High-precision Multiclass Cell Classification by Supervised Machine Learning on Lectin Microarray Data

Introduction: Establishment of a cell classification platform for evaluation and selection of human pluripotent stem cells (hPSCs) is of great importance to assure the efficacy and safety of cell-based therapy. In our previous work, we introduced a discriminant function that evaluates pluripotency from the cells' glycome. However, it is not yet suitable for general use.

Methods: The current study aims to establish a high-precision cell classification platform introducing supervised machine learning and test the platform on glycome analysis as a proof-of-concept study. We employed linear classification and neural network to the lectin microarray data from 1577 human cells and categorized them into five classes including hPSCs.

Results: The linear-classification-based model and the neural-network-based model successfully predicted the sample type with accuracies of 89% and 97%, respectively.

Conclusions: Because of the high recognition accuracies and the small amount of computing resources required for these analyses, our platform can be a high precision conventional cell classification system for hPSCs.