A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study - Zhuang, Jujuan; Ren, Changjing; Ren, Dan; Li, Yu’ang; Liu, Danyang; Cui, Lingyu; Tian, Geng; Yang, Jiasheng; Liu, Jingbo (Frontiers in Oncology, 2021)
Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data taking the advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and combines the selected features (for each functional gene set) to-gether for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hier-archical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluations metrics including Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Homogeneity (HOM) and Completeness (COM). For example, the ARIs of FEGFS are 0.955 and 0.910 respectively on Pollen and Goolam and those of the second-best method are only 0.938 and 0.910, respec-tively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than SC3 (0.798 and 0.807 respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation, and in inferring cell lineage trajectories. As for application, take glioma as an example, we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.