GND-PCA Method for Identification of Gene Functions Involved in Asymmetric Division of C. elegans

Abstract

Due to the rapid development of imaging technology, a large number of biological images have been obtained with three-dimensional (3D) spatial information, time information, and spectral information. Compared with the case of two-dimensional images, the framework for analyzing multidimensional bioimages has not been completely established yet. WDDD is an open biological image database. It dynamically records 3D developmental images of 186 samples of nematodes C. elegans. In this study, based on WDDD, we constructed a framework to analyze the multidimensional dataset, which includes image segmentation, image registration, size registration by the length of main axes, time registration by extracting key time points, and finally, using generalized N-dimensional principal component analysis (GND-PCA) to analyze the phenotypes of bioimages. As a data-driven technique, GND-PCA can automatically extract the important factors involved in the development of P1 and AB in C. elegans. A 3D bioimage can be regarded as a third-order tensor. Therefore, GND-PCA was applied to the set of third-order tensors, and a set of third-order tensor bases was iteratively learned to linearly approximate the set. For each tensor base, a corresponding characteristic image is built to reveal its geometric meaning. The results show that different bases can be used to express different vital factors in development, such as the asymmetric division within the two-cell stage of C. elegans. Based on selected bases, statistical models were built by 50 wild-type (WT) embryos in WDDD, and were applied to RNA interference (RNAi) embryos. The results of statistical testing demonstrated the effectiveness of this method.