# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 1 Introduction
After clustering, labels are assigned using marker genes. However, their accuracy depends on selecting the right clustering resolution--too many clusters may create unnecessary subgroups, while too few may miss rare cell types. Also, interpreting the biological meaning of clusters can be difficult, as these methods do not always provide functional insights. Supervised learning-based cell annotation uses machine learning models trained on labelled reference datasets to classify new, unlabeled scRNA-seq data [22; 23]. These models learn patterns in gene expression to identify cell types and can capture complex relationships in high-dimensional data. Common algorithms include random forest, SVM, k-NN, neural networks, and transformers. Tools such as canonical correlation analysis (CCA) [24], MNNCorrect [25], scAnnotate [26], scClassify [27], SingleCellNet [28], Moana [29], and LAmbDA [30] apply supervised learning for annotation. CCA [24] and MNNCorrect [25] integrate 'query' data with known datasets to analyze gene expression and gene-to-gene correlations. scAnnotate [26] uses random forest along with batch correction tools like Harmony and Seurat, and combines results using models like Elastic Net or XGBoost. SingleCellNet [28] applies a Random Forest model to compute similarity scores for each query cell and detect transition states or ambiguous identities. Moana combines SVM with hierarchical classification to refine labels, while LAmbDA [30] applies neural networks in a transfer learning framework to correct for batch effects across datasets. The main benefit of supervised methods is their ability to capture complex biological patterns and perform hierarchical classification. However, their accuracy depends on how well the training data represents all relevant cell types, and missing cell types in the reference can lead to misclassification. Recently, deep learning techniques have demonstrated significant success across diverse scientific domains, encompassing medical imaging, text analysis, and single-cell studies [31; 32; 33; 34; 35; 36]. Deep learning-based supervised methods such as scPred [37], scDeepSort [38], scBERT [12], GPT-4 [39], and scGPT [40] are employed to discover complex patterns in scRNA-seq data. These methods often outperform traditional unsupervised techniques by leveraging large amounts of annotated data. For example, scPred [37] applies principal component analysis (PCA) for dimensionality reduction followed by a support vector machine (SVM) classifier for cell classification. scDeepSort [38] utilizes a weighted graph neural network (GNN) trained on gene-cell graphs, enabling cell type annotation without prior knowledge integration. scBERT [12], adapted from the BERT architecture [41], leverages pre-training and fine-tuning on gene expression data to capture complex cellular relationships and mitigate batch effects. [PERSON] et al. [39] evaluates the performance of GPT-4 for cell annotation across 10 datasets from five species, demonstrating over 75% accuracy for most cell types by leveraging marker gene information. The single-cell generative pre-trained transformer (scGPT) [40], a domain-adapted generative pre-trained transformer model, supports cell annotation, multi-modal integration, and perturbation prediction using self-attention mechanisms, requiring large-scale pre-training for effective representation learning. Hybrid annotation approaches combine supervised and unsupervised methods to make cell type identification in scRNA-seq data more accurate and reliable. These methods use multiple reference datasets to minimize batch effects and capture the hierarchical structure of cell types. Tools like scClassify [27] and CHETAH [42] demonstrate this strategy. scClassify employs ensemble learning with k-nearest neighbours to build a hierarchical classification tree and can assign intermediate or \"unassigned\" labels when reference mismatches occur. CHETAH iteratively refines annotations by comparing cells to subgroups of reference types.