Cytometry is likely to be the technology of choice for observing and understanding cellular processes in the foreseeable future. Unfortunately, there are problems with the current flow cytometry technology that limit its full potential. Some of these limitations include parameter scalability, hierarchical gating errors, and multiple sample analysis integration and visualization.

A new paradigm has been proposed that is based on a probability state model capable of representing the rich multiparameter information embedded in cytometric listmode files. Model-derived parametric plots merge intensity, and percentage, as well as variance information into easy-to-understand graphical formats that represent virtually every bit of information contained in these files. Normal bone marrow specimens analyzed with this new approach reveal lineage relationships not easily appreciated with conventional analysis approaches and may change our understanding of what “normal” really means.

The major advantage of the probability state modeling system is that it scales well with number of parameters or measurement, so largely eliminates the dimensionality barrier for data analysts to extract information. Because this system stochastically classifies events, it also appropriately accounts for population overlap and thus minimizes the need for the usual cytometric gates and analysis regions. Multiple listmode files can be integrated into a single model if there are appropriate common parameters. Finally, the system presents high dimensional cytometry data in an understandable format that needs little interpretation.