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

Fan Gao, Jae Mun Kim, Ji Hong Kim, Ming Yi Lin, Charles Y. Liu, Jonathan J. Russin, Christopher P. Walker, William Mack, Oleg V. Evgrafov, Robert H. Chow, James A. Knowles, Kai Wang

Research output: Contribution to journalEditorialpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
JournalInternational Journal of Computational Biology and Drug Design
Volume11
Issue number1-2
DOIs
StatePublished - 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Drug Discovery
  • Computer Science Applications

Keywords

  • ANNOVAR
  • Annotate variation
  • Bioinformatics
  • PCA
  • Principal component analysis
  • RNA-Seq
  • RNA-Seq by expectation maximisation
  • RSEM
  • Single-cell sequencing
  • T-SNE
  • T-distributed stochastic neighbour embedding
  • TopHat
  • Variance

Fingerprint

Dive into the research topics of 'Evaluation of biological and technical variations in low-input RNA-Seq and single-cell RNA-Seq'. Together they form a unique fingerprint.

Cite this