A Comparison of Cell Type Identification for Single-Cell RNA Sequencing Data Analysis
Since single-cell RNA sequencing (scRNA-Seq) was introduced to the biology community, it has been a powerful tool for different applications. The developers in the community released several open-source software packages to analyze high-throughput biological data rapidly. The data analysis pipeline has become relatively straightforward to the community. However, the cell type identifying method remains the most challenging part throughout the scRNA-Seq analysis workflow and relies heavily on prior knowledge when defining cell type manually. Fortunately, more computational approaches on cell type annotation were releasing with recent advancements in technologies. However, the results appear to be varied when comparing one another approaches. In this dissertation, various computational techniques for cell-type identification are surveyed, and their performances are evaluated on benchmark scRNA-Seq datasets. This study facilitates the prospective users to select existing technologies for cell type identifications in single-cell scRNA-seq analysis.