Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data

Briefings in Bioinformatics

Volumn 20, Issue 4, 2018, Pages 1583-1589

  Yip, Shun H ^a   Sham, Pak Chung ^b   Wang, Junwen ^c  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

^c ARIZONA STATE UNIVERSITY   (United States)

Author keywords

DEG analysis; highly variable gene; scRNA seq; single cell RNA seq; software

Indexed keywords

ARTICLE; CELL POPULATION; EMBRYONIC STEM CELL; GENE EXPRESSION; HUMAN CELL; INTERMETHOD COMPARISON; REPRODUCIBILITY; RNA SEQUENCE; SAMPLE SIZE; SOFTWARE; ANIMAL; BIOLOGY; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENETIC VARIATION; HUMAN; NUCLEIC ACID DATABASE; SINGLE CELL ANALYSIS;

ANIMALS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, NUCLEIC ACID; GENE EXPRESSION PROFILING; GENETIC VARIATION; HUMANS; REPRODUCIBILITY OF RESULTS; RNA-SEQ; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 85072958522     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bby011     Document Type: Article

References (30)

1. Islam S, Kjallquist U, Moliner A, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 2011;21(7):1160-7.
2. Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq wholetranscriptome analysis of a single cell. Nat Methods 2009;6(5): 377-82.
3. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;10(1):57-63.
4. Vallejos CA, Marioni JC, Richardson S. BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput Biol 2015; 11(6):e1004333.
5. Brennecke P, Anders S, Kim JK, et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods 2013; 10(11):1093-5.
6. Buettner F, Natarajan KN, Casale FP, et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol 2015;33(2):155-60.
7. Lun AT, McCarthy DJ, Marioni JC. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res 2016;5:2122.
8. Chen H-IH, Jin Y, Huang Y, et al. Detection of high variability in gene expression from single-cell RNA-seq profiling. BMC Genomics 2016;17(S7):508.
9. Satija R, Farrell JA, Gennert D, et al. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 2015;33(5):495-502.
10. Finak G, McDavid A, Yajima M, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 2015;16(1):278.
11. Yip SH, Wang P, Kocher J-PA, et al. Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Res 2017;45(22):e179.
12. Law CW, Chen Y, Shi W, et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 2014;15:R29.
13. Korthauer KD, Chu LF, Newton MA, et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol 2016;17(1):222.
14. Lun AT, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 2016;17:75.
15. Gierahn TM, Wadsworth MH, II, Hughes TK, et al. Seq-well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 2017;14(4):395-8.
16. Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015;161(5):1187-201.
17. Deng Q, Ramskold D, Reinius B, et al. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 2014;343(6167):193-6.
18. Yan L, Yang M, Guo H, et al. Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 2013;20(9):1131-9.
19. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30(15): 2114-20.
20. Bray NL, Pimentel H, Melsted P, et al. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016;34(8): 888-527.
21. Yates A, Akanni W, Amode MR, et al. Ensembl 2016. Nucleic Acids Res 2016;44(D1):D710-6.
22. Ganna A, Lee D, Ingelsson E, et al. Rediscovery rate estimation for assessing the validation of significant findings in highthroughput studies. Brief Bioinform 2015;16(4):563-75.
23. McDavid A, Finak G, Gottardo R. The contribution of cell cycle to heterogeneity in single-cell RNA-seq data. Nat Biotechnol 2016;34(6):591-3.
24. Leng N, Li Y, McIntosh BE, et al. EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments. Bioinformatics 2015;31(16): 2614-22.
25. Zhou X, Lindsay H, Robinson MD. Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res 2014;42(11):e91.
26. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15(12):550.
27. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26(1): 139-40.
28. Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 2010;11(1):422.
29. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010;11(10):R106.
30. Kiselev VY, Kirschner K, Schaub MT, et al. SC3: consensus clustering of single-cell RNA-seq data. Nat Methods 2017;14(5): 483-6.