Abstract Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as network-based methods Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process, explicitly considering their role in mitigating inherent data noise and potential batch effects for robust cell type identification. Leveraging three independent datasets, PBMC, ROSMAP, and MOp, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that clusters identified by multiresolution Infomap and Leiden show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegeneration and immunology in scRNA-seq.
Nasrollahi et al. (Fri,) studied this question.