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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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Approximate registration. Images show the difference between expression image and reference image before(left) and after approximate registration. In these images red channel corresponds to reference image and green channel corresponds to expression image.
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Figure 2: Approximate registration. Images show the difference between expression image and reference image before(left) and after approximate registration. In these images red channel corresponds to reference image and green channel corresponds to expression image.

Mentions: The coarse registration step is done to put the ISH image in rough alignment with the reference image. Figure 2 shows the result of the coarse registration step. The main purpose of this step is to ease the computational burden on the fine registration step (discussed below). In particular, if the images are in rough alignment, the fine registration step can assume that a pair of corresponding points in the reference image and the histological image are at similar locations. To obtain a rough alignment, a global affine transformation (one affine transformation for the entire expression image) is done between the reference image and the expression image. More details are given on this step in the methods section.


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)

Approximate registration. Images show the difference between expression image and reference image before(left) and after approximate registration. In these images red channel corresponds to reference image and green channel corresponds to expression image.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Approximate registration. Images show the difference between expression image and reference image before(left) and after approximate registration. In these images red channel corresponds to reference image and green channel corresponds to expression image.
Mentions: The coarse registration step is done to put the ISH image in rough alignment with the reference image. Figure 2 shows the result of the coarse registration step. The main purpose of this step is to ease the computational burden on the fine registration step (discussed below). In particular, if the images are in rough alignment, the fine registration step can assume that a pair of corresponding points in the reference image and the histological image are at similar locations. To obtain a rough alignment, a global affine transformation (one affine transformation for the entire expression image) is done between the reference image and the expression image. More details are given on this step in the methods section.

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