메뉴 건너뛰기




Volumn 19, Issue 1, 2018, Pages

DrImpute: Imputing dropout events in single cell RNA sequencing data

Author keywords

Dropout events; Imputation; Next generation sequencing; Single cell RNA sequencing data

Indexed keywords

CYTOLOGY; GENE EXPRESSION; STATISTICAL MECHANICS; STOCHASTIC SYSTEMS;

EID: 85048291476     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-018-2226-y     Document Type: Article
Times cited : (227)

References (50)
  • 1
    • 84882455458 scopus 로고    scopus 로고
    • Single-cell sequencing-based technologies will revolutionize whole-organism science
    • Shapiro E, Biezuner T, Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet. 2013;14:618-30.
    • (2013) Nat Rev Genet , vol.14 , pp. 618-630
    • Shapiro, E.1    Biezuner, T.2    Linnarsson, S.3
  • 2
    • 84923647450 scopus 로고    scopus 로고
    • Computational and analytical challenges in single-cell transcriptomics
    • Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet. 2015;16(3):133-45.
    • (2015) Nat Rev Genet. , vol.16 , Issue.3 , pp. 133-145
    • Stegle, O.1    Teichmann, S.A.2    Marioni, J.C.3
  • 4
    • 79959403670 scopus 로고    scopus 로고
    • Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
    • 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.
    • (2011) Genome Res , vol.21 , pp. 1160-1167
    • Islam, S.1    Kjällquist, U.2    Moliner, A.3    Zajac, P.4    Fan, J.-B.5    Lönnerberg, P.6
  • 5
    • 84994860357 scopus 로고    scopus 로고
    • Revealing the vectors of cellular identity with single-cell genomics
    • Wagner A, Regev A, Yosef N. Revealing the vectors of cellular identity with single-cell genomics. Nat Biotechnol. 2016;34:1145-60.
    • (2016) Nat Biotechnol , vol.34 , pp. 1145-1160
    • Wagner, A.1    Regev, A.2    Yosef, N.3
  • 6
    • 84892179132 scopus 로고    scopus 로고
    • Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells
    • Deng Q, Ramsköld D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science. 2014;343:193-6.
    • (2014) Science , vol.343 , pp. 193-196
    • Deng, Q.1    Ramsköld, D.2    Reinius, B.3    Sandberg, R.4
  • 7
    • 77951912210 scopus 로고    scopus 로고
    • Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst
    • Guo G, Huss M, Tong GQ, Wang C, Li Sun L, Clarke ND, et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev Cell. 2010;18:675-85.
    • (2010) Dev Cell , vol.18 , pp. 675-685
    • Guo, G.1    Huss, M.2    Tong, G.Q.3    Wang, C.4    Li Sun, L.5    Clarke, N.D.6
  • 8
    • 84922321862 scopus 로고    scopus 로고
    • Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex
    • Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014;32(10):1053-8.
    • (2014) Nat Biotechnol , vol.32 , Issue.10 , pp. 1053-1058
    • Pollen, A.A.1    Nowakowski, T.J.2    Shuga, J.3    Wang, X.4    Leyrat, A.A.5    Lui, J.H.6
  • 9
    • 85012069825 scopus 로고    scopus 로고
    • Dpath software reveals hierarchical haemato-endothelial lineages of Etv2 progenitors based on single-cell transcriptome analysis
    • Gong W, Rasmussen TL, N SB, Koyano-Nakagawa N, Pan W, Garry DJ. Dpath software reveals hierarchical haemato-endothelial lineages of Etv2 progenitors based on single-cell transcriptome analysis. Nat Commun Nature Publishing Group. 2017;8:14362.
    • (2017) Nat Commun Nature Publishing Group , vol.8 , pp. 14362
    • Gong, W.1    Rasmussen, T.L.2    Koyano-Nakagawa, N.3    Pan, W.4    Garry, D.J.5
  • 10
    • 84903574951 scopus 로고    scopus 로고
    • Bayesian approach to single-cell differential expression analysis
    • Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014;11(7):740-2.
    • (2014) Nat Methods , vol.11 , Issue.7 , pp. 740-742
    • Kharchenko, P.V.1    Silberstein, L.2    Scadden, D.T.3
  • 11
    • 84962658087 scopus 로고    scopus 로고
    • Design and computational analysis of single-cell RNA-sequencing experiments
    • Bacher R, Kendziorski C. Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol BioMed Central. 2016;17:63.
    • (2016) Genome Biol BioMed Central , vol.17 , pp. 63
    • Bacher, R.1    Kendziorski, C.2
  • 12
    • 84895562012 scopus 로고    scopus 로고
    • From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing
    • Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. 2014;24:496-510.
    • (2014) , vol.24 , pp. 496-510
    • Marinov, G.K.1    Williams, B.A.2    McCue, K.3    Schroth, G.P.4    Gertz, J.5    Myers, R.M.6
  • 14
    • 84951574149 scopus 로고    scopus 로고
    • MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome biol
    • 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. BioMed Central. 2015;16:278.
    • (2015) BioMed Central , vol.16 , pp. 278
    • Finak, G.1    McDavid, A.2    Yajima, M.3    Deng, J.4    Gersuk, V.5    Shalek, A.K.6
  • 15
    • 77955504378 scopus 로고    scopus 로고
    • Statistical design and analysis of RNA sequencing data
    • ed. 2010/05/05; 2010;.
    • Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics. 2010 ed. 2010/05/05; 2010;185:405-16.
    • (2010) Genetics , vol.185 , pp. 405-416
    • Auer, P.L.1    Doerge, R.W.2
  • 16
    • 2342426727 scopus 로고    scopus 로고
    • Gaussian mixture clustering and imputation of microarray data
    • Ouyang M, Welsh WJ, Georgopoulos P. Gaussian mixture clustering and imputation of microarray data. Bioinformatics. 2004;20:917-23.
    • (2004) Bioinformatics , vol.20 , pp. 917-923
    • Ouyang, M.1    Welsh, W.J.2    Georgopoulos, P.3
  • 19
    • 33644856110 scopus 로고    scopus 로고
    • Improving missing value estimation in microarray data with gene ontology
    • Tuikkala J, Elo L, Nevalainen OS, Aittokallio T. Improving missing value estimation in microarray data with gene ontology. Bioinformatics. 2006;22:566-72.
    • (2006) Bioinformatics , vol.22 , pp. 566-572
    • Tuikkala, J.1    Elo, L.2    Nevalainen, O.S.3    Aittokallio, T.4
  • 20
    • 85048263567 scopus 로고    scopus 로고
    • A unified statistical framework for RNA sequence data from individual cells and tissue
    • Zhu L, Lei J, Roeder K. A unified statistical framework for RNA sequence data from individual cells and tissue. In: arXiv; 2016.
    • (2016)
    • Zhu, L.1    Lei, J.2    Roeder, K.3
  • 21
    • 84992108405 scopus 로고    scopus 로고
    • Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data
    • Prabhakaran S, Azizi E, Pe'er D. Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data; 2016. p. 1070-9.
    • (2016) , pp. 1070-1079
    • Prabhakaran, S.1    Azizi, E.2    Pe'er, D.3
  • 23
    • 85045314028 scopus 로고    scopus 로고
    • An accurate and robust imputation method scImpute for single-cell RNA-seq data
    • Li WV, Li JJ. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun. Nature Publishing Group. 2018;9:997.
    • (2018) Nat Commun. Nature Publishing Group , vol.9 , pp. 997
    • Li, W.V.1    Li, J.J.2
  • 24
    • 85028022371 scopus 로고    scopus 로고
    • MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data
    • van Dijk D, Nainys J, Sharma R, Kathail P, Carr AJ, Moon KR, et al. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data.
    • van Dijk, D.1    Nainys, J.2    Sharma, R.3    Kathail, P.4    Carr, A.J.5    Moon, K.R.6
  • 25
    • 84977499231 scopus 로고    scopus 로고
    • pcaReduce: hierarchical clustering of single cell transcriptional profiles
    • Žurauskiene J, Yau C. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinformatics. 2016;17:140.
    • (2016) BMC Bioinformatics , vol.17 , pp. 140
    • Žurauskiene, J.1    Yau, C.2
  • 28
    • 84900873950 scopus 로고    scopus 로고
    • The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
    • 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.
    • (2014) Nat Biotechnol , vol.32 , pp. 381-386
    • Trapnell, C.1    Cacchiarelli, D.2    Grimsby, J.3    Pokharel, P.4    Li, S.5    Morse, M.6
  • 29
    • 84982806105 scopus 로고    scopus 로고
    • TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
    • Ji Z, Ji H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 2016;44(13):e117.
    • (2016) Nucleic Acids Res , vol.44 , Issue.13
    • Ji, Z.1    Ji, H.2
  • 30
    • 85016502564 scopus 로고    scopus 로고
    • CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
    • Lin P, Troup M, JWK H. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 2017;18:59.
    • (2017) Genome Biol , vol.18 , pp. 59
    • Lin, P.1    Troup, M.2    J.W.K, H.3
  • 31
    • 84955706109 scopus 로고    scopus 로고
    • ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
    • Pierson E, Yau C. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 2015;16:241.
    • (2015) Genome Biol , vol.16 , pp. 241
    • Pierson, E.1    Yau, C.2
  • 32
    • 84942163495 scopus 로고    scopus 로고
    • Defining the three cell lineages of the human blastocyst by single-cell RNA-seq
    • Blakeley P, Fogarty NME, Del Valle I, Wamaitha SE, Hu TX, Elder K, et al. Defining the three cell lineages of the human blastocyst by single-cell RNA-seq. Development. 2015;142(18):3151-65.
    • (2015) Development , vol.142 , Issue.18 , pp. 3151-3165
    • Blakeley, P.1    Fogarty, N.M.E.2    Del Valle, I.3    Wamaitha, S.E.4    Hu, T.X.5    Elder, K.6
  • 34
    • 85037674285 scopus 로고    scopus 로고
    • Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
    • Hrvatin S, Hochbaum DR, Nagy MA, Cicconet M, Robertson K, Cheadle L, et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat Neurosci. 2018;21:120-9.
    • (2018) Nat Neurosci , vol.21 , pp. 120-129
    • Hrvatin, S.1    Hochbaum, D.R.2    Nagy, M.A.3    Cicconet, M.4    Robertson, K.5    Cheadle, L.6
  • 36
    • 84975473183 scopus 로고    scopus 로고
    • Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq
    • Treutlein B, Lee QY, Camp JG, Mall M, Koh W, Shariati SAM, et al. Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq. Nature. 2016;534:391-5.
    • (2016) Nature , vol.534 , pp. 391-395
    • Treutlein, B.1    Lee, Q.Y.2    Camp, J.G.3    Mall, M.4    Koh, W.5    Shariati, S.A.M.6
  • 37
    • 84978761773 scopus 로고    scopus 로고
    • Resolving early mesoderm diversification through single-cell expression profiling
    • Scialdone A, Tanaka Y, Jawaid W, Moignard V, Wilson NK, Macaulay IC, et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature. 2016;535(7611):289-93.
    • (2016) Nature , vol.535 , Issue.7611 , pp. 289-293
    • Scialdone, A.1    Tanaka, Y.2    Jawaid, W.3    Moignard, V.4    Wilson, N.K.5    Macaulay, I.C.6
  • 38
    • 84962688754 scopus 로고    scopus 로고
    • Single-cell RNA-Seq reveals lineage and X chromosome dynamics in human Preimplantation embryos. Cell
    • Petropoulos S, Edsgärd D, Reinius B, Deng Q, Panula SP, Codeluppi S, et al. Single-cell RNA-Seq reveals lineage and X chromosome dynamics in human Preimplantation embryos. Cell. The Authors. 2016;165:1012-26.
    • (2016) The Authors , vol.165 , pp. 1012-1026
    • Petropoulos, S.1    Edsgärd, D.2    Reinius, B.3    Deng, Q.4    Panula, S.P.5    Codeluppi, S.6
  • 40
    • 85048301582 scopus 로고    scopus 로고
    • A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes
    • Lopez R, Regier J, Cole M, Jordan M, Yosef N. A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes. In: arXiv; 2017.
    • (2017)
    • Lopez, R.1    Regier, J.2    Cole, M.3    Jordan, M.4    Yosef, N.5
  • 42
    • 84983497196 scopus 로고    scopus 로고
    • Pseudotime estimation: deconfounding single cell time series
    • Reid JE, Wernisch L. Pseudotime estimation: deconfounding single cell time series. Bioinformatics. 2016;32:2973-80.
    • (2016) Bioinformatics , vol.32 , pp. 2973-2980
    • Reid, J.E.1    Wernisch, L.2
  • 43
    • 85020138228 scopus 로고    scopus 로고
    • LEAP: Constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering
    • Specht AT, Li J. LEAP: Constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering. Bioinformatics. 2017;33:764-6.
    • (2017) Bioinformatics , vol.33 , pp. 764-766
    • Specht, A.T.1    Li, J.2
  • 44
    • 85048305489 scopus 로고    scopus 로고
    • A descriptive marker gene-based approach to single-cell pseudotime trajectory learning. bioRxiv
    • Campbell K, Yau C. A descriptive marker gene-based approach to single-cell pseudotime trajectory learning. bioRxiv. Cold Spring Harbor Laboratory; 2017:060442.
    • (2017) Cold Spring Harbor Laboratory
    • Campbell, K.1    Yau, C.2
  • 46
    • 13444304426 scopus 로고    scopus 로고
    • Missing value estimation for DNA microarray gene expression data: local least squares imputation
    • Kim H, Golub GH, Park H. Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics. 2005;21:187-98.
    • (2005) Bioinformatics , vol.21 , pp. 187-198
    • Kim, H.1    Golub, G.H.2    Park, H.3
  • 47
    • 84924629414 scopus 로고    scopus 로고
    • Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
    • Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
    • (2014) Genome Biol , vol.15 , pp. 550
    • Love, M.I.1    Huber, W.2    Anders, S.3
  • 48
    • 85014528252 scopus 로고    scopus 로고
    • Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
    • Wang B, Zhu J, Pierson E, Ramazzotti D, Batzoglou S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods. 2017;14:414-6.
    • (2017) Nat Methods , vol.14 , pp. 414-416
    • Wang, B.1    Zhu, J.2    Pierson, E.3    Ramazzotti, D.4    Batzoglou, S.5
  • 50
    • 77954583359 scopus 로고    scopus 로고
    • Web-scale k-means clustering
    • New York: ACM Press.
    • Sculley D. Web-scale k-means clustering. WWW '10. New York. New York: ACM Press; 2010. p. 1177.
    • (2010) WWW '10. New York , pp. 1177
    • Sculley, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.