STEAM incorporates various machine learning models, including Random Forest, XGBoost, and SVM, to assess the prediction accuracy and consistency of clusters, along with statistical metrics like Kappa score, F1 score, ARI (Adjusted Rand Index), etc. We demonstrated the capability of STEAM on multi-cell and single-cell resolution spatial transcriptomics and proteomics. **Notably, STEAM supports multi-sample training, enabling the evaluation of cross-replicate clustering consistency.** Furthermore, we used STEAM to evaluate the performance of spatial-aware and spatial-ignorant clustering methods, offering researchers a valuable tool for more informed result interpretation.
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