Fraser, Jane Hung, Vebjorn Ljosa, Shantanu Singh and Anne E. The Whitehead Institute for Biomedical Research and MIT's CSAIL.ĬellProfiler Analyst is distributed under the BSD License.ĬPA is described in the following publications:ĬellProfiler Analyst: interactive data exploration, analysis, and classification of large biological image setsīy David Dao, Adam N. Carpenter and Thouis (Ray) Jones in the laboratories of David M. The CellProfiler project is based at the Broad Institute Imaging Platform. Please contact us to discuss contributing to CPA, or notify us of papers referencing CPA so that we can provide the link to the paper and its results. CPA provides tools for exploring and analyzing multidimensional data, particularly data from high-throughput, image-based experiments analyzed by its companion image analysis software, Phenotypes, for automatic scoring of millions of cells. Included is a supervised machine learning system which can be trained to recognize complicated and subtle If negative cells are incorrectly fetched (false positives), drag and drop them into the negative bin.CellProfiler Analyst (CPA) allows interactive exploration and analysis of data, particularly from high-throughput, image-based experiments. Refine your training set by doing the following: If positive cells are correctly fetched (true positives), drag and drop them into the positive bin. Click the “Fetch!” button to retrieve samples of what the computer thinks are positive cells based on the current set of rules. Select “positive” from the drop-down list. Click on the drop-down box labeled “random” in the fetch controls. Change the number next to the word “Fetch” from “20” to “5”. These new sample cells can be added to the corresponding bins, in order to improve the classifier’s performance, with respect to distinguishing the FOXO1A-GFP subcellular localization phenotypes. Therefore you can now request that the computer fetch more examples of positive and negative cells. You now have your initial training set, and the rules that define the computer’s first attempt at distinguishing the phenotype. Refining the training set by fetching positive and negative cells: For each of these cells that you find, click on it andĭrag-and-drop it into the appropriate bin. Look for up to 5 cells that are clearly misclassified. On Macs, select “View” from the image menu, and then select “View cell classes as numbers.” Then, to see what each number means, click the “Show controls >” button at the bottom to On Windows computers this will also show which color corresponds to which class. Click the “Show controls >” button at the bottom to reveal the total counts of each class on the The cells will be color-coded according to their classification based on the current rules. The cells will be color-coded according to their classification based on the current rules.įrom the image that opens, click “Classify” from the menu, then “Classify Image”. From the image that opens, click “Classify” from the menu, then “Classify Image”. Double-click any of cell thumbnails in the positive or negative bins. You may also apply the rules to all the identified cells in an image, and use it to correct misclassifications. Refining the training set by correcting misclassified cells in an image: Divide the "MeanIntensity" of the rawGFP intensity from the nuclei by the of the cytoplasm. Select "Divide" from the drop-down for the "Operation" setting. Type "IntensityRatio" as descriptive name for the output measurement.
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