Abstract
In recent years, with the development of automated microscopy technologies, the volume and complexity of image data on gene expression have increased tremendously. The only way to analyze quantitatively and comprehensively such biological data is by developing and applying new sophisticated mathematical approaches. Here, we present extensions of 2D singular spectrum analysis (2D-SSA) for application to 2D and 3D datasets of embryo images. These extensions, circular and shaped 2D-SSA, are applied to gene expression in the nuclear layer just under the surface of the Drosophila (fruit fly) embryo. We consider the commonly used cylindrical projection of the ellipsoidal Drosophila embryo. We demonstrate how circular and shaped versions of 2D-SSA help to decompose expression data into identifiable components (such as trend and noise), as well as separating signals from different genes. Detection and improvement of under- and overcorrection in multichannel imaging is addressed, as well as the extraction and analysis of 3D features in 3D gene expression patterns.
Highlights
While the availability of genome sequences has drastically revolutionized biological and biomedical research, our understanding of how genes encode regulatory mechanisms is still limited
Singular spectrum analysis [10,11,12,13,14,15] was originally suggested as a method for decomposition of time series into a sum of identifiable components such as trend, oscillations, and noise
Data are taken from the Berkeley Drosophila Transcription Network Project (BDTNP) [4], which contains threedimensional (3D) measurements of relative mRNA concentration for 95 genes in early development (including snail) and the protein expression patterns for four genes (bicoid, giant, hunchback, and Kruppel (Kr)) during nuclear cleavage cycles (C13) and (C14A)
Summary
While the availability of genome sequences has drastically revolutionized biological and biomedical research, our understanding of how genes encode regulatory mechanisms is still limited. Imaging of single intact small organisms, like Drosophila and C. elegans, is feasible with high resolution in two dimensions, three dimensions, and across time, resulting in massive image data sets available for comprehensive computational analysis. These large-scale quantitative data sets provide new insights to address many fundamental questions in developmental biology. 2D-SSA and related subspace-based methods are applied in texture analysis [19], seismology [20], spatial gene expression data [21], and medical imaging [22].
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