Visualizing Phenotypes from High Content Screening
High content screening (HCS) poses a data challenge not only by the scale of the problem, but by the variety of ways that objects that can be characterized. Traditional image processing approaches have focused on identifying cells and enumerating measurements from the images to create multidimensional feature sets. Deep learning offers opportunities to learn to recognize cell types or cellular components and quantifies them by their co-locations, counts, etc. A difficulty of both methods is providing an interpretation of the results to the scientist. In HCS where the phenotypes are unknown, the variability between treatments and cells further complicates the ability to distinguish the classes of response and quantify how they vary from one or more sets of controls. This talk will provide an example imaging workflow and highlight approaches for visualizing the results.
Farhan Siddiqui is an Advanced Analytics Architect at Pfizer and is a technology leader with extensive technology management experience delivering cloud hosted advanced analytics solutions for top pharmaceutical companies in US. Deep expertise in cloud computing, big data and machine learning