Multidimensional data analysis in immunophenotyping

The complexity of cell populations requires careful selection of reagents to detect cells of interest and distinguish them from other types. Additional reagents are frequently used to provide independent criteria for cell identification. Two or three monoclonal antibodies in combination with forward and right-angle light scatter generate a data set that is difficult to visualize because the data must be represented in four- or five-dimensional space. The separation between cell populations provided by the multiple characteristics is best visualized by multidimensional analysis using all parameters simultaneously to identify populations within the resulting hyperspace. Groups of cells are distinguished based on a combination of characteristics not apparent in any usual two-dimensional representation of the data.