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Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data.

Costa IG, Krause R, Opitz L, Schliep A - BMC Bioinformatics (2007)

Bottom Line: We investigate the influence of these pairwise constraints in the clustering and discuss the biological relevance of our results.Spatial information contributes to a detailed, biological meaningful analysis of temporal gene expression data.Semi-supervised learning provides a flexible, robust and efficient framework for integrating data sources of differing quality and abundance.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. ivan.filho@molgen.mpg.de

ABSTRACT

Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. Complimentary large data sets of in situ RNA hybridization images for different stages of the fly embryo elucidate the spatial expression patterns.

Results: Using a semi-supervised approach, constrained clustering with mixture models, we can find clusters of genes exhibiting spatio-temporal similarities in expression, or syn-expression. The temporal gene expression measurements are taken as primary data for which pairwise constraints are computed in an automated fashion from raw in situ images without the need for manual annotation. We investigate the influence of these pairwise constraints in the clustering and discuss the biological relevance of our results.

Conclusion: Spatial information contributes to a detailed, biological meaningful analysis of temporal gene expression data. Semi-supervised learning provides a flexible, robust and efficient framework for integrating data sources of differing quality and abundance.

Show MeSH
Image processing pipeline. The image pipeline combines registration, morphological operations and further processing steps to automatically process raw images, even if they include multiple touching embryos. Shown here is the image insitu8784, gene CG5353.
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Figure 9: Image processing pipeline. The image pipeline combines registration, morphological operations and further processing steps to automatically process raw images, even if they include multiple touching embryos. Shown here is the image insitu8784, gene CG5353.

Mentions: The final step of image processing is to register the embryos extracted to a standardized orientation and size to allow for comparison of different expression patterns. The embryo is rotated to align horizontally to the principal axis. Subsequently the bounding box is scaled to a standard size. Fig. 9 shows the steps of the image processing pipeline for one example image.


Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data.

Costa IG, Krause R, Opitz L, Schliep A - BMC Bioinformatics (2007)

Image processing pipeline. The image pipeline combines registration, morphological operations and further processing steps to automatically process raw images, even if they include multiple touching embryos. Shown here is the image insitu8784, gene CG5353.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2230504&req=5

Figure 9: Image processing pipeline. The image pipeline combines registration, morphological operations and further processing steps to automatically process raw images, even if they include multiple touching embryos. Shown here is the image insitu8784, gene CG5353.
Mentions: The final step of image processing is to register the embryos extracted to a standardized orientation and size to allow for comparison of different expression patterns. The embryo is rotated to align horizontally to the principal axis. Subsequently the bounding box is scaled to a standard size. Fig. 9 shows the steps of the image processing pipeline for one example image.

Bottom Line: We investigate the influence of these pairwise constraints in the clustering and discuss the biological relevance of our results.Spatial information contributes to a detailed, biological meaningful analysis of temporal gene expression data.Semi-supervised learning provides a flexible, robust and efficient framework for integrating data sources of differing quality and abundance.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. ivan.filho@molgen.mpg.de

ABSTRACT

Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. Complimentary large data sets of in situ RNA hybridization images for different stages of the fly embryo elucidate the spatial expression patterns.

Results: Using a semi-supervised approach, constrained clustering with mixture models, we can find clusters of genes exhibiting spatio-temporal similarities in expression, or syn-expression. The temporal gene expression measurements are taken as primary data for which pairwise constraints are computed in an automated fashion from raw in situ images without the need for manual annotation. We investigate the influence of these pairwise constraints in the clustering and discuss the biological relevance of our results.

Conclusion: Spatial information contributes to a detailed, biological meaningful analysis of temporal gene expression data. Semi-supervised learning provides a flexible, robust and efficient framework for integrating data sources of differing quality and abundance.

Show MeSH