2019/12/18 by Jiawei Long, Yu Xia, Long, Jiawei +1
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #Cancer-related molecular mechanisms research #E.0 #Extracellular vesicles in disease #FOS: Computer and information sciences #Machine Learning (stat.ML) #Single-cell and spatial transcriptomics
paper · pdf · doi:10.48550/arxiv.1912.08400
openalex publication_date 2019/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.