![]() ![]() Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary. ![]() Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. ![]()
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