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Assessing characteristics of RNA amplification methods for single cell RNA sequencing

Citation

Dueck, HR and Ai, R and Camarena, A and Ding, B and Dominguez, R and Evgrafov, OV and Fan, J-B and Fisher, SA and Herstein, JS and Kim, TK and Kim, JM and Lin, M-Y and Liu, R and Mack, WJ and McGroty, S and Nguyen, JD and Salathia, N and Shallcross, J and Souaiaia, T and Spaethling, JM and Walker, CP and Wang, CP and Wang, J and Wang, W and Wildberg, A and Zheng, L and Chow, RH and Eberwine, J and Knowles, JA and Zhang, K and Kim, J, Assessing characteristics of RNA amplification methods for single cell RNA sequencing, Bmc Genomics, 17 pp. 1-22. ISSN 1471-2164 (2016) [Refereed Article]


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Copyright Statement

The Author(s). 2016. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1186/s12864-016-3300-3

Abstract

Background: Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known.

Results: Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate measurements to be quantitative at an expression level greater than ~510 molecules.

Conclusions: Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

Item Details

Item Type:Refereed Article
Keywords:Single-cell, RNA sequencing, Bioinformatics
Research Division:Biological Sciences
Research Group:Genetics
Research Field:Genomics
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Biological Sciences
Author:Dominguez, R (Dr Reymundo Dominguez)
ID Code:122238
Year Published:2016
Web of Science® Times Cited:6
Deposited By:Medicine (Discipline)
Deposited On:2017-11-06
Last Modified:2017-11-17
Downloads:1 View Download Statistics

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