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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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High entropy landmarks. Squares on the left image highlight some of the high entropy landmarks in the reference image. Large squares on the right image show the search space for those landmarks in the expression image, and small squares show the optimal patch that corresponds to those landmarks.
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Figure 3: High entropy landmarks. Squares on the left image highlight some of the high entropy landmarks in the reference image. Large squares on the right image show the search space for those landmarks in the expression image, and small squares show the optimal patch that corresponds to those landmarks.

Mentions: 1. In the first step, which is only performed once per reference image, points in the reference image that are "distinctive" are selected as a basis for the alignment. The goal is to find a set of points which can be matched with corresponding points in the histological image with high reliability. The measure of distinctiveness is the entropy of the neighborhood of the point. The entropy can be thought of as a measure of the complexity of a point's neighborhood in the reference image. Neighborhoods with high entropy (such as the junction of three different anatomical structures) are likely to have more structure to provide a local and repeatable match. Neighborhoods with low entropy (such as a neighborhood around a patch of constant brightness) do not have enough structure to provide an unambiguous match. The left side of Figure 3 shows some of the high entropy neighborhoods selected in the reference image as distinctive landmarks for registration.


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)

High entropy landmarks. Squares on the left image highlight some of the high entropy landmarks in the reference image. Large squares on the right image show the search space for those landmarks in the expression image, and small squares show the optimal patch that corresponds to those landmarks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: High entropy landmarks. Squares on the left image highlight some of the high entropy landmarks in the reference image. Large squares on the right image show the search space for those landmarks in the expression image, and small squares show the optimal patch that corresponds to those landmarks.
Mentions: 1. In the first step, which is only performed once per reference image, points in the reference image that are "distinctive" are selected as a basis for the alignment. The goal is to find a set of points which can be matched with corresponding points in the histological image with high reliability. The measure of distinctiveness is the entropy of the neighborhood of the point. The entropy can be thought of as a measure of the complexity of a point's neighborhood in the reference image. Neighborhoods with high entropy (such as the junction of three different anatomical structures) are likely to have more structure to provide a local and repeatable match. Neighborhoods with low entropy (such as a neighborhood around a patch of constant brightness) do not have enough structure to provide an unambiguous match. The left side of Figure 3 shows some of the high entropy neighborhoods selected in the reference image as distinctive landmarks for registration.

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