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Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering.

Jagalur M, Pal C, Learned-Miller E, Zoeller RT, Kulp D - BMC Bioinformatics (2007)

Bottom Line: We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery.Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Massachusetts Amherst, Amherst, MA-01003, USA. manju@cs.umass.edu

ABSTRACT

Background: Many important high throughput projects use in situ hybridization and may require the analysis of images of spatial cross sections of organisms taken with cellular level resolution. Projects creating gene expression atlases at unprecedented scales for the embryonic fruit fly as well as the embryonic and adult mouse already involve the analysis of hundreds of thousands of high resolution experimental images mapping mRNA expression patterns. Challenges include accurate registration of highly deformed tissues, associating cells with known anatomical regions, and identifying groups of genes whose expression is coordinately regulated with respect to both concentration and spatial location. Solutions to these and other challenges will lead to a richer understanding of the complex system aspects of gene regulation in heterogeneous tissue.

Results: We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a non-linear, information theoretic based image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information within an anatomical region. Our method consists of coarse registration, fine registration, and expression feature extraction steps. From this we obtain a matrix for expression characteristics with rows corresponding to genes and columns corresponding to anatomical sub-structures. We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.

Conclusion: Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery. Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method. While we focus here on imagery and experiments of the mouse brain our approach should be applicable to a variety of in situ experiments.

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Delaunay triangulation of landmarks. The left image shows the Delaunay triangulation based upon a set of landmarks in the reference image. The right image shows the corresponding triangulation of mapped points in an expression image. Triangulation in the right image might not be the Delaunay triangulation.
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Figure 4: Delaunay triangulation of landmarks. The left image shows the Delaunay triangulation based upon a set of landmarks in the reference image. The right image shows the corresponding triangulation of mapped points in an expression image. Triangulation in the right image might not be the Delaunay triangulation.

Mentions: 3. Using the landmarks in the reference image (from the first step), the next step is to define a set of triangular regions in the reference image that will be individually registered to corresponding regions in the expression image. To do this, a "triangulation" of the reference image, using the set of identified landmarks, must be performed. We do this using a standard procedure known as Delaunay triangulation, which is described further in the methods section. Intuitively, a Delaunay triangulation is designed to break the image into triangles such that "sliver-like" triangles are avoided as much as possible. The left side of Figure 4 shows a Delaunay triangulation of the reference image based upon the landmarks which have been defined at each triangle vertex.


Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering.

Jagalur M, Pal C, Learned-Miller E, Zoeller RT, Kulp D - BMC Bioinformatics (2007)

Delaunay triangulation of landmarks. The left image shows the Delaunay triangulation based upon a set of landmarks in the reference image. The right image shows the corresponding triangulation of mapped points in an expression image. Triangulation in the right image might not be the Delaunay triangulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2230506&req=5

Figure 4: Delaunay triangulation of landmarks. The left image shows the Delaunay triangulation based upon a set of landmarks in the reference image. The right image shows the corresponding triangulation of mapped points in an expression image. Triangulation in the right image might not be the Delaunay triangulation.
Mentions: 3. Using the landmarks in the reference image (from the first step), the next step is to define a set of triangular regions in the reference image that will be individually registered to corresponding regions in the expression image. To do this, a "triangulation" of the reference image, using the set of identified landmarks, must be performed. We do this using a standard procedure known as Delaunay triangulation, which is described further in the methods section. Intuitively, a Delaunay triangulation is designed to break the image into triangles such that "sliver-like" triangles are avoided as much as possible. The left side of Figure 4 shows a Delaunay triangulation of the reference image based upon the landmarks which have been defined at each triangle vertex.

Bottom Line: We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery.Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Massachusetts Amherst, Amherst, MA-01003, USA. manju@cs.umass.edu

ABSTRACT

Background: Many important high throughput projects use in situ hybridization and may require the analysis of images of spatial cross sections of organisms taken with cellular level resolution. Projects creating gene expression atlases at unprecedented scales for the embryonic fruit fly as well as the embryonic and adult mouse already involve the analysis of hundreds of thousands of high resolution experimental images mapping mRNA expression patterns. Challenges include accurate registration of highly deformed tissues, associating cells with known anatomical regions, and identifying groups of genes whose expression is coordinately regulated with respect to both concentration and spatial location. Solutions to these and other challenges will lead to a richer understanding of the complex system aspects of gene regulation in heterogeneous tissue.

Results: We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a non-linear, information theoretic based image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information within an anatomical region. Our method consists of coarse registration, fine registration, and expression feature extraction steps. From this we obtain a matrix for expression characteristics with rows corresponding to genes and columns corresponding to anatomical sub-structures. We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.

Conclusion: Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery. Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method. While we focus here on imagery and experiments of the mouse brain our approach should be applicable to a variety of in situ experiments.

Show MeSH