Automated analysis of human protein atlas immunofluorescence images

Authors:
Justin Y. Newberg;Jieyue Li;Arvind Rao;Fredrik Pontén;Mathias Uhlén;Emma Lundberg;Robert F. Murphy
Affiliations:
Center for Bioimage Informatics and Dept. of Biomedical Engineering;Center for Bioimage Informatics and Dept. of Biomedical Engineering;Lane Center for Comp. Biol. and Depts. of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, PA;Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden;Dept. of Biotechnology, AlbaNova University Center, Royal Institute of Technology, Stockholm, Sweden;Dept. of Biotechnology, AlbaNova University Center, Royal Institute of Technology, Stockholm, Sweden;Center for Bioimage Informatics and Dept. of Biomedical Engineering and Lane Center for Comp. Biol. and Depts. of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, ...
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

The Human Protein Atlas is a rich source of location proteomics data. In this work, we present an automated approach for processing and classifying major subcellular patterns in the Atlas images. We demonstrate that two different classification frameworks (support vector machine and random forest) are effective at determining subcellular locations; we can analyze over 3500 Atlas images with a high degree of accuracy, up to 87.5% for all of the samples and 98.5% when only considering samples in whose classification assignments we are most confident. Moreover, the features obtained in both of these frameworks are observed to be highly consistent and generalizable. Additionally, we observe that the features relating the proteins to cell markers are especially important in automated learning approaches.