COMPARATIVE ANALYSIS OF REFERENCE-BASED CELL TYPE MAPPING AND MANUAL ANNOTATION IN SINGLE CELL RNA SEQUENCING ANALYSIS - Larisa Goričan; Boris Gole; Gregor Jezernik; Gloria Krajnc; Uroš Potočnik; Mario Gorenjak (Slovenian Veterinary Research)
Single-cell RNA sequencing (scRNA-seq) offers unprecedented insight into cellular diversity in complex tissues like peripheral blood mononuclear cells (PBMC). Furthermore, differential gene expression at a single-cell level can provide a basis for understanding the specialized roles of individual cells and cell types in biological processes and disease mechanisms. Accurate annotation of cell types in scRNA-seq datasets is, however, challenging due to the high complexity of the data. Here, we compare two cell-type annotation strategies applied to PBMCs in scRNA-seq datasets: automated reference-based tool Azimuth and unsupervised Shared Nearest Neighbor (SNN) clustering, followed by manual annotation. Our results highlight the strengths and limitations of the two approaches. Azimuth easily processed large-scale scRNAseq datasets and reliably identified even relatively rare cell populations. It, however, struggled with cell types outside its reference range. In contrast, unsupervised SNN clustering clearly delineated all the different cell populations in a sample. This makes it well suited for identifying rare or novel cell types, but the method requires time-consuming and bias-prone manual annotation. To minimize the bias, we used rigorous criteria and the collaborative expertise of multiple independent evaluators, which resulted in the manual annotation that was closely related to the automated one. Finally, pseudo-temporal analysis of the major cell types further confirmed the validity of the Azimuth and manual annotations. In conclusion, each annotation method has its merits and downsides. Our research thus highlights the need to combine different clustering and annotation approaches to manage the complexity of scRNA-seq and to improve the reliability and depth of scRNA-seq analyses.