# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 5 Conclusions  
This study systematically evaluated machine learning models for cell type annotation across PBMC, multiple sclerosis, and Zhengh68k, and pancreatic datasets. The models evaluated included decision tree, random forest, gradient boosting, KNN, naive Bayes, SVM, logistic regression, and transformer. Among the tested models, SVM and logistic regression consistently achieved superior performance, marked by high accuracy, F1-scores, and minimal misclassifications. SVM's kernel-based approach excelled at capturing non-linear relationships in high-dimensional data, while logistic regression's simplicity and regularization ensured robust and reliable predictions. The transformer model also demonstrated strong potential, leveraging self-attention mechanisms to capture complex dependencies in gene expression data. Its superior clustering of cell types underscores its effectiveness, although computational demands remain a consideration for large-scale applications. Similarly, gradient boosting performed well, with its iterative learning approach enabling it to handle complex patterns, albeit with occasional overlaps in closely related cell types. Random forest and decision trees displayed moderate performance, benefiting from ensemble methods but struggling with overlapping classes. Simpler models, such as KNN and naive Bayes, performed poorly due to limitations in handling high-dimensional and interdependent gene expression data. The findings highlight the critical role of model selection in single cell analysis, with advanced models like SVM, logistic regression, and transformers offering significant advantages for single-cell annotation. However, challenges in perfectly separating highly similar cell types indicate opportunities for further refinement. Our findings underscore the importance of selecting robust and sophisticated models for single-cell applications to ensure the reliability and accuracy of cell type annotations.  
Conceptualization, S.A.W. and S.Q.; methodology, S.A.W.; software, S.A.W., validation, S.A.W., S.Q., M.S.M. and Y.G.; formal analysis, S.A.W., M.S.M. and Y.G.; resources, M.S.M. and Y.G.; data curation, S.A.W.; writing--original draft preparation, S.A.W.; writing--review and editing, S.A.W. and S.Q.; visualization, Y.G.; supervision, S.Q.; project administration, S.A.W., S.Q., M.S.M. and Y.G.; funding acquisition, M.S.M. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Deanship of Scientific Research, the Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, under the project (KFU250055).  
The code used to generate the results presented in this study is available at: [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) CZTtauk88 WXkvqTlAsJwdZHGkMkP2e_D?usp=drive_link]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) CZTtauk88 WXkvqTlAsJwdZHGkMkP2e_D?usp=drive_link) (accessed on 11 April 2025).  
The authors declare no conflict of interest.