Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning

Tanel Pärnamaa; Leopold Parts

doi:10.1101/050757

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bioRxiv · 10.1101/050757

Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning

Tanel Pärnamaa·Leopold Parts

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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.

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Tanel Pärnamaa, Leopold Parts. 2016-04-28. Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning. https://doi.org/10.1101/050757

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