Classification method for heterogeneity in monoclonal cell population

Monoclonal cell populations are known to be composed of heterogeneous subpopulations, thus complicating the data analysis. To gain clear insights into the mechanisms of cellular systems, biological data from a homogeneous cell population should be obtained. In this study, we developed a method based on Latent Profile Analysis (LPA) combined with Confirmatory Factor Analysis (CFA) to divide mixed data into classes, depending on their heterogeneity. In general cluster analysis, the number of measured points is a constraint, and thereby the data must be classified into fewer groups than the number of samples. By our newly developed method, the measured data can be divided into groups depending on their latent effects, without constraints. Our method is useful to clarify all types of omics data, including transcriptome, proteome and metabolic information.