Systematic image-driven analysis of the spatial Drosophila embryonic expression landscape
By Erwin Frise, Ann Hammonds, S. Celniker. 2010
46 citations recorded by the source corpus.
Discovery of temporal and spatial patterns of gene expression is essential for understanding the regulatory networks and development in multicellular organisms. We analyzed the images from our large‐scale spatial expression data set of early Drosophila embryonic development and present a comprehensive computational image analysis of the expression landscape. For this study, we created an innovative virtual representation of embryonic expression patterns using an elliptically shaped mesh grid that allows us to make quantitative comparisons of gene expression using a common frame of reference. Demonstrating the power of our approach, we used gene co‐expression to identify distinct expression domains in the early embryo; the result is surprisingly similar to the fate map determined using laser ablation. We also used a clustering strategy to find genes with similar patterns and developed new analysis tools to detect variation within consensus patterns, adjacent non‐overlapping patterns, and anti‐correlated patterns. Of the 1800 genes investigated, only half had previously assigned functions. The known genes suggest developmental roles for the clusters, and identification of related patterns predicts requirements for co‐occurring biological functions.
Subfields: Biophysics, Molecular Biology
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Citations in the corpus
- Iterative random forests to discover predictive and stable high-order interactions (2017, background): “1 Drosophila Enhancers In total, 7809 genomic sequences have been evaluated for their enhancer activity (Berman et al., 2002; Fisher et al., 2012; Frise et al., 2010; Kvon et al., 2014) in a gold-standard, stable-integration transgenic assay.”
- Building quantitative, three dimensional atlases of gene expression and morphology at cellular resolution (2013, background): “Lower resolution studies or two-dimensional image analyses have proven useful for addressing some important questions, 20–28 but space does not permit discussion of these approaches here.”