Application of scRNA-seq in Investigating Immune Cell Subtypes in PBMC

Abstract

Bulk RNA sequencing produces population-average data, but unable to group the cells into different cell types and thus loses the heterogeneity. Hence, we applied single-cell RNA sequencing to the Peripheral Blood Mononuclear Cells (PBMC) dataset to investigate the underlying distinct cell types in the PBMC sample. To extract important information and reduce the dimensionality we have to investigate when facing complicated datasets, we applied the principal component analysis (PCA). We included the first 10 PCs, applied UMAP non-linear dimensionality reduction and clustered 9 cell subtypes and corresponding marker genes. We searched for the biomarkers for each cell type and assigned cell identities to these 9 immune cell types according to prior knowledge. Our results demonstrated the power of scRNA-seq in distinguishing cell subtypes in a complicated mix of samples, providing insights into important biological questions.