# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 3 Results and Discussion
### Performance Evaluation of Machine Learning Models for Cell Annotation of Pancreatic Dataset  
The performance of various machine learning models for pancreatic cell annotation was assessed using UMAP visualizations, confusion matrices, accuracy, and F1-scores. The Umap and confusion matrix results are given in Figures 8 and 9, respectively. Among the ML methods, SVM, transformer, and gradient boosting emerged as the top-performing methods, as evidenced by their high accuracy and F1-score (Table 2). Methods like KNN, logistic regression, and decision tree demonstrated moderate performance, while random forest and naive Bayes were the worst performing methods. The decision tree classifier performed well, achieving an accuracy of 0.9834 and an F1-score of 0.9846 in the pancreas dataset (Table 2). However, the confusion matrix for the dataset suggests that it has misclassified some minority cell types, indicating overfitting to majority classes. Random forest, slightly lower in performance (0.9810 accuracy, 0.9803 F1-score), showed better generalization than the decision tree, with more balanced predictions but still some confusion in closely related cell types. Gradient boosting achieved high accuracy (0.9941) and F1-score (0.9969), demonstrating robust cell annotation capability. The UMAP visualization shows clear, well-separated clusters aligning closely with the true labels, confirming its superior performance in distinguishing cell types. KNN also performed well (0.9921 accuracy, 0.9958 F1-score), though its confusion matrix shows slightly more misclassification compared to gradient boosting. This suggests that while KNN captures local structures well, it may struggle with boundary cases. The naive Bayes classifier performed the worst among all models (0.8578 accuracy, 0.8456 F1-score), with significant misclassification in the confusion matrix. This re  
Figure 7: Confusion matrix for cell annotation of models for Zhengh68K dataset. sult is expected due to the model's assumption of feature independence, which does not hold well in transcriptomic data. The UMAP plot for naive Bayes shows poor separation of cell types, with overlapping clusters indicating a failure to capture complex relationships. In contrast, support vector machine (SVM) achieved the highest performance (0.9953 accuracy, 0.9976 F1-score), with well-separated clusters in the UMAP visualization and minimal misclassification in the confusion matrix, making it the most reliable method for cell annotation. Logistic regression (accuracy: 0.9921, F1-score: 0.9934) performed comparably to KNN but struggled slightly in distinguishing closely related cell types, as observed in the confusion matrix. The transformer-based model achieved 0.9943 accuracy and 0.9958 F1-score, performing similarly to gradient boosting and SVM. The UMAP plot for the transformer model shows clear and well-defined clusters, suggesting that it effectively captures non-linear relationships and latent patterns in high-dimensional data. The performance trends observed in the PBMC dataset largely align with the results from the pancreas dataset. SVM, gradient boosting, and transformer-based models consistently outperformed other methods, achieving high accuracy and well-separated clusters in UMAP. Naive Bayes performed poorly in both datasets, reinforcing its limitations in handling high-dimensional single-cell data. The confusion matrices reveal that models like decision tree and random forest, while effective, exhibit occasional misclassification, particularly for rare cell types. Overall, SVM, gradient boosting, and transformer-based models demonstrated the best performance for cell annotation, with high accuracy, well-clustered UMAP plots, and minimal misclassification in the confusion matrices. Naive Bayes performed the worst, while KNN and logistic regression were moderately effective. The results are consistent for pancreatic datasets, highlighting the robustness of advanced models like SVM and transformers for single-cell transcriptomic classification.