Abstract
Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data.
Highlights
Advances in automated image acquisition and analysis, coupled with the availability of reagents for genome-scale perturbation, have enabled systematic analyses of cellular and subcellular phenotypes (Mattiazzi Usaj et al, 2016)
We provide a brief overview of convolutional neural networks in
In DeepLoc, input images are processed through convolutional blocks in which trainable sets of filters are applied at different spatial locations, thereby having local connections between layers, and enabling discovery of invariant patterns associated with a particular class
Summary
Advances in automated image acquisition and analysis, coupled with the availability of reagents for genome-scale perturbation, have enabled systematic analyses of cellular and subcellular phenotypes (Mattiazzi Usaj et al, 2016). One powerful application of microscopy-based assays involves assessment of changes in the subcellular localization or abundance of fluorescently labeled proteins in response to various genetic lesions or environmental insults (Laufer et al, 2013; Ljosa et al, 2013; Chong et al, 2015). Highthroughput (HTP) microscopy enables analysis of proteome-wide changes in protein localization in different conditions, providing data with the spatiotemporal resolution that is needed to understand the dynamics of biological systems. Several studies have since used the collection to quantify protein abundance changes and to map protein re-localization in response to various stress conditions, again using manual assessment of protein localization (Tkach et al, 2012; Breker et al, 2013)
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