Differential expression analyses for single-cell RNA-Seq: old questions on new data

Quantitative Biology

Volumn 4, Issue 4, 2016, Pages 243-260

  Miao, Zhun ^a   Zhang, Xuegong ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

differential expression; RNA Seq; single cell

Indexed keywords

EID: 85042780617     PISSN: 20954689     EISSN: 20954697     Source Type: Journal    
DOI: 10.1007/s40484-016-0089-7     Document Type: Article

References (39)

1. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. and Wold, B. (2008) Mapping and quantifying mammalian transcriptomes by RNASeq. Nat. Methods, 5, 621–628
2. Stegle, O., Teichmann, S. A. and Marioni, J. C. (2015) Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet., 16, 133–145
3. Shapiro, E., Biezuner, T. and Linnarsson, S. (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet., 14, 618–630
4. Macaulay, I. C. and Voet, T. (2014) Single cell genomics: advances and future perspectives. PLoS Genet., 10, e1004126
5. Tang, F., Lao, K. and Surani, M. A. (2011) Development and applications of single-cell transcriptome analysis. Nat. Methods, 8, S6–S11
6. Kanter, I. and Kalisky, T. (2015) Single cell transcriptomics: methods and applications. Front. Oncol., 5, 53
7. Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C. and Teichmann, S. A. (2015) The technology and biology of single-cell RNA sequencing. Mol. Cell, 58, 610–620
8. Sandberg, R. (2014) Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods, 11, 22–24
9. Saliba, A. E., Westermann, A. J., Gorski, S. A. and Vogel, J. (2014) Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res., 42, 8845–8860
10. Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biol., 11, R106
11. Robinson, M. D., McCarthy, D. J. and Smyth, G. K. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140
12. Wang, L., Feng, Z., Wang, X., Wang, X. and Zhang, X. (2010) DEGseq: an R package for identifying differentially expressed genes from RNAseq data. Bioinformatics, 26, 136–138
13. Kharchenko, P. V., Silberstein, L. and Scadden, D. T. (2014) Bayesian approach to single-cell differential expression analysis. Nat. Methods, 11, 740–742
14. Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S. and Rinn, J. L. (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol., 32, 381–386
15. Delmans, M. and Hemberg, M. (2016) Discrete distributional differential expression (D3E)—a tool for gene expression analysis of single-cell RNA-seq data. BMC Bioinformatics, 17, 110
16. Vu, T. N., Wills, Q. F., Kalari, K. R., Niu, N., Wang, L., Rantalainen, M. and Pawitan, Y. (2016) Beta-Poisson model for single-cell RNA-seq data analyses. Bioinformatics, 32, 2128–2135
17. Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol., 16, 278
18. Wu, L., Zhang, X., Zhao, Z., Wang, L., Li, B., Li, G., Dean, M., Yu, Q., Wang, Y., Lin, X., et al. (2015) Full-length single-cell RNA-seq applied to a viral human cancer: applications to HPV expression and splicing analysis in HeLa S3 cells. Gigascience, 4, 51
19. Freeman, B. T., Jung, J. P. and Ogle, B. M. (2015) Single-cell RNA-seq of bone marrow-derived mesenchymal stem cells reveals unique profiles of lineage priming. PLoS One, 10, e0136199
20. Avraham, R., Haseley, N., Brown, D., Penaranda, C., Jijon, H. B., Trombetta, J. J., Satija, R., Shalek, A. K., Xavier, R. J., Regev, A., et al. (2015) Pathogen cell-to-cell variability drives heterogeneity in host immune responses. Cell, 162, 1309–1321
21. Blakeley, P., Fogarty, N. M. E., Valle, I. D., Wamaitha, S. E., Hu, T. X., Elder, K., Snell, P., Christie, L., Robson, P. and Niakan, K. K. (2015) Defining the three cell lineages of the human blastocyst by single-cell RNA-seq. Development, 142, 3613
22. Fan, X., Zhang, X., Wu, X., Guo, H., Hu, Y., Tang, F. and Huang, Y. (2015) Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol., 16, 148
23. Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci., 19, 335–346
24. Hardcastle, T. J. and Kelly, K. A. (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 11, 422
25. Di, Y., Schafer, D. W., Cumbie, J. S. and Chang, J. H. (2011) The NBP negative binomial model for assessing differential gene expression from RNA-Seq. Stat. Appl. Genet. Mol. Biol., 10, 1–28
26. Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L. and Pachter, L. (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol., 31, 46–53
27. Auer, P. L. and Doerge, R. W. (2011) A two-stage Poisson model for testing RNA-Seq data. Stat. Appl. Genet. Mol. Biol., 10, doi: 10.2202/1544-6115.1627
28. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W. and Smyth, G. K. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 43, e47
29. Frazee, A. C., Pertea, G., Jaffe, A. E., Langmead, B., Salzberg, S. L. and Leek, J. T. (2014) Flexible analysis of transcriptome assemblies with Ballgown. Biorxiv: http://dx.doi.org/10.1101/003665
30. Li, J. and Tibshirani, R. (2013) Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat. Methods Med. Res., 22, 519–536
31. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G. Durbin, R., and the 1000 Genome Project Data Processing Subgroup. (2009) The sequence alignment/map format and SAMtools. Bioinformatics, 25, 2078–2079
32. Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., et al. (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature, 510, 363–369
33. Brunskill, E. W., Park, J. S., Chung, E., Chen, F., Magella, B. and Potter, S. S. (2014) Single cell dissection of early kidney development: multilineage priming. Development, 141, 3093–3101
34. Kimmerling, R. J., Lee Szeto, G., Li, J. W., Genshaft, A. S., Kazer, S. W., Payer, K. R., de Riba Borrajo, J., Blainey, P. C., Irvine, D. J., Shalek, A. K., et al. (2016) A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat. Commun., 7, 10220
35. Su, Z., Labaj, P. P., Li, S., Thierry-Mieg, J., Thierry-Mieg, D., Shi, W., Wang, C., Schroth, G. P., Setterquist, R. A., and Thompson, J. F. (2014) A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol., 32, 903–914
36. Tan, P. K., Downey, T. J., Spitznagel, E. L., Xu, P., Fu, D., Dimitrov, D. S., Lempicki, R. A., Raaka, B. M. and Cam, M. C. (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res., 31, 5676–5684
37. Shi, L., Shi, L., Reid, L. H., Jones,W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., Kawasaki, E. S., et al. (2006) The MicroArray Quality Control (MAQC) project shows interand intraplatform reproducibility of gene expression measurements. Nat. Biotechnol., 24, 1151–1161
38. Trapnell, C., Pachter, L. and Salzberg, S. L. (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25, 1105–1111
39. Anders, S., Pyl, P. T., Huber, W (2015) HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 2015, 31, 166–169