Cluster Analysis of High-Dimensional scRNA Sequencing Data
This work provides a quantitative comparison of single-cell sequencing methods for biological and medical researchers, though it appears incremental as it applies existing clustering analysis to a new dataset.
The researchers analyzed single-cell RNA sequencing data from 36 libraries across six experiments to compare characteristics of different sequencing methods, particularly low-throughput versus high-throughput approaches, and evaluated each method's ability to recover known biological information through clustering analysis.
With ongoing developments and innovations in single-cell RNA sequencing methods, advancements in sequencing performance could empower significant discoveries as well as new emerging possibilities to address biological and medical investigations. In the study, we will be using the dataset collected by the authors of Systematic comparative analysis of single cell RNA-sequencing methods. The dataset consists of single-cell and single nucleus profiling from three types of samples - cell lines, peripheral blood mononuclear cells, and brain tissue, which offers 36 libraries in six separate experiments in a single center. Our quantitative comparison aims to identify unique characteristics associated with different single-cell sequencing methods, especially among low-throughput sequencing methods and high-throughput sequencing methods. Our procedures also incorporate evaluations of every method's capacity for recovering known biological information in the samples through clustering analysis.