# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 1 Introduction  
Cells are the building blocks of life, playing a central role in every aspect of an organism's functioning. The human body is made up of approximately 30 trillion cells, each with its designated functions [1]. The functioning of tissue and body depends upon the interplay of individual cells. They operate both individually and collaboratively, carrying out specific duties that maintain health and balance within the body. By studying cells, we gain insights into the complex systems that govern development, health, and disease. Understanding cells has practical implications across various fields such as medicine and pathology. Exploring how cells operate helps us understand diseases better, develop targeted therapies, and even pave the way for breakthroughs in personalized medicine [2]. This knowledge is instrumental in crafting treatments that are tailored to individual needs, potentially revolutionizing how we approach illness and health care. Recent advances in high-throughput single-cell RNA sequencing (scRNA-seq) have significantly enhanced our capacity to analyze gene expression at the resolution of individual cells. The advent of scRNA-seq has opened up new avenues in cell research, particularly in identifying and classifying cell types [3]. This technology allows for precise examination of how cells differ from one another, even within the same tissue, enhancing our understanding of their specific roles and interactions. The insights gained from scRNA-seq are not justacademic; they have practical applications in developing better diagnostic tools [4], refining therapeutic strategies [5], and advancing tissue engineering [6]. scRNA-seq has revolutionized biology and medicine by enabling detailed characterization of complex tissue composition, identification of new and rare cell types [7], understanding of developmental stages [8], and analysis of cellular responses to perturbations [9]. A major goal in scRNA-seq data analysis is the classification of cellular phenotypes, such as cell type annotation. Cell annotation is the process of categorizing and labelling cells based on their gene expression profiles, typically derived from single-cell RNA sequencing (scRNA-seq) data [10; 11]. Cell annotation is essential for studying disease progression and tumour microenvironments [12]. Accurate identification is crucial because it allows scientists to map out the diverse landscape of cells within a body, explore their unique roles in both healthy and diseased states, and unearth new cellular types that could be critical in understanding life's complexities [13]. This categorization helps researchers understand the cellular identity and functions within heterogeneous tissues. Cell annotation allows researchers to identify different cell types within mixed populations, enabling the separate study of each cell type and their interactions. Thus, cell annotation is one of the most significant applications of scRNA-seq to study the biological process and advancing medical practice. Cell annotation methods can be broadly classified into manual and automated approaches. Manual annotation relies on identifying known marker genes specific to certain cell types. Researchers inspect gene expression clusters and assign labels based on these markers [14]. Databases like CellMarker [15], PanglaDB [16], and CancerSEA [17] provide curated lists of such marker genes. Tools like scCATCH [18], SCSA [19], CellAssign [20], and SCINA [21] use statistical models to fit marker gene distributions and assign labels accordingly. For instance, scCATCH builds tissue-specific reference datasets and matches gene expression profiles to a predefined taxonomy, while CellAssign applies a Bayesian probabilistic model to label single cells. Automated methods are increasingly used for their speed and ability to handle large datasets. These include unsupervised, supervised, and hybrid learning approaches. Unsupervised methods, like t-SNE, UMAP, and Louvain, group cells based on gene expression without prior labels.