A step-by-step workflow for low-level analysis of single-cell RNA-seq data .

F1000Research

Volumn 5, Issue , 2016, Pages

  Lun, Aaron T L ^a   McCarthy, Davis J ^b,c   Marioni, John C ^a,b,d  

^a CANCER RESEARCH UK CAMBRIDGE RESEARCH INSTITUTE   (United Kingdom)

^b EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^c ST VINCENT'S INSTITUTE OF MEDICAL RESEARCH   (Australia)

^d WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL CELL; ARTICLE; CELL CYCLE PHASE; COMPUTATIONAL FLUID DYNAMICS; CONTROLLED STUDY; EMBRYONIC STEM CELL; GENE CLUSTER; GENE EXPRESSION; HELPER CELL; HEMATOPOIETIC STEM CELL; HUMAN; HUMAN CELL; METHODOLOGY; MOUSE; NONHUMAN; RNA ANALYSIS; RNA SEQUENCE; SEQUENCE ANALYSIS; SOFTWARE; WORKFLOW;

EID: 85010931059     PISSN: None     EISSN: 20461402     Source Type: Journal    
DOI: 10.12688/F1000RESEARCH.9501.1     Document Type: Article

References (48)

1. Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol. 2010; 11(10): R106
2. Angerer P, Haghverdi L, Büttner M, et al.: destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics. 2016; 32(8): 1241-1243
3. Bertoli C, Skotheim JM, de Bruin RA: Control of cell cycle transcription during G1 and S phases. Nat Rev Mol Cell Biol. 2013; 14(8): 518-528
4. Bourgon R, Gentleman R, Huber W: Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci U S A. 2010; 107(21): 9546-9551
5. Bray NL, Pimentel H, Melsted P, et al.: Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016; 34(5): 525-527
6. Brennecke P, Anders S, Kim JK, et al.: Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods. 2013; 10(11): 1093-1095
7. 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): 155-160
8. Chen Y, Lun AT, Smyth GK: From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline [version 2; referees: 5 approved]. F1000Res. 2016; 5: 1438
9. Conboy CM, Spyrou C, Thorne NP, et al.: Cell cycle genes are the evolutionarily conserved targets of the E2F4 transcription factor. PLoS One. 2007; 2(10): e1061
10. Fan J, Salathia N, Liu R, et al.: Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat Methods. 2016; 13(3): 241-244
11. Hicks SC, Teng M, Irizarry RA: On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. bioRxiv. 2015
12. Huber W, Carey VJ, Gentleman R, et al.: Orchestrating highthroughput genomic analysis with Bioconductor. Nat Methods. 2015; 12(2): 115-121
13. Ilicic T, Kim JK, Kolodziejczyk AA, et al.: Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016; 17: 29
14. Islam S, Kjällquist U, Moliner A, et al.: Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 2011; 21(7): 1160-1167
15. Islam S, Zeisel A, Joost S, et al.: Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2014; 11(2): 163-166
16. Ji Z, Ji H: TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 2016; 44(13): e117
17. Juliá M, Telenti A, Rausell A: Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from singlecell RNA-seq. Bioinformatics. 2015; 31(20): 3380-3382
18. Kim JK, Kolodziejczyk AA, Ilicic T, et al.: Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat Commun. 2015; 6: 8687
19. Klein AM, Mazutis L, Akartuna I, et al.: Droplet barcoding for singlecell transcriptomics applied to embryonic stem cells. Cell. 2015; 161(5): 1187-1201
20. Kolodziejczyk AA, Kim JK, Tsang JC, et al.: Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation. Cell Stem Cell. 2015; 17(4): 471-485
21. Langfelder P, Zhang B, Horvath S: Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics. 2008; 24(5): 719-720
22. 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(2): R29
23. Leng N, Chu LF, Barry C, et al.: Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments. Nat Methods. 2015; 12(10): 947-950
24. Liao Y, Smyth GK, Shi W: The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013; 41(10): e108
25. Liao Y, Smyth GK, Shi W: featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014; 30(7): 923-930
26. Love MI, Anders S, Kim V, et al.: RNA-Seq workflow: gene-level exploratory analysis and differential expression [version 1; referees: 2 approved]. F1000Res. 2015; 4: 1070
27. 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
28. 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
29. Macosko EZ, Basu A, Satija R, et al.: Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5): 1202-1214
30. Mahata B, Zhang X, Kolodziejczyk AA, et al.: Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Rep. 2014; 7(4): 1130-1142
31. Marinov GK, Williams BA, McCue K, et al.: From single-cell to cellpool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014; 24(3): 496-510
32. McCarthy DJ, Chen Y, Smyth GK: Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012; 40(10): 4288-4297
33. Patro R, Duggal G, Kingsford C: Accurate, fast, and model-aware transcript expression quantification with Salmon. bioRxiv. 2015
34. Phipson B, Smyth GK: Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat Appl Genet Mol Biol. 2010; 9: Article39
35. Picelli S, Faridani OR, Björklund AK, et al.: Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc. 2014; 9(1): 171-181
36. Pollen AA, Nowakowski TJ, Shuga J, et al.: Low-coverage singlecell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014; 32(10): 1053-1058
37. Ritchie ME, Phipson B, Wu D, et al.: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43(7): e47
38. Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139-140
39. Robinson MD, Oshlack A: A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010; 11(3): R25
40. Scialdone A, Natarajan KN, Saraiva LR, et al.: Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods. 2015; 85: 54-61
41. Stegle O, Teichmann SA, Marioni JC: Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet. 2015; 16(3): 133-145
42. Trapnell C, Cacchiarelli D, Grimsby J, et al.: The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32(4): 381-386
43. Tung PY, Blischak JD, Hsiao C, et al.: Batch effects and the effective design of single-cell gene expression studies. bioRxiv. 2016
44. Vallejos CA, Marioni JC, Richardson S: BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput Biol. 2015; 11(6): e1004333
45. Van der Maaten L, Hinton G: Visualizing data using t-SNE. J Mach Learn Res. 2008; 9: 2579-2605
46. Wilson NK, Kent DG, Buettner F, et al.: Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell. 2015; 16(6): 712-724
47. Zeisel A, Muñoz-Manchado AB, Codeluppi S, et al.: Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015; 347(6226): 1138-1142
48. Zhu J, Yamane H, Cote-Sierra J, et al.: GATA-3 promotes Th2 responses through three different mechanisms: induction of Th2 cytokine production, selective growth of Th2 cells and inhibition of Th1 cell-specific factors. Cell Res. 2006; 16(1): 3-10