# 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
After normalization (e.g., log transformation), it calculates the probability of each cell type and assigns the one with the highest value. Its speed and ability to handle many genes make it useful for analyzing complex genomic data and quickly adapting to new inputs. Support vector machines (SVM) are a robust and versatile machine learning method widely used for classifying cell types in single-cell RNA sequencing (scRNA-seq) data due to their ability to handle high-dimensional and complex data [43]. The core principle of a SVM is to find an optimal hyperplane that separates different cell types in the gene expression space with the maximum margin, minimizing misclassification [52]. This \"margin maximization\" enhances the model's generalizability, making it effective in distinguishing between closely related cell types [53]. In cases where cell types are not linearly separable, SVM employs kernel functions, such as the radial basis function (RBF), to transform the data into a higher-dimensional space where a separating hyperplane can be identified. This enables SVM to capture non-linear relationships between genes that are crucial for accurate classification. Despite its strengths, SVM can be computationally expensive, especially for large datasets, due to the need to compute pairwise distances between data points. However, optimized implementations and support for parallelization have improved scalability. The ability of SVM to classify cells based on their gene expression profiles with high accuracy, even in the presence of noise and non-linearity, makes it a valuable tool for single-cell annotation tasks. Logistic regression is a machine learning method used for classifying cell types in single-cell RNA sequencing (scRNA-seq) data due to its simplicity, interpretability, and probabilistic output [54]. The goal of logistic regression is to model the probability that a given cell, represented by its gene expression profile, belongs to a specific cell type. This is achieved by fitting a linear model to the data and applying a logistic (sigmoid) function to map the output to a probability value between 0 and 1. In the case of multiple cell types, logistic regression extends to multinomial logistic regression, enabling classification across several classes simultaneously. Logistic regression computes a weighted sum of gene expression values, where the weights correspond to the model's learned parameters. These weights indicate the contribution of each gene to the classification decision, making the model's predictions biologically interpretable. One of the key strengths of logistic regression is its probabilistic output, which provides a confidence score for each predicted cell type. This allows researchers to interpret and rank classifications based on certainty. Logistic regression is also resistant to over-fitting in low-dimensional settings but may struggle with high-dimensional data, a common challenge in scRNA-seq studies. To address this, regularization techniques, such as L1 (lasso) and L2 (ridge) penalties, are applied to prevent over-fitting by constraining the magnitude of the weights. The transformer is a model which was originally developed for natural language processing, but now it is being used to annotate and identify cell types in single-cell RNA sequencing data [55]. The powerful architecture of the model can be used effectively to handle the complexities of scRNA-seq data by offering sophisticated ways of classifying cell types. Transformers involve an attention mechanism which weighs important parts against other parts of the input data, making the model focus more on the relevant genes than others, for cell type classification [12]. This results in very useful subtleties for scRNA-seq data where under most circumstances, expression is gained for a set of specific genes that indicate cell type rather than others.