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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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Quantitative performance of registration. Density plot of mutual information between test and reference images before approximate registration, after approximate registration, and final registration.
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Figure 8: Quantitative performance of registration. Density plot of mutual information between test and reference images before approximate registration, after approximate registration, and final registration.

Mentions: All images for each gene were registered against all five reference images and the best pair was selected according to maximum mutual information. Figures 5, 6 and 7 show the resulting images at various steps of registration. Analytical results of the registration is presented in Figure 8. Masks (examples shown in Figure 9) were created for each anatomical structure that was labeled in the reference image, allowing for the corresponding pixels to be extracted for each feature (examples shown in 10). Further analysis was done on these extracted features to provide biological validation of the methods (Figures 11 and 12). Detailed results are provided below.


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)

Quantitative performance of registration. Density plot of mutual information between test and reference images before approximate registration, after approximate registration, and final registration.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Quantitative performance of registration. Density plot of mutual information between test and reference images before approximate registration, after approximate registration, and final registration.
Mentions: All images for each gene were registered against all five reference images and the best pair was selected according to maximum mutual information. Figures 5, 6 and 7 show the resulting images at various steps of registration. Analytical results of the registration is presented in Figure 8. Masks (examples shown in Figure 9) were created for each anatomical structure that was labeled in the reference image, allowing for the corresponding pixels to be extracted for each feature (examples shown in 10). Further analysis was done on these extracted features to provide biological validation of the methods (Figures 11 and 12). Detailed results are provided below.

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