Identifying key factors in cell fate decisions by machine learning interpretable strategies

Cell fate decisions and transitions are common in almost all developmental processes. Therefore, it is important to identify the decision-making mechanisms and important individual molecules behind the fate decision processes. In this paper, we propose an interpretable strategy based on systematic perturbation, unsupervised hierarchical cluster analysis (HCA), machine learning (ML), and Shapley additive explanation (SHAP) analysis for inferring the contribution and importance of individual molecules in cell fate decision and transition processes. In order to verify feasibility of the approach, we apply it to the core epithelial to mesenchymal transition (EMT)-metastasis network. The key factors identified in EMT-metastasis are consistent with relevant experimental observations. The approach presented here can be applied to other biological networks to identify important factors related to cell fate decisions and transitions.

Automated summary

In this paper, a strategy based on systematic perturbation, unsupervised hierarchical cluster analysis (HCA), machine learning (ML), and Shapley additive explanation (SHAP) analysis is proposed to infer the contribution and importance of individual molecules in cell fate decision and transition processes. The feasibility of the approach is verified by applying it to the core epithelial to mesenchymal transition (EMT)-metastasis network, and the key factors identified are consistent with relevant experimental observations. This approach can be applied to other biological networks to identify important factors related to cell fate decisions and transitions.