1/7/2024 0 Comments Flowjo software tree starPrincipal component analysis has been used to detect the complexity in CD8 T cell populations characterized by intermediate phenotypes that show a continuum of expression of different combinations of cytokines and surface markers ( Newell et al., 2012). However, to delineate the high heterogeneity of immune cell populations, it is necessary to look simultaneously at the whole staining panel. The common approach used to analyze the data produced by FCM is to visually select cells of interest through 1 or 2 markers known to be highly specific. Nowadays it is possible to analyze up to 20 markers at a time in a single staining panel by using an equal number of different fluorochromes detected in separate channels. The data are stored in Flow Cytometry Standard (FCS) files that include the fluorescence and scattered light levels for each cell that passed through the laser beams. Moreover, FCM helped increase our understanding of cellular functions of the immune system and is widely used in cell cycle analysis, pre-transplant crossmatching, cell sorting, apoptosis, vaccine development and other applications that scrutinize cellular properties ( Jaye et al., 2012 Mulley and Kanellis, 2011 Pozarowski and Darzynkiewicz, 2004 Vermes et al., 2000). In hematology, FCM is the technology of choice, as, for example, it requires only few drops of blood to diagnose leukemia through the detection of the perturbation of normal cell frequencies ( Brown and Wittwer, 2000). The most common application is the immune-phenotyping of blood samples and thus the quantification of the number and frequency of various immune cell populations. FCM applications have been developed mainly for both research and clinical settings in medicine but also for other non-biomedical domains such as marine and plant biology. As cells pass through the laser (excitation), the fluorochrome will change its state of energy and emit a light (emission) that is captured by a series of detectors. Fluorescence-conjugated antibodies are used to target antigens expressed inside or at the surface of the cells of interest. The software presented is an intuitive solution seeking to improve the results not only of manual but also and in particular of automatic analysis on FCM data.Īvailability and implementation: R source code available through Bioconductor: Ĭontacts: or information: Supplementary data are available at Bioinformatics online.įlow cytometry (FCM) is a laser-based methodology designed to capture the physical and biochemical characteristics of a cell or a particle in a stream of fluid. For each file analyzed our software generates a summary of the quality assessment from the aforementioned steps. The general approach behind the two methods consists of three key steps to check and remove suspected anomalies that derive from (i) abrupt changes in the flow rate, (ii) instability of signal acquisition and (iii) outliers in the lower limit and margin events in the upper limit of the dynamic range. 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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