-
1
-
-
84882455458
-
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
-
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
-
3
-
-
67349146589
-
mRNA-Seq whole-transcriptome analysis of a single cell
-
Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods Nature Publishing Group. 2009;6:377-82.
-
(2009)
Nat Methods Nature Publishing Group
, vol.6
, pp. 377-382
-
-
Tang, F.1
Barbacioru, C.2
Wang, Y.3
Nordman, E.4
Lee, C.5
Xu, N.6
-
4
-
-
79959403670
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
13
-
-
84929687805
-
The technology and biology of single-cell RNA sequencing
-
Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. The technology and biology of single-cell RNA sequencing. Mol Cell. 2015;58:610-20.
-
(2015)
Mol Cell
, vol.58
, pp. 610-620
-
-
Kolodziejczyk, A.A.1
Kim, J.K.2
Svensson, V.3
Marioni, J.C.4
Teichmann, S.A.5
-
14
-
-
84951574149
-
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
-
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
-
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
-
17
-
-
0034960264
-
Missing value estimation methods for DNA microarrays
-
Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17:520-5.
-
(2001)
Bioinformatics
, vol.17
, pp. 520-525
-
-
Troyanskaya, O.1
Cantor, M.2
Sherlock, G.3
Brown, P.4
Hastie, T.5
Tibshirani, R.6
-
19
-
-
33644856110
-
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
-
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
-
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
-
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
-
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
-
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
-
26
-
-
85016121177
-
SC3: consensus clustering of single-cell RNA-seq data
-
Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, et al. SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017;14(5):483-6.
-
(2017)
Nat Methods
, vol.14
, Issue.5
, pp. 483-486
-
-
Kiselev, V.Y.1
Kirschner, K.2
Schaub, M.T.3
Andrews, T.4
Yiu, A.5
Chandra, T.6
-
28
-
-
84900873950
-
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
-
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
-
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
-
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
-
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
-
33
-
-
85009446777
-
Massively parallel digital transcriptional profiling of single cells
-
Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. Nature Publishing Group. 2017;8:14049.
-
(2017)
Nat Commun. Nature Publishing Group
, vol.8
, pp. 14049
-
-
Zheng, G.X.Y.1
Terry, J.M.2
Belgrader, P.3
Ryvkin, P.4
Bent, Z.W.5
Wilson, R.6
-
34
-
-
85037674285
-
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
-
35
-
-
84921466417
-
Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
-
PMID: 25420068
-
Usoskin D, Furlan A, Islam S, Abdo H, Lönnerberg P, Lou D, Hjerling-Leffler J, Haeggström J, Kharchenko O, Kharchenko PV, Linnarsson S, Ernfors P. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat Neurosci. 2015;18(1):145-53. https://doi.org/10.1038/nn.3881. PMID: 25420068.
-
(2015)
Nat Neurosci.
, vol.18
, Issue.1
, pp. 145-153
-
-
Usoskin, D.1
Furlan, A.2
Islam, S.3
Abdo, H.4
Lönnerberg, P.5
Lou, D.6
Hjerling-Leffler, J.7
Haeggström, J.8
Kharchenko, O.9
Kharchenko, P.V.10
Linnarsson, S.11
Ernfors, P.12
-
36
-
-
84975473183
-
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
-
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
-
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
-
39
-
-
85040785722
-
A general and flexible method for signal extraction from single-cell RNA-seq data
-
Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun Nature Publishing Group. 2018;9:284.
-
(2018)
Nat Commun Nature Publishing Group
, vol.9
, pp. 284
-
-
Risso, D.1
Perraudeau, F.2
Gribkova, S.3
Dudoit, S.4
Vert, J.-P.5
-
40
-
-
85048301582
-
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
-
41
-
-
84984643819
-
Diffusion pseudotime robustly reconstructs lineage branching
-
Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods. 2016;13:845-8.
-
(2016)
Nat Methods
, vol.13
, pp. 845-848
-
-
Haghverdi, L.1
Büttner, M.2
Wolf, F.A.3
Buettner, F.4
Theis, F.J.5
-
42
-
-
84983497196
-
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
-
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
-
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
-
45
-
-
85027990252
-
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
-
Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. bioRxiv Cold Spring Harbor Laboratory; 2017;:128843.
-
(2017)
bioRxiv Cold Spring Harbor Laboratory
, pp. 128843
-
-
Street, K.1
Risso, D.2
Fletcher, R.B.3
Das, D.4
Ngai, J.5
Yosef, N.6
-
46
-
-
13444304426
-
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
-
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
-
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
-
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
|