These cells can be from control samples if the screen has been designed to address a particular phenotype, or selected at random if the screen's goal is to uncover previously uncharacterized phenotypes in an exploratory screen. Next, the researcher initiates the training phase by identifying a few positive example cells that display a phenotype of interest and negative example cells without the phenotype ( Fig. This cytoprofile consists of a set of numbers that describe the cell's characteristics, including size, shape, and the intensity and texture of various stains in various compartments ( Fig. First, we automatically identify and measure every cell in every image in the experiment by using the cell-image analysis software CellProfiler ( 13), which generates a cytological profile ( 27), or cytoprofile, for each cell. We have developed and validated a method for researchers to rapidly train a computer to score unusual cell morphologies automatically ( Fig. Even when positive control samples are available, using positive example cells from only those samples can lead to inaccurate scoring because of overfitting of the machine learning algorithm. However, when this is not the case, as in classic exploratory screens, finding a sufficient number of positive cells can be prohibitively difficult. Finding positive cells is straightforward when positive control samples are available and most of the cells therein show the phenotype. These methods require the provision of example cells that do and do not display the morphology of interest (i.e., positive and negative cells). Machine learning methods that select and combine multiple features for automated cell classification have been used to score many phenotypes ( 15– 26). However, many interesting phenotypes require the assessment of several measured features of cells. Cell image analysis allows accurate identification and measurement of cells' features, enabling automated analysis of certain phenotypes that were previously intractable ( 13– 26). By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.ĭespite these advances, scoring cells in images for rare and unusual morphologies has, in general, remained a significant bottleneck ( 9– 12). Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. ![]() First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Now, automated image analysis can effectively score many phenotypes. ![]() Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection.
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