Results: We present an R package, flowAI, containing two methods to clean FCM files from unwanted events: (i) an automatic method that adopts algorithms for the detection of anomalies and (ii) an interactive method with a graphical user interface implemented into an R shiny application. However, automated analyses may lead to false discoveries due to inter-sample differences in quality and properties. This requires the use of the latest clustering and dimensionality reduction techniques to automatically segregate cell sub-populations in an unbiased manner. The latest FCM instruments analyze up to 20 markers of individual cells, producing high-dimensional data. Motivation: Flow cytometry (FCM) is widely used in both clinical and basic research to characterize cell phenotypes and functions.
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