From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells.

ABSTRACT: Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of "supercell statistics", a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a

SUBMITTER: Candia J 

PROVIDER: S-EPMC3763994 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature