
Explore GPA alignment and PCA analysis of 2D landmark morphometrics using Morpheus and Past to derive mean shapes and eigenvalues.
Learn landmark visualization in PAST 2.7 and 3.0, including importing data, configuring plots, and comparing groups for morphometric analysis.
Measure euclidean distance between landmarks to quantify metric distances in a 2d morphometrics analysis; this lesson shows computing and presenting distances, with Python support.
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.