ABSTRACT: Accurate prediction of cell cycle phases is important in understanding how certain diseases (e.g. cancer) develop and respond to treatment. It is also essential for reducing the confounding effects in single-cell RNA sequencing (scRNA-Seq) data analysis. We evaluated both traditional machine learning techniques and deep learning approaches for predicting cell cycle phases from scRNA-seq data. Models were trained on consensus predictions from four popular cell cycle analysis tools, namely, CellCycleScore, ccAF, Revelio, and Tricycle, then applied to unlabeled scRNA-seq data generated from human leukemia cell lines. We evaluated model performance using publicly available datasets, GSE146773 (human leukemia cells, 1,151 cells) and GSE64016 (human embryonic stem cells, 213 cells), both labeled with their respective cell cycle phases through experiments. In addition to the four tools, we assessed traditional machine learning models (AdaBoost, Random Forest, and LightGBM), deep learning models including multiple dense networks DNN (2-5), CNN, Hybrid CNN-Dense, Feature embedding, and ensemble models. DNN 3 achieved the highest accuracy, reaching 75.16% on the GSE146773 dataset and 70.85% on GSE64016. This outperformed existing tools such as Revelio (68.34% on GSE146773, 68.2% on GSE64016), CellCycleScore (66.54% on GSE146773, 53.84% on GSE64016), ccAF (45% on GSE146773, 40% on GSE64016), ccAFv2 (47.23% on GSE146773, 43.72% on GSE64016), Tricycle (53.23% on GSE146773, 58.7% on GSE64016), and Cyclum (38.52% on GSE146773, 26.3% for GSE64016). These results signify the potential of deep learning models for robust cell cycle phase prediction in scRNA-seq data.
