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RT-qPCR work-flow for single-cell data analysis.

Journal article
Authors Anders Ståhlberg
Vendula Rusnakova
Amin Forootan
Miroslava Anderova
Mikael Kubista
Published in Methods (San Diego, Calif.)
Volume 59
Issue 1
Pages 80-88
ISSN 1095-9130
Publication year 2013
Published at Institute of Biomedicine, Department of Pathology
Sahlgrenska Cancer Center
Pages 80-88
Language en
Links dx.doi.org/10.1016/j.ymeth.2012.09....
Keywords RT-qPCR, Single-cell data analysis, Single-cell biology, Data pre-processing, Missing data, Gene expression profiling
Subject categories Basic Medicine

Abstract

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.

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