TY - JOUR T1 - Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning JF - bioRxiv DO - 10.1101/050757 SP - 050757 AU - Tanel Pärnamaa AU - Leopold Parts Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/04/28/050757.abstract N2 - 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. ER -