Guo Minzhe, Xu Yan
The Perinatal Institute, Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Methods Mol Biol. 2018;1751:209-222. doi: 10.1007/978-1-4939-7710-9_15.
Genome-scale single-cell biology has recently emerged as a powerful technology with important implications for both basic and medical research. There are urgent needs for the development of computational methods or analytic pipelines to facilitate large amounts of single-cell RNA-Seq data analysis. Here, we present a detailed protocol for SINCERA (SINgle CEll RNA-Seq profiling Analysis), a generally applicable analytic pipeline for processing single-cell data from a whole organ or sorted cells. The pipeline supports the analysis for the identification of major cell types, cell type-specific gene signatures, and driving forces of given cell types. In this chapter, we provide step-by-step instructions for the functions and features of SINCERA together with application examples to provide a practical guide for the research community. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html .
基因组规模的单细胞生物学最近已成为一项强大的技术，对基础研究和医学研究都具有重要意义。迫切需要开发计算方法或分析流程，以促进对大量单细胞RNA测序数据分析。在这里，我们展示了SINCERA（单细胞RNA测序分析）的详细方案，这是一种普遍适用的分析流程，用于处理来自整个器官或分选细胞的单细胞数据。该流程支持对主要细胞类型、细胞类型特异性基因特征以及特定细胞类型的驱动因素进行识别分析。在本章中，我们提供了SINCERA功能和特性的逐步说明以及应用示例，为研究界提供实用指南。SINCERA是用R语言实现的，遵循GNU通用公共许可证v3许可，可从辛辛那提儿童医院医学中心（CCHMC）的PBGE网站（https://research.cchmc.org/pbge/sincera.html）免费获取。
