# 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 Zheng68k Datasets  
In this study, we evaluated the performance of machine learning models on the Zheng68k dataset for single-cell annotation. As shown in Table 2, SVM and logistic regression achieved the highest accuracy and F1-scores, followed by the transformer and gradient boosting. Random forest, decision tree, and KNN delivered moderate results, while naive Bayes performed the poorest. Clustering and classification outcomes are visualized in the UMAP plots and confusion matrices in Figures 6 and 7. Support vector machine (SVM) and logistic regression emerged as the top-performing models for cell type classification, achieving accuracies of 84.76% and 84.40%, and F1-scores of 84.72% and 84.15% (Table 2), respectively. SVM demonstrated robust performance with minimal misclassifications and clear, well-separated clusters in UMAP space (Figure 6), reflecting its capacity to model complex non-linear scenarios. Despite its inherently linear nature, logistic regression demonstrated strong performance, attributed to the incorporation of regularization techniques that mitigated overfitting and facilitated robust generalization across heterogeneous cell populations. The transformer model also ranked among the best performers (accuracy: 83.54%, F1-score: 83.30%), leveraging its self-attention mechanism to capture intricate gene-gene dependencies, as evidenced by distinct clustering of major immune cell types. The UMAP plot showed distinct clusters, especially for major cell types like CD4+ T helper and CD8+ cytotoxic T cells (Figure 5), indicating the model's ability to capture complex gene relationships through its attention mechanism. Gradient boosting followed with competitive performance (accuracy: 77.90%, F1-score: 77.29%), benefitting from its iterative refinement process that allowed it to model complex non-linear relationships. Its UMAP plots showed well-separated clusters, especially for distinct cell types like naive T cells and CD8+ cytotoxic T cells, indicating the model's ability to learn complex non-linear relationships in gene expression data. However, its performance was slightly lower, likely due to sensitivity to noise and class imbalance. Random forest outperformed the decision tree model with improved accuracy (73.17%) and F1-score (71.77%) by leveraging ensemble learning, which reduced overfitting and improved robustness; however, it still struggled with closely related cell types like monocytes and dendritic cells, as reflected in the confusion matrix and partially overlapping UMAP clusters. The UMAP plot showed more defined clusters with less overlap between cell types, suggesting that random forest's ensemble learning approach helped improve the model's robustness to noise and over-fitting. However, while random forest performs well in capturing relationships in the data, it still faces challenges in distinguishing highly similar cell types, particularly in the case of memory T cells and naive T cells. Transformer models delivered strong results by capturing complex gene-gene relationships through self-attention mechanisms, although their computational demands limit scalability. Gradient boosting slightly outperformed Random Forest by iteratively correcting errors, demonstrating better separation in UMAP plots, yet still misclassified similar cell types. Decision tree achieved moderate accuracy (69.58%) but showed poor generalization, with overlapping UMAP clusters and frequent confusion between transcriptionally similar cells such as naive and memory T cells, due to its inability to model non-linear boundaries. These models leveraged ensemble learning, which helps mitigate overfitting and improves performance by aggregating multiple weak learners. However, they still struggled with distinguishing closely related cell types, indicating that ensemble models, while effective, are not always perfect at separating similar classes.