Article

A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images

Center for Bioimage Informatics, and Departments of Biological Sciences, Biomedical Engineering, and Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15217, USA.
Journal of Proteome Research (Impact Factor: 4.25). 07/2008; 7(6):2300-8. DOI: 10.1021/pr7007626
Source: PubMed

ABSTRACT

The systematic study of subcellular location patterns is required to fully characterize the human proteome, as subcellular location provides critical context necessary for understanding a protein's function. The analysis of tens of thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates a need for automated subcellular pattern analysis. We therefore describe the application of automated methods, previously developed and validated by our laboratory on fluorescence micrographs of cultured cell lines, to analyze subcellular patterns in tissue images from the Human Protein Atlas. The Atlas currently contains images of over 3000 protein patterns in various human tissues obtained using immunohistochemistry. We chose a 16 protein subset from the Atlas that reflects the major classes of subcellular location. We then separated DNA and protein staining in the images, extracted various features from each image, and trained a support vector machine classifier to recognize the protein patterns. Our results show that our system can distinguish the patterns with 83% accuracy in 45 different tissues, and when only the most confident classifications are considered, this rises to 97%. These results are encouraging given that the tissues contain many different cell types organized in different manners, and that the Atlas images are of moderate resolution. The approach described is an important starting point for automatically assigning subcellular locations on a proteome-wide basis for collections of tissue images such as the Atlas.

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    • "Considering different features will have their own advantages, a common strategy is to fuse multiple types of features. For instance, different features are concatenated as a long vector to perform the subsequent classification task (Xu, et al., 2013)(Newberg and Murphy, 2008)(Yang, et al., 2014). Intuitively, since single type of features cannot reflect all the information of a protein image, fusing multiple types of features together is expected to be a more promising way. "
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