  Cellular phenotypic features derived from histopathology images are the basis
of pathologic diagnosis and are thought to be related to underlying molecular
profiles. Due to overwhelming cell numbers and population heterogeneity, it
remains challenging to quantitatively compute and compare features of cells
with distinct molecular signatures. In this study, we propose a self-reliant
and efficient analysis framework that supports quantitative analysis of
cellular phenotypic difference across distinct molecular groups. To demonstrate
efficacy, we quantitatively analyze astrocytomas that are molecularly
characterized as either Isocitrate Dehydrogenase (IDH) mutant (MUT) or wildtype
(WT) using imaging data from The Cancer Genome Atlas database. Representative
cell instances that are phenotypically different between these two groups are
retrieved after segmentation, feature computation, data pruning, dimensionality
reduction, and unsupervised clustering. Our analysis is generic and can be
applied to a wide set of cell-based biomedical research.
