Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

ABSTRACT: Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent sampling

SUBMITTER: Singh SP 

PROVIDER: S-EPMC6249247 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature