  A single solid tumor, composed of nearly identical cells, exhibits
heterogeneous dynamics. Cells dynamics in the core is glass-like whereas those
in the periphery undergo diffusive or super-diffusive behavior. Quantification
of heterogeneity using the mean square displacement or the self-intermediate
scattering function, which involves averaging over the cell population, hides
the complexity of the collective movement. Using the t-distributed stochastic
neighbor embedding (t-SNE), a popular unsupervised machine learning
dimensionality reduction technique, we show that the phase space structure of
an evolving colony of cells, driven by cell division and apoptosis, partitions
into nearly disjoint sets composed principally of core and periphery cells. The
non-equilibrium phase separation is driven by the differences in the
persistence of self-generated active forces induced by cell division. Extensive
heterogeneity revealed by t-SNE paves way towards understanding the origins of
intratumor heterogeneity using experimental imaging data.
