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Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples

Figure 6

Comparison of FLOCK, FLAME and flowClust for detection of rare antigen-specific events.

The panels show the estimated frequencies of antigen-specific cells (large red dots) expressed as a percentage of all events (yellow boxes). (Left panel) FLOCK detects the antigen-specific cluster at the highest spiked-in frequency but not in the other samples. There are several CD3-negative events included in the detected cluster that are most likely false positive events. As indicated by the color coding of events, FLOCK does not provide any alignment of cell subsets across samples. (Middle panel) Using the default settings, FLAME failed to identify any antigen-specific cell subsets. Cell subsets found were aligned but there were alignment artifacts when the event partitioning was different across samples (arrowed example). (Right panel) Using 64 components and 1000 iterations, flowClust only identified antigen-specific clusters at the highest spiked-in levels and did not provide any methods to align clusters across samples.

Figure 6

doi: https://doi.org/10.1371/journal.pcbi.1003130.g006