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Automatic quantification of subtle cellular phenotypes in microscopy-based high-throughput experiments

Automatic quantification of subtle cellular phenotypes in microscopy-based high-throughput experiments

This video was recorded at 4th International Workshop on Machine Learning in Systems Biology (MLSB), Edinburgh 2010. Microscopy-based high-throughput experiments can provide a view into biological responses and states at the resolution of singe cells. CellProfiler, our open-source image-analysis software, has become widely used by biologists to design custom analysis pipelines for complex high-throughput assays. I will discuss our work in progress to automatically quantify the prevalence of subtle cellular phenotypes in high-throughput samples of cultured cells I will also touch briedly on the use of machine learning to improve the accuracy and robustness of CellProfiler's image segmentation. Our classification tool, CellProfiler Analyst, enables a biologist to train a boosting classifier iteratively to detect rare, complex phenotypes, and its usefulness has been demonstrated in several high-throughput screens. Here, I will describe a method to learn phenotypes without requiring hand-labeled cells for training. Instead, a classifier is trained from negative and positive controls in the experiment, where the positives are known to be enriched in the phenotype of interest, even if only slightly (e.g., 55% vs. 45% penetrance). By nonlinearly projecting cells into a random feature space, we can use efficient linear methods but still benefit from nonlinear notions of similarity, and can overcome experimental noise by training on millions of cells. Using the resulting classifier to assign soft labels to each cell in the experiment, we can identify enriched samples ("hits") nonparametrically. Furthermore, we are developing techniques to automatically identify relevant cellular phenotypes in large-scale chemical profiling experiments.

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