BEARscc determines robustness of single-cell clusters using simulated technical replicates

Nature Communications

Volumn 9, Issue 1, 2018, Pages

  Severson, D T ^a   Owen, R P ^a,b   White, M J ^a,b   Lu, X ^a   Schuster Bockler B ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b JOHN RADCLIFFE HOSPITAL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA; RNA;

ALGORITHM; CELL; CLUSTER ANALYSIS; EXPERIMENTAL STUDY; GENE EXPRESSION; RNA;

ARTICLE; BAYESIAN ERCC ASSESSMENT OF ROBUSTNESS OF SINGLE CELL CLUSTER; BAYESIAN LEARNING; BIOLOGICAL MONITORING; CLUSTER ANALYSIS; MATHEMATICAL COMPUTING; RNA SEQUENCE; SINGLE CELL ANALYSIS; WORKFLOW; ALGORITHM; ANIMAL; EVALUATION STUDY; GENE EXPRESSION PROFILING; GENETICS; HUMAN; MOUSE; PROCEDURES; SEQUENCE ANALYSIS;

ALGORITHMS; ANIMALS; GENE EXPRESSION PROFILING; HUMANS; MICE; RNA; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS;

EID: 85044318374     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-018-03608-y     Document Type: Article

References (20)

1. Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251-255 (2015).
2. Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145-1160 (2016).
3. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189-196 (2016).
4. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637-640 (2014).
5. Kim, J. K., Kolodziejczyk, A. A., Illicic, T., Teichmann, S. A. & Marioni, J. C. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun. 6, 8687-8688 (2015).
6. Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Preprint at: Https://doi.org/10.1093/biostatistics/kxx053 (2017).
7. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543-1551 (2011).
8. Vallejos, C. A., Marioni, J. C. & Richardson, S. BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput. Biol. 11, e1004333-18 (2015).
9. Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093-1095 (2013).
10. Grün, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Stem Cell 19, 266-277 (2016).
11. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138-1142 (2015).
12. Rousseeuw, P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53-65 (1987).
13. Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B Stat. Methodol. 63, 411-423 (2001).
14. Kiselev, V. Y. et al. SC3: Consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483-486 (2017).
15. Tintori, S. C., Osborne Nishimura, E., Golden, P., Lieb, J. D. & Goldstein, B. A transcriptional lineage of the early C. elegans embryo. Dev. Cell 38, 430-444 (2016).
16. Leinonen, R., Sugawara, H. & Shumway, M. International Nucleotide Sequence Database Collaboration. The sequence read archive. Nucleic Acids Res. 39, D19-D21 (2011).
17. Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013).
18. Gentleman, R. C. et al. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).
19. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171-181 (2014).
20. Hicks, S. C., Teng, M. & Irizarry, R. A. On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. Preprint at: Https://doi.org/10.1101/025528 (2015).