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.

细胞命运决定和转变在几乎所有发育过程中都很常见。因此，确定命运决定过程背后的决策机制和重要的个体分子非常重要。在本文中，我们提出了一种基于系统扰动、无监督层次聚类分析（HCA）、机器学习（ML）和沙普利附加解释（SHAP）分析的可解释策略，用于推断单个分子在细胞命运决策中的贡献和重要性。过渡过程。为了验证该方法的可行性，我们将其应用于核心上皮间质转化（EMT）转移网络。EMT转移中确定的关键因素与相关实验观察结果一致。这里提出的方法可以应用于其他生物网络，以识别与细胞命运决定和转变相关的重要因素。