# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 4 Discussion  
We implemented machine learning methods for comparison to establish a reference framework that allows for an objective evaluation of classification performance across diverse single-cell RNA-seq datasets. These baseline models such as SVM, logistic regression, decision trees, KNN, and more are well-established, interpretable, and widely accessible,  
Figure 9: Confusion matrices for cell annotation of models for pancreatic dataset.  
making them suitable starting points for both benchmarking and real-world applications. By assessing their performance, we aim to identify core strengths and limitations in handling high-dimensional, sparse, and biologically complex data. This provides valuable insights into which foundational models are most effective under varying conditions and serves as a benchmarking baseline for comparing more complex or specialized annotation tools in future studies.  
While our study focused on comparison of these methods for cell type annotation, it is essential to understand the underlying reasons for the variation in performance across models. Supervised models such as SVM and logistic regression consistently outperformed others, largely due to their capacity to handle high-dimensional data and exploit complex decision boundaries. SVM, in particular, benefits from kernel transformations that allow it to separate non-linearly distributed classes, which is crucial when distinguishing subtle transcriptional differences between closely related cell types. Logistic regression, though simpler, leverages regularization to maintain generalizability in sparse, high-dimensional spaces typical of scRNA-seq data. Transformers also performed well by utilizing self-attention mechanisms to weigh gene interactions across the input space, capturing contextual gene dependencies. However, their performance, while strong, may be sensitive to training data diversity and require large, high-quality datasets to generalize effectively. In contrast, methods like naive Bayes and KNN performed poorly due to their reliance on assumptions that are violated in scRNA-seq data. Naive Bayes assumes feature independence, which fails in the presence of correlated gene expression, while KNN is susceptible to the curse of dimensionality, where meaningful distance metrics become diluted. The impact of data quality and normalization is profound. The scRNA-seq data are inherently noisy and sparse, necessitating rigorous preprocessing. Quality control removes low-quality cells and artefacts, while normalization corrects for sequencing depth and library size. Inadequate normalization may distort true gene expression signals, leading to biassed model training. For instance, models like SVM and logistic regression are sensitive to feature scaling, and improper normalization may adversely affect margin optimization or coefficient estimation. Moreover, batch effects introduce confounding biases. Without appropriate correction, models may learn batch-specific patterns rather than true biological signals, leading to poor generalization. This is particularly problematic in models like random forest and gradient boosting, which may overfit to batch-specific noise if not properly regularized. Additionally, differences in cell type representation, particularly imbalances or rare subpopulations, can skew classification. Ensemble models may mitigate this by combining multiple decision pathways, but simple models tend to be biassed toward dominant cell types.