Single-Cell Automated Annotation Algorithm Based on Reference Expression Profiles

Abstract

In recent years, the rapid advancement of single-cell RNA sequencing (scRNA-seq) technology has provided a powerful tool for delving into the diversity of cellular populations, offering researchers a unique perspective to explore intracellular heterogeneity. This technology enables us to gain profound insights into the gene expression patterns of individual cells, unveiling latent heterogeneity within cell populations. Accurately predicting single-cell types is a crucial step in understanding the dynamics and functional impacts of cells. This paper aims to introduce the application of hypergeometric testing in gene set enrichment as a foundational tool for predicting single-cell types. In comparison to traditional gene expression analysis methods, scRNA-seq captures individual differences in each cell, presenting unprecedented opportunities for understanding development, diseases, and tissue functionality.