# 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 PBMC Datasets  
This section summarizes the performance of different machine learning models for cell type annotation on a peripheral blood mononuclear cell (PBMC) dataset. The SVM model outperformed the others, achieving the highest accuracy, 95.83% and F1-score, 95.73%, highlighting its strong ability to manage complex, high-dimensional data in multi-class classification. Detailed metrics are shown in Table 2. SVM excels by using kernel transformations, allowing it to separate non-linearly distributed classes, an essential feature for detecting subtle transcriptional differences between closely related cell types. Logistic regression and transformers also displayed strong performances, with logistic regression displaying accuracy of 95.27% and an F1-score of 95.15%, while transformers showed 93.37% accuracy and 92.99% F1-score (Table 2), suggesting their effectiveness in capturing linear and non-linear relationships, respectively, within the data. The UMAP plots, coloured by predicted labels in Figure 2, visually underscore the clustering effectiveness of each model. These plots closely resembled the ground truth with well-separated and compact clusters, particularly for dominant cell types such as CD14+ monocytes, CD4 naive, and CD8 TEM_1. The corresponding confusion matrices shows high diagonal dominance with minimal off-diagonal misclassifications, indicating strong predictive accuracy and class separability (Figure 3). SVM and logistic regression showed high precision across most cell types, particularly in identifying CD4 T cells and B cells with minimal false positives, which is crucial for reducing misdiagnosis in clinical settings. Logistic regression models linear patterns well and uses regularization to avoid overfitting, while transformers use self-attention to capture gene-level context, making them highly effective for detecting subtle signals in multi-class classification. Gradient boosting exhibited strong performance, with an accuracy of 92.99% and an F1-score of 92.89%, leveraging its ensemble and sequential learning framework to effectively model complex patterns while minimizing overfitting. UMAP visualizations revealed well-defined clusters, though slight overlaps between monocyte subtypes indicated challenges in resolving closely related cell types (Figure 2). Random forest outperformed decision trees, benefitting from ensemble aggregation that improved generalization, though both models showed confusion between CD14+ monocytes and CD8_TEM subtypes, reflecting limited discriminative power in high-dimensional scRNA-seq data. Random forest provided better robustness, yet its lack of iterative error correction, unlike gradient boosting, limited its ability to capture complex gene-level dependencies. While decision trees offer interpretability, their susceptibility to overfitting was evident in dispersed and overlapping clusters. KNN and naive Bayes exhibited poor performance, with accuracies near 50% (Table 2), reflecting their limitations in handling high-dimensional, sparse scRNA-seq data. KNN's reliance on distance metrics suffers from the curse of dimensionality, while naive Bayes assumption of feature independence is incompatible with gene expression data, where complex interdependencies exist. Both models showed widespread misclassifications across cell types, particularly between closely related populations such as memory and naive T cells. UMAP plots revealed dispersed, overlapping clusters (Figure 2), confirming their inability to capture underlying biological variation. In contrast, models like SVM, logistic regression, transformers, and gradient boosting effectively addressed the challenges of high-dimensional feature spaces through kernel transformations, regularization, self-attention, and sequential error correction, respectively.