期刊
METHODS
卷 59, 期 1, 页码 80-88出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2012.09.007
关键词
RT-qPCR; Single-cell data analysis; Single-cell biology; Data pre-processing; Missing data; Gene expression profiling
资金
- Assar Gabrielssons Research Foundation
- Johan Jansson Foundation for Cancer Research
- Socialstyrelsen
- Swedish Society for Medical Research
- Swedish Research Council [521-2011-2367]
- EMBO Short Term Fellowship
- ESF Functional Genomics Short Visit Grant
- BioCARE National Strategic Research Program at University of Gothenburg
- Wilhelm and Martina Lundgren Foundation for Scientific Research
- Czech Ministry of Education [ME10052]
- GACR [GA P303/10/1338]
- [AV0Z50520701]
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. (C) 2012 Elsevier Inc. All rights reserved.
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