W. Geng, P. Cosman (USA), J.-H. Baek (Korea), C. Berry, and W.R. Schafer (USA)
C. elegans; Natural clustering; Feature selection; Imaging; Shape features
Genetic analysis of nervous system function relies on the rigorous description of animal behaviors. However, standard methods for classifying the behavioral patterns of mutant Caenorhabditis elegans (a microscopic worm) rely on human observation and are therefore subjective and imprecise. Here we describe the application of machine learning and image feature extraction techniques to quantitatively define and classify the behavioral patterns of C. elegans nervous system mutants. We have used an automated tracking and image processing system to obtain measurements of a wide range of morphological and behavioral features from videos of representative mutant types. By performing principal component analysis using a selected subset of features, we represented the behavioral patterns of eight mutant types as data clouds distributed in multidimensional feature space. Cluster analysis using the k-means algorithm made it possible to quantitatively assess the relative similarities between worm types and to identify natural clusters among the data. The patterns of similarity identified in this study closely paralleled the functional similarities of the mutant gene products, suggesting that the quantitative image features are an effective diagnostic of the mutants' underlying molecular defects.
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