  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.
