TATAA Biocenter, Gothenburg, Sweden; Sahlgrenska Cancer Center, Department of Pathology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
Methods. 2013 Jan;59(1):80-8. doi: 10.1016/j.ymeth.2012.09.007. Epub 2012 Sep 25.
Individual cells represent the basic unit in tissues and organisms and are in many aspects unique in their properties. The introduction of new and sensitive techniques to study single-cells opens up new avenues to understand fundamental biological processes. Well established statistical tools and recommendations exist for gene expression data based on traditional cell population measurements. However, these workflows are not suitable, and some steps are even inappropriate, to apply on single-cell data. Here, we present a simple and practical workflow for preprocessing of single-cell data generated by reverse transcription quantitative real-time PCR. The approach is demonstrated on a data set based on profiling of 41 genes in 303 single-cells. For some pre-processing steps we present options and also recommendations. In particular, we demonstrate and discuss different strategies for handling missing data and scaling data for downstream multivariate analysis. The aim of this workflow is provide guide to the rapidly growing community studying single-cells by means of reverse transcription quantitative real-time PCR profiling.
个体细胞是组织和生物体的基本单位，在许多方面具有独特的性质。引入新的和敏感的技术来研究单细胞为理解基本的生物学过程开辟了新的途径。基于传统的细胞群体测量，已经有了成熟的统计工具和建议用于基因表达数据。然而，这些工作流程并不适用于单细胞数据，有些步骤甚至不适用。在这里，我们提出了一种简单实用的工作流程，用于预处理由反转录定量实时 PCR 生成的单细胞数据。该方法基于对 303 个单细胞中 41 个基因的分析进行了演示。对于一些预处理步骤，我们提供了选项和建议。特别是，我们演示和讨论了处理缺失数据和对下游多元分析进行数据缩放的不同策略。该工作流程的目的是为通过反转录定量实时 PCR 分析研究单细胞的快速发展的研究社区提供指导。
