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Evaluation of biological and technical variations in low-input RNA-Seq and single-cell RNA-Seq


Gao, F and Kim, JM and Kim, J and Lin, M-Y and Liu, CY and Russin, JJ and Walker, CP and Dominguez, R and Camarena, A and Nguyen, JD and Herstein, J and Mack, W and Evgrafov, OV and Chow, RH and Knowles, JA and Wang, K, Evaluation of biological and technical variations in low-input RNA-Seq and single-cell RNA-Seq, International Journal of Computational Biology and Drug Design, 11, (1/2) ISSN 1756-0756 (2018) [Professional, Refereed Article]

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DOI: doi:10.1504/IJCBDD.2018.090839


Copyright © 2018 Inderscience Enterprises Ltd. Background: Low-input or single-cell RNA-Seq are widely used today, but two technical questions remain: 1) in technical replicates, what proportion of noises comes from input RNA quantity rather than variation of bioinformatics tools?; 2) In single neurons, whether variation in gene expression is attributable to biological heterogeneity or just random noise? To examine the sources of variability, we have generated RNA-Seq data from low-input (10/100/1000pg) reference RNA samples and 38 single neurons from human brains. Results: For technical replicates, the quantity of input RNA is negatively correlated with expression variation. For genes in the medium- and high-expression groups, input RNA amount explains most of the variation, whereas bioinformatic pipelines explain some variation for the low-expression group. The t-distributed stochastic neighbour embedding (t-SNE) method reveals data-inherent aggregation of low-input replicate data, and suggests heterogeneity of single pyramidal neuron transcriptome. Interestingly, expression variation in single neurons is biologically relevant. Conclusions: We found that differences in bioinformatics pipelines do not present a major source of variation.

Item Details

Item Type:Professional, Refereed Article
Keywords:RNA-Seq; single-cell sequencing; bioinformatics; TopHat; RNA-Seq by expectation maximisation; RSEM; t-distributed stochastic neighbour embedding; t-SNE; principal component analysis; PCA; annotate variation; ANNOVAR; variance
Research Division:Biological Sciences
Research Group:Genetics
Research Field:Gene expression (incl. microarray and other genome-wide approaches)
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biological sciences
UTAS Author:Dominguez, R (Dr Reymundo Dominguez)
ID Code:128008
Year Published:2018
Deposited By:Medicine
Deposited On:2018-08-28
Last Modified:2018-08-28

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