Quantitative Classification and Natural Clustering of Caenorhabditis elegans Behavioral Phenotypes

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Abstract
Genetic analysis of nervous system function relies on the rigorous description of behavioral phenotypes. However, standard methods for classifying the behavioral patterns of mutant Caenorhabditis elegans rely on human observation and are therefore subjective and imprecise. Here we describe the application of machine learning 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 recordings of representative mutant types. Using principal component analysis, 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 different behavioral phenotypes and to identify natural phenotypic clusters among the data. Since the patterns of phenotypic similarity identified in this study closely paralleled the functional similarities of the mutant gene products, the complex phenotypic signatures obtained from these image data appeared to represent an effective diagnostic of the mutants' underlying molecular defects.