# 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 on Multiple Sclerosis Dataset  
Next, we assess the performance of machine learning models for cell annotation on a multiple sclerosis (MS) dataset. SVM and logistic regression achieved the highest accuracies of 88.23% and 88.01%, with corresponding F1-scores of 87.74% and 87.44%. Detailed metrics are presented in Table 2. These results highlight their effectiveness in managing the complexity and heterogeneity of high-dimensional MS-related data. UMAP visualizations (Figure 4) for SVM and logistic regression revealed distinct, well-separated clusters closely aligned with true labels, indicating strong learning and generalization. Both models achieved high precision and recall across most cell types (Figure 5), including transcriptionally similar neuronal and glial populations critical in MS. SVM effectively captured non-linear boundaries via kernel methods, while logistic regression leveraged regularization to maintain high accuracy without overfitting. Their confusion matrices confirmed minimal misclassifications.  
Gradient boosting also performed well (accuracy: 85.12%, F1-score: 84.19%), benefiting from its sequential error correction to improve classification in complex, hierarchical data. UMAP plots showed coherent clusters, though with some overlaps in closely related cell types. Random forest achieved moderate performance (accuracy: 75.76%, F1-score: 73.73%) with reasonable cluster formation, but struggled in regions representing transitional states. Both models exhibited misclassification among similar neuron subtypes (Figure 5), reflecting limitations in resolving overlapping feature spaces.  
Decision tree, KNN, and naive Bayes demonstrated significantly lower performance, underscoring their limitations in handling the complexity of single-cell data. Their underperformance can be attributed to inherent algorithmic constraints when applied to high-dimensional, heterogeneous datasets typical of transcriptomic profiles. Decision trees suffered from overfitting due to their reliance on a single-tree structure, which tends to memorize the training data rather than generalize to unseen examples. This limitation was evident in the UMAP (Figure 4), which displayed poorly separated clusters, and in the confusion matrix (Figure 5), which showed extensive off-diagonal misclassifications, especially among closely related cell types. KNN was hindered by the curse of dimensionality, where the notion of distance central to its functioning becomes less meaningful as the number of features increases. In high-dimensional gene expression space, the Euclidean distance loses its discriminative power, leading to scattered and overlapping clusters in the UMAP and frequent misclassification in the confusion matrix. Naive Bayes assumes conditional independence among features, an assumption that is clearly violated in gene expression data, where genes are often co-expressed or functionally interdependent. This fundamental mismatch between model assumptions and biological reality led to widespread classification errors across all cell types, as illustrated by both UMAP and confusion matrix results. These observations highlight that models relying on oversimplified assumptions or lacking mechanisms to manage high-dimensional interdependencies are ill-suited for annotating complex cellular phenotypes in MS datasets. In contrast, models such as SVM, logistic regression, and gradient boosting demonstrated superior performance due to their capacity to model non-linear relationships, apply regularization, and iteratively refine predictions.  
Figure 4: Cell annotation UMAP plots by various models for multiple sclerosis dataset.  
Figure 5: Confusion matrices for cell annotation of models for multiple sclerosis dataset.