Clustering Gene Expression Patterns of Fly Embryos

Abstract

The spatio-temporal patterning of gene expression in early embryos is an important source of information for understanding the functions of genes involved in development. Most analyses to date rely on biologists' visual inspection of microscope images, which for large-scale datasets becomes impractical and subjective. In this paper, we introduce a new method for clustering 2D images of gene expression patterns in Drosophila melanogaster (fruit fly) embryos. These patterns, typically generated from in situ hybridization of mRNA probes, reveal when, where and how abundantly a target gene is expressed. Our method involves two steps. First, we use an eigen-embryo model to reduce noise and generate feature vectors that form a better basis for capturing the salient aspects of quantized embryo images. Second, we cluster these feature vectors by an efficient minimum-spanning-tree partition algorithm. We investigate this approach on fly embryo datasets that span the entire course of embryogenesis. The experimental results show that our clustering algorithm produces superior pattern clusters. We also find previously unobserved clusters of genes that share biologically interesting patterns of gene-expression.