# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 2 Materials and Methods
### Machine Learning Techniques for Comparative Analysis of Single Cell Annotation  
Decision trees are a commonly employed machine learning approach for classifying cell types in single-cell RNA sequencing (scRNA-seq) data due to their simplicity and interpretability [44, 45]. A decision tree constructs a flowchart-like model where each internal node represents a \"decision\" based on the expression level of a particular gene, branches correspond to possible outcomes, and leaf nodes represent predicted cell types. In practice, decision trees make sequential decisions, guiding each new cell's classification from the tree'sroot to a specific leaf based on its gene expression, which simplifies understanding complex biological data and provides clear insights into gene relevance. Decision trees are favoured for their intuitive nature and ability to clearly explain their decision-making process. However, decision trees are prone to over-fitting, especially when the dataset contains many features and noise. To address this, strategies such as pruning (removing branches that contribute little to predictive accuracy) and setting depth limits are commonly used. Random forest is an ensemble learning technique widely used for classifying cell types in single-cell RNA sequencing data [46]. Random forest is a popular ensemble learning method for classifying cell types in scRNA-seq data. It builds multiple decision trees using random subsets of data and features (bagging), reducing overfitting and improving accuracy [47]. Each tree predicts a cell type, and the final result is based on majority voting. This approach handles noisy and imbalanced data well, making it suitable for complex biological datasets. However, it can be computationally demanding for large datasets due to the high number of trees. Gradient boosting is a robust machine learning method often used for classifying cell types in single-cell RNA sequencing data for accurate cell type classification. Unlike random forest, it builds trees sequentially, where each tree corrects the errors of the previous ones using a method called \"boosting\". This is performed by minimizing a loss function through gradient descent [48]. Gradient boosting models, such as XGBoost, are popular for their speed, ability to handle missing data, and for providing feature importance to highlight key genes [49]. However, training can be time-consuming due to the sequential nature of the process, especially with large datasets. K-nearest neighbour (KNN) is a simple, non-parametric, distance-based algorithm used in scRNA-seq data for cell type classification [50]. It assumes that similar cells are close in gene expression space. For a given cell, KNN finds its k-nearest neighbours using  
Figure 1: Single-cell annotation pipeline illustrates the steps involved in annotating cell types. (**a**) Cell isolation and count matrix generation involves isolating individual cells and creating a cell-by-gene count matrix; (**b**) pre-processing techniques highlight the crucial pre-processing steps applied to the raw count matrix, including quality control, normalization feature identification, and batch correction; (**c**) downstream analysis for cell annotation involves clustering cells, identifying marker genes, and assigning cell type labels using known markers or reference datasets. a distance metric (e.g., Euclidean or cosine) and assigns the most common label among them. Choosing the right K is crucial; too small can be noisy, too large can blur distinctions. Tools like Seurat use KNN for clustering, and its simplicity and effectiveness in capturing subtle patterns make it useful for classifying closely related cell types [4]. Naive Bayes is a simple, probabilistic algorithm used for cell type classification in scRNA-seq data [51]. It calculates the probability of each cell type from gene expression levels, assuming each gene's presence is independent of others. Normalization, like log-transformation, ensures that gene expressions are comparable across different cells.