An Unsupervised Domain Adaptation Approach to Classification of Stem Cell-Derived Cardiomyocytes

Abstract

The use of human embryonic stem cell-derived cardiomyocytes (hESC-CMs) in applications such as cardiac regenerative medicine requires understanding them in the context of adult CMs. Their classification in terms of the major adult CM phenotypes is a crucial step to build this understanding. However, this is a challenging problem due to the lack of labels for hESC-CMs. Adult CM phenotypes are easily distinguishable based on the shape of their action potentials (APs), but it is still unclear how these phenotypes are expressed in the APs of hESC-CM populations. Recently, a metamorphosis distance was proposed to measure similarities between hESC-CM APs and adult CM APs, which led to state-of-the-art performance when used in a 1 nearest neighbor scheme. However, its computation is prohibitively expensive for large datasets. A recurrent neural network (RNN) classifier was recently shown to be computationally more efficient than the metamorphosis-based method, but at the expense of accuracy. In this paper we argue that the APs of adult CMs and hESC-CMs intrinsically belong to different domains, and propose an unsupervised domain adaptation approach to train the RNN classifier. The idea is to capture the domain shift between hESC-CMs and adult CMs by adding a term to the loss function that penalizes their maximum mean discrepancy (MMD) in feature space. Experimental results in an unlabeled 6940 hESC-CM dataset show that our approach outperforms the state of the art in terms of both clustering quality and computational efficiency. Moreover, it achieves state-of-the-art classification accuracy in a completely different dataset without retraining, which demonstrates the generalization capacity of the proposed method.