title: Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.
abstract: High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.
authors.affiliation: Institute of Computer Science, University of Tartu, 50409, Estonia.
authors.forename: Tanel
authors.initials: T
authors.lastname: Pärnamaa
authors.affiliation: Institute of Computer Science, University of Tartu, 50409, Estonia leopold.parts@sanger.ac.uk.|Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire CB10 1SA, United Kingdom.
authors.forename: Leopold
authors.initials: L
authors.lastname: Parts
mesh_terms: D005656:Fungal Proteins / Q000145:classification / Q000378:metabolism*; D057166:High-Throughput Screening Assays / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008856:Microscopy, Fluorescence / Q000379:methods; D021381:Protein Transport; D020543:Proteome / Q000145:classification / Q000378:metabolism*; D015003:Yeasts / Q000378:metabolism / Q000648:ultrastructure
chemical_list: D005656:Fungal Proteins; D020543:Proteome
keywords: deep learning; high-content screening; machine learning; microscopy; yeast