Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data

BMC Bioinformatics

Volumn 20, Issue 1, 2019, Pages

  Wang, Tianyu ^a   Li, Boyang ^a   Nelson, Craig E ^a   Nabavi, Sheida ^a  

^a UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Comparative analysis; Differential gene expression analysis; RNAseq; Single cell

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; RNA;

ACCURACY AND PRECISION; BIOMEDICAL RESEARCH; COMPARATIVE ANALYSIS; COMPREHENSIVE EVALUATION; DIFFERENTIAL GENE EXPRESSIONS; RNASEQ; SINGLE CELLS; TRUE POSITIVE RATES;

GENE EXPRESSION;

GENE EXPRESSION PROFILING; HUMAN; PROCEDURES; SEQUENCE ANALYSIS;

GENE EXPRESSION PROFILING; HUMANS; SEQUENCE ANALYSIS, RNA;

EID: 85060160036     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-019-2599-6     Document Type: Article

References (48)

1. Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010;11:31-46.
2. Perry GH, Melsted P, Marioni JC, Wang Y, Bainer R, Pickrell JK, et al. Comparative RNA sequencing reveals substantial genetic variation in endangered primates. Genome Res. 2012;22:602-10.
3. Arnold CD, Gerlach D, Stelzer C, Boryn ŁM, Rath M, Stark A. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science. 2013;339:1074-7.
4. Han Y, Gao S, Muegge K, Zhang W, Zhou B. Advanced applications of RNA sequencing and challenges. Bioinforma Biol Insights. 2015;9(Suppl 1):29-46.
5. Schissler AG, Li Q, Chen JL, Kenost C, Achour I, Billheimer DD, et al. Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells. Bioinformatics. 2016;32:i80-9.
6. Saliba A-E, Westermann AJ, Gorski SA, Vogel J. Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res. 2014;42:8845-60.
7. Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet. 2015;16:133-45.
8. Bacher R, Kendziorski C. Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol. 2016;17. https://doi.org/10.1186/s13059-016-0927-y.
9. Grün D, van Oudenaarden A. Design and analysis of single-cell sequencing experiments. Cell. 2015;163:799-810.
10. Myers JS, von Lersner AK, Robbins CJ, Sang Q-XA. Differentially expressed genes and signature pathways of human prostate Cancer. PLoS One. 2015;10:e0145322.
11. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.
12. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139-40.
13. Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BMG, et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013;29:1035-43.
14. Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics. 2010;11:422.
15. Di Y, Schafer DW, Cumbie JS, Chang JH. The NBP negative binomial model for assessing differential gene expression from RNA-Seq. Stat Appl Genet Mol Biol. 2011;10. https://doi.org/10.2202/1544-6115.1637.
16. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3.
17. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
18. Li J, Tibshirani R. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res. 2013;22:519-36.
19. Tarazona S, García-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in RNA-seq: a matter of depth. Genome Res. 2011;21:2213-23.
20. Auer PL, Doerge RW. A two-stage Poisson model for testing RNA-Seq data. Stat Appl Genet Mol Biol. 2011;10. https://doi.org/10.2202/1544-6115.1627.
21. Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014;11:740-2.
22. Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a single cell. Science. 2002;297:1183-6.
23. Raj A, van Oudenaarden A. Stochastic gene expression and its consequences. Cell. 2008;135:216-26.
24. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344:1396-401.
25. Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM, et al. A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci U S A. 2015;112:7285-90.
26. Steinmeyer JD, Yanik MF. High-throughput single-cell manipulation in brain tissue. PLoS One. 2012;7:e35603.
27. Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D, Trombetta JJ, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352:189-96.
28. Nelson SB. Cortical microcircuits: diverse or canonical? Neuron. 2002;36:19-27.
29. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015;16. https://doi.org/10.1186/s13059-015-0844-5.
30. Korthauer KD, Chu L-F, Newton MA, Li Y, Thomson J, Stewart R, et al. scDD: a statistical approach for identifying differential distributions in single-cell RNA-seq experiments. bioRxiv. 2016:035501. https://doi.org/10.1101/035501.
31. Nabavi S, Schmolze D, Maitituoheti M, Malladi S, Beck AH. EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes. Bioinformatics. 2016;32:533-41.
32. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381-6.
33. Delmans M, Hemberg M. Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data. BMC Bioinformatics. 2016;17:110.
34. Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. SINCERA: a pipeline for single-cell RNA-Seq profiling analysis. PLoS Comput Biol. 2015;11:e1004575.
35. Katayama S, Töhönen V, Linnarsson S, Kere J. SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization. Bioinformatics. 2013;29:2943-5.
36. Miao Z, Deng K, Wang X, Zhang X. DEsingle for detecting three types of differential expression in single-cell RNA-seq data. Bioinformatics. 2018;34:3223-4.
37. Wang T, Nabavi S. SigEMD: a powerful method for differential gene expression analysis in single-cell RNA sequencing data. Methods San Diego Calif. 2018;145:25-32.
38. Qiu X, Hill A, Packer J, Lin D, Ma Y-A, Trapnell C. Single-cell mRNA quantification and differential analysis with census. Nat Methods 2017;advance online publication. doi: https://doi.org/10.1038/nmeth.4150.
39. Korthauer KD, Chu L-F, Newton MA, Li Y, Thomson J, Stewart R, et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol. 2016;17. https://doi.org/10.1186/s13059-016-1077-y.
40. Jaakkola MK, Seyednasrollah F, Mehmood A, Elo LL. Comparison of methods to detect differentially expressed genes between single-cell populations. Brief Bioinform. https://doi.org/10.1093/bib/bbw057.
41. Miao Z, Zhang X. Differential expression analyses for single-cell RNA-Seq: old questions on new data. Quant Biol. 2016;4:243-60.
42. Dal Molin A, Baruzzo G, Di Camillo B. Single-cell RNA-sequencing: assessment of differential expression analysis methods. Front Genet. 2017;8. https://doi.org/10.3389/fgene.2017.00062.
43. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
44. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289-300.
45. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2016. URL https://www.R-project.org /. https://www.R-project.org/
46. Islam S, Kjällquist U, Moliner A, Zajac P, Fan J-B, Lönnerberg P, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 2011;21:1160-7.
47. Moliner A, Enfors P, Ibáñez CF, Andäng M. Mouse embryonic stem cell-derived spheres with distinct neurogenic potentials. Stem Cells Dev. 2008;17:233-43.
48. Grün D, Kester L, van Oudenaarden A. Validation of noise models for single-cell transcriptomics. Nat Methods. 2014;11:637-40.