and data reports from the hand study are made available for download.

Who this course is for:

Scientists or Biologists dealing with statistical shape or image analysis
Researchers in the field of forensics studies of human facial morphology such as face recognition, age estimation, sex dimorphism, and facial expression recognition
Students and researchers in evolutionary anthropology and cognitive science
Students and researchers hypothesizing on quantitative genetics and general-purpose species identification (such as in plants, animals and human)
Experts conducting research on disease diagnosis or in genetic disorder prediction
Students and researchers in orthodontics, phylogenetics, biomechanics and bioinformatics, neuroimaging analysis and bone histomorphometry
Students and researchers in geomorphometrics or terrain analysis

Practical Morphometrics Analysis (2D Model)

What you'll learn

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Course content

Introduction and Theoretical Background4 lectures • 1hr 12min

Landmark and Acquisition4 lectures • 1hr 25min

Landmark Visualization4 lectures • 1hr 59min

Statistical Analysis on Landmark Data7 lectures • 2hr 29min

Requirements

Description

Who this course is for: