-
1
-
-
67349146589
-
mRNA-Seq whole-transcriptome analysis of a single cell
-
COI: 1:CAS:528:DC%2BD1MXktVKgu78%3D, PID: 19349980
-
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
-
(2009)
Nat. Methods
, vol.6
, pp. 377-382
-
-
Tang, F.1
-
3
-
-
84929684999
-
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
-
COI: 1:CAS:528:DC%2BC2MXpt1Sgt7o%3D, PID: 26000488
-
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
-
(2015)
Cell
, vol.161
, pp. 1202-1214
-
-
Macosko, E.Z.1
-
4
-
-
85041394976
-
SCANPY: large-scale single-cell gene expression data analysis
-
PID: 29409532
-
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
-
(2018)
Genome Biol.
, vol.19
-
-
Wolf, F.A.1
Angerer, P.2
Theis, F.J.3
-
5
-
-
84949293695
-
SINCERA: a pipeline for single-cell RNA-Seq profiling analysis
-
PID: 26600239
-
Guo, M., Wang, H., Potter, S. S., Whitsett, J. A. & Xu, Y. SINCERA: a pipeline for single-cell RNA-Seq profiling analysis. PLOS Comput. Biol. 11, e1004575 (2015).
-
(2015)
PLOS Comput. Biol.
, vol.11
-
-
Guo, M.1
Wang, H.2
Potter, S.S.3
Whitsett, J.A.4
Xu, Y.5
-
6
-
-
84923647450
-
Computational and analytical challenges in single-cell transcriptomics
-
COI: 1:CAS:528:DC%2BC2MXhs1Shur4%3D, PID: 25628217
-
Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).
-
(2015)
Nat. Rev. Genet.
, vol.16
, pp. 133-145
-
-
Stegle, O.1
Teichmann, S.A.2
Marioni, J.C.3
-
7
-
-
85010931059
-
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. [version 2; referees: 3 approved, 2 approved with reservations]
-
PID: 27909575
-
Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. [version 2; referees: 3 approved, 2 approved with reservations]. F1000Res 5, 2122 (2016).
-
(2016)
F1000Res
, vol.5
, pp. 2122
-
-
Lun, A.T.L.1
McCarthy, D.J.2
Marioni, J.C.3
-
8
-
-
85027696020
-
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
-
PID: 28821273
-
Haque, A., Engel, J., Teichmann, S. A. & Lönnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 9, 75 (2017).
-
(2017)
Genome Med.
, vol.9
-
-
Haque, A.1
Engel, J.2
Teichmann, S.A.3
Lönnberg, T.4
-
9
-
-
85064576822
-
-
satijalab.org, References 4 and 9 are unsupervised clustering methods based on the Louvain method that have been shown to perform very well for large scRNA-seq data sets
-
Satija, R. SEURAT - R toolkit for single cell genomics: single cell integration in Seurat v3.0. satijalab.org https://satijalab.org/seurat/ (2015). References 4 and 9 are unsupervised clustering methods based on the Louvain method that have been shown to perform very well for large scRNA-seq data sets.
-
(2015)
SEURAT - R Toolkit for Single Cell Genomics: Single Cell Integration in Seurat V3.0
-
-
Satija, R.1
-
11
-
-
77950369345
-
Data clustering: 50 years beyond K-means
-
Jain, A. K. Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666 (2010).
-
(2010)
Pattern Recognit. Lett.
, vol.31
, pp. 651-666
-
-
Jain, A.K.1
-
13
-
-
85050565701
-
Molecular architecture of the mouse nervous system
-
Preprint at
-
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Preprint at bioRxiv 10.1101/294918 (2018).
-
(2018)
bioRxiv
-
-
Zeisel, A.1
-
14
-
-
85042366842
-
Mapping the mouse cell atlas by Microwell-Seq
-
COI: 1:CAS:528:DC%2BC1cXjt12ltr4%3D, PID: 29474909, References 12–14 are large collections of scRNA-seq data from mouse, and they give an indication of what a full atlas could look like
-
Han, X. et al. Mapping the mouse cell atlas by Microwell-Seq. Cell 172, 1091–1107 (2018). References 12–14 are large collections of scRNA-seq data from mouse, and they give an indication of what a full atlas could look like.
-
(2018)
Cell
, vol.172
, pp. 1091-1107
-
-
Han, X.1
-
15
-
-
85045640021
-
Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites
-
PID: 29580379
-
Reid, A. J. et al. Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites. eLife 7, e33105 (2018).
-
(2018)
eLife
, vol.7
-
-
Reid, A.J.1
-
16
-
-
85048501088
-
A single-cell transcriptome atlas of the aging Drosophila brain
-
COI: 1:CAS:528:DC%2BC1cXhtFCqtL3O, PID: 29909982
-
Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998 (2018).
-
(2018)
Cell
, vol.174
, pp. 982-998
-
-
Davie, K.1
-
17
-
-
85044334985
-
The cis-regulatory dynamics of embryonic development at single-cell resolution
-
COI: 1:CAS:528:DC%2BC1cXksFegu7g%3D, PID: 29539636
-
Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018).
-
(2018)
Nature
, vol.555
, pp. 538-542
-
-
Cusanovich, D.A.1
-
18
-
-
85032448109
-
The Human Cell Atlas: from vision to reality
-
COI: 1:CAS:528:DC%2BC2sXhslajtr7M, PID: 29072289
-
Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The Human Cell Atlas: from vision to reality. Nature 550, 451–453 (2017).
-
(2017)
Nature
, vol.550
, pp. 451-453
-
-
Rozenblatt-Rosen, O.1
Stubbington, M.J.T.2
Regev, A.3
Teichmann, S.A.4
-
20
-
-
84887109584
-
Accounting for technical noise in single-cell RNA-seq experiments
-
COI: 1:CAS:528:DC%2BC3sXhsVyqtb3L, PID: 24056876
-
Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).
-
(2013)
Nat. Methods
, vol.10
, pp. 1093-1095
-
-
Brennecke, P.1
-
21
-
-
0020102027
-
Least squares quantization in PCM
-
Lloyd, S. Least squares quantization in PCM. IEEE Trans. Inform. Theory 28, 129–137 (1982).
-
(1982)
IEEE Trans. Inform. Theory
, vol.28
, pp. 129-137
-
-
Lloyd, S.1
-
22
-
-
85016121177
-
SC3: consensus clustering of single-cell RNA-seq data
-
COI: 1:CAS:528:DC%2BC2sXltVWgtLY%3D, PID: 28346451, SC3 is a user-friendly clustering method that works very well for smaller data sets
-
Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017). SC3 is a user-friendly clustering method that works very well for smaller data sets.
-
(2017)
Nat. Methods
, vol.14
, pp. 483-486
-
-
Kiselev, V.Y.1
-
23
-
-
84941201582
-
Single-cell messenger RNA sequencing reveals rare intestinal cell types
-
PID: 26287467
-
Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).
-
(2015)
Nature
, vol.525
, pp. 251-255
-
-
Grün, D.1
-
24
-
-
85014528252
-
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
-
COI: 1:CAS:528:DC%2BC2sXltVWgt7c%3D, PID: 28263960
-
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 14, 414–416 (2017).
-
(2017)
Nat. Methods
, vol.14
, pp. 414-416
-
-
Wang, B.1
Zhu, J.2
Pierson, E.3
Ramazzotti, D.4
Batzoglou, S.5
-
25
-
-
85016502564
-
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
-
PID: 28351406
-
Lin, P., Troup, M. & Ho, J. W. K. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 18, 59 (2017).
-
(2017)
Genome Biol.
, vol.18
-
-
Lin, P.1
Troup, M.2
Ho, J.W.K.3
-
26
-
-
84924565530
-
Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
-
COI: 1:CAS:528:DC%2BC2MXjsF2hsro%3D, PID: 25700174
-
Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
-
(2015)
Science
, vol.347
, pp. 1138-1142
-
-
Zeisel, A.1
-
27
-
-
84977499231
-
pcaReduce: hierarchical clustering of single cell transcriptional profiles
-
PID: 27005807
-
Žurauskiene˙, J. & Yau, C. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinformatics 17, 140 (2016).
-
(2016)
BMC Bioinformatics
, vol.17
-
-
Žurauskiene˙, J.1
Yau, C.2
-
28
-
-
84961327715
-
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
-
COI: 1:CAS:528:DC%2BC28XitFWqsw%3D%3D, PID: 26727548
-
Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).
-
(2016)
Nat. Neurosci.
, vol.19
, pp. 335-346
-
-
Tasic, B.1
-
29
-
-
56349094785
-
Fast unfolding of communities in large networks
-
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, 10008 (2008).
-
(2008)
J. Stat. Mech.
, vol.2008
, pp. 10008
-
-
Blondel, V.D.1
Guillaume, J.-L.2
Lambiotte, R.3
Lefebvre, E.4
-
30
-
-
84863516584
-
Overlapping community detection in networks
-
Xie, J., Kelley, S. & Szymanski, B. K. Overlapping community detection in networks. ACM Comput. Surv. 45, 1–35 (2013).
-
(2013)
ACM Comput. Surv.
, vol.45
, pp. 1-35
-
-
Xie, J.1
Kelley, S.2
Szymanski, B.K.3
-
32
-
-
84934442835
-
Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis
-
COI: 1:CAS:528:DC%2BC2MXhtV2it7jE, PID: 26095251
-
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
-
(2015)
Cell
, vol.162
, pp. 184-197
-
-
Levine, J.H.1
-
33
-
-
85064579299
-
matchSCore: matching single-cell phenotypes across tools and experiments
-
Preprint at
-
Mereu, E. et al. matchSCore: matching single-cell phenotypes across tools and experiments. Preprint at bioRxiv 10.1101/314831 (2018).
-
(2018)
bioRxiv
-
-
Mereu, E.1
-
34
-
-
85056462361
-
Cluster headache: comparing clustering tools for 10X single cell sequencing data
-
Preprint at
-
Freytag, S., Lonnstedt, I., Ng, M. & Bahlo, M. Cluster headache: comparing clustering tools for 10X single cell sequencing data. Preprint at bioRxiv 10.1101/203752 (2017).
-
(2017)
bioRxiv
-
-
Freytag, S.1
Lonnstedt, I.2
Ng, M.3
Bahlo, M.4
-
35
-
-
85054667891
-
Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data
-
Menon, V. Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data. Brief. Funct. Genom. 17, 240–245 (2018).
-
(2018)
Brief. Funct. Genom.
, vol.17
, pp. 240-245
-
-
Menon, V.1
-
36
-
-
33846126275
-
Resolution limit in community detection
-
COI: 1:CAS:528:DC%2BD2sXjs1Wmug%3D%3D, PID: 17190818
-
Fortunato, S. & Barthélemy, M. Resolution limit in community detection. Proc. Natl Acad. Sci. USA 104, 36–41 (2007).
-
(2007)
Proc. Natl Acad. Sci. USA
, vol.104
, pp. 36-41
-
-
Fortunato, S.1
Barthélemy, M.2
-
39
-
-
85049105514
-
A comparison of single-cell trajectory inference methods: towards more accurate and robust tools
-
Preprint at
-
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools. Preprint at bioRxiv 10.1101/276907 (2018).
-
(2018)
bioRxiv
-
-
Saelens, W.1
Cannoodt, R.2
Todorov, H.3
Saeys, Y.4
-
40
-
-
84900873950
-
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
-
COI: 1:CAS:528:DC%2BC2cXks12ku7c%3D, PID: 24658644
-
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
-
(2014)
Nat. Biotechnol.
, vol.32
, pp. 381-386
-
-
Trapnell, C.1
-
41
-
-
84982806105
-
TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
-
PID: 27179027
-
Ji, Z. & Ji, H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117 (2016).
-
(2016)
Nucleic Acids Res.
, vol.44
-
-
Ji, Z.1
Ji, H.2
-
42
-
-
84892179132
-
Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells
-
COI: 1:CAS:528:DC%2BC2cXktVykug%3D%3D, PID: 24408435
-
Deng, Q., Ramsköld, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).
-
(2014)
Science
, vol.343
, pp. 193-196
-
-
Deng, Q.1
Ramsköld, D.2
Reinius, B.3
Sandberg, R.4
-
43
-
-
84873284300
-
Soft clustering – fuzzy and rough approaches and their extensions and derivatives
-
Peters, G., Crespo, F., Lingras, P. & Weber, R. Soft clustering – fuzzy and rough approaches and their extensions and derivatives. Int. J. Approx. Reason. 54, 307–322 (2013).
-
(2013)
Int. J. Approx. Reason.
, vol.54
, pp. 307-322
-
-
Peters, G.1
Crespo, F.2
Lingras, P.3
Weber, R.4
-
44
-
-
85047447859
-
Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
-
Preprint at
-
Wolf, F. A. et al. Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Preprint at bioRxiv 10.1101/208819 (2017).
-
(2017)
bioRxiv
-
-
Wolf, F.A.1
-
45
-
-
84977080410
-
Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development
-
COI: 1:CAS:528:DC%2BC28XhtFSqtLbP, PID: 27356503
-
Chen, J., Schlitzer, A., Chakarov, S., Ginhoux, F. & Poidinger, M. Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development. Nat. Commun. 7, 11988 (2016).
-
(2016)
Nat. Commun.
, vol.7
-
-
Chen, J.1
Schlitzer, A.2
Chakarov, S.3
Ginhoux, F.4
Poidinger, M.5
-
47
-
-
85048881841
-
Recovering gene interactions from single-cell data using data diffusion
-
PID: 29961576
-
van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).
-
(2018)
Cell
, vol.174
, pp. 716-729
-
-
van Dijk, D.1
-
48
-
-
85045314028
-
An accurate and robust imputation method scImpute for single-cell RNA-seq data
-
PID: 29520097
-
Li, W. V. & Li, J. J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat. Commun. 9, 997 (2018).
-
(2018)
Nat. Commun.
, vol.9
-
-
Li, W.V.1
Li, J.J.2
-
49
-
-
80052521697
-
Synthetic spike-in standards for RNA-seq experiments
-
COI: 1:CAS:528:DC%2BC3MXhtFGisLnM, PID: 21816910
-
Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).
-
(2011)
Genome Res.
, vol.21
, pp. 1543-1551
-
-
Jiang, L.1
-
50
-
-
84901831004
-
Validation of noise models for single-cell transcriptomics
-
PID: 24747814
-
Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).
-
(2014)
Nat. Methods
, vol.11
, pp. 637-640
-
-
Grün, D.1
Kester, L.2
van Oudenaarden, A.3
-
51
-
-
84959189722
-
Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
-
COI: 1:CAS:528:DC%2BC28Xps1Gjtw%3D%3D, PID: 26780092
-
Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).
-
(2016)
Nat. Methods
, vol.13
, pp. 241-244
-
-
Fan, J.1
-
52
-
-
84903574951
-
Bayesian approach to single-cell differential expression analysis
-
COI: 1:CAS:528:DC%2BC2cXotFCjs70%3D, PID: 24836921
-
Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).
-
(2014)
Nat. Methods
, vol.11
, pp. 740-742
-
-
Kharchenko, P.V.1
Silberstein, L.2
Scadden, D.T.3
-
53
-
-
85021816036
-
Normalizing single-cell RNA sequencing data: challenges and opportunities
-
COI: 1:CAS:528:DC%2BC2sXnslKkt7o%3D, PID: 28504683
-
Vallejos, C. A., Risso, D., Scialdone, A., Dudoit, S. & Marioni, J. C. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14, 565–571 (2017).
-
(2017)
Nat. Methods
, vol.14
, pp. 565-571
-
-
Vallejos, C.A.1
Risso, D.2
Scialdone, A.3
Dudoit, S.4
Marioni, J.C.5
-
54
-
-
85044318374
-
BEARscc determines robustness of single-cell clusters using simulated technical replicates
-
COI: 1:STN:280:DC%2BC1MnjvF2jtw%3D%3D, PID: 29567991
-
Severson, D. T., Owen, R. P., White, M. J., Lu, X. & Schuster-Böckler, B. BEARscc determines robustness of single-cell clusters using simulated technical replicates. Nat. Commun. 9, 1187 (2018).
-
(2018)
Nat. Commun.
, vol.9
-
-
Severson, D.T.1
Owen, R.P.2
White, M.J.3
Lu, X.4
Schuster-Böckler, B.5
-
55
-
-
85046700621
-
-
Preprint at bioRxiv
-
Buttner, M., Miao, Z., Wolf, A., Teichmann, S. A. & Theis, F. J. Assessment of batch-correction methods for scRNA-seq data with a new test metric. Preprint at bioRxiv https://doi.org/10.1101/200345 (2017).
-
(2017)
Assessment of Batch-Correction Methods for Scrna-Seq Data with a New Test Metric
-
-
Buttner, M.1
Miao, Z.2
Wolf, A.3
Teichmann, S.A.4
Theis, F.J.5
-
56
-
-
84977929803
-
A reanalysis of mouse ENCODE comparative gene expression data. [version 1; referees: 3 approved, 1 approved with reservations]
-
PID: 26236466
-
Gilad, Y. & Mizrahi-Man, O. A reanalysis of mouse ENCODE comparative gene expression data. [version 1; referees: 3 approved, 1 approved with reservations]. F1000Res 4, 121 (2015).
-
(2015)
F1000Res
, vol.4
, pp. 121
-
-
Gilad, Y.1
Mizrahi-Man, O.2
-
57
-
-
85008384488
-
Batch effects and the effective design of single-cell gene expression studies
-
COI: 1:CAS:528:DC%2BC2sXkslChtg%3D%3D, PID: 28045081
-
Tung, P.-Y. et al. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7, 39921 (2017).
-
(2017)
Sci. Rep.
, vol.7
-
-
Tung, P.-Y.1
-
58
-
-
85046289733
-
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
-
COI: 1:CAS:528:DC%2BC1cXmslKrtLo%3D, PID: 29608177
-
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
-
(2018)
Nat. Biotechnol.
, vol.36
, pp. 421-427
-
-
Haghverdi, L.1
Lun, A.T.L.2
Morgan, M.D.3
Marioni, J.C.4
-
59
-
-
85046298440
-
Integrating single-cell transcriptomic data across different conditions, technologies, and species
-
COI: 1:CAS:528:DC%2BC1cXmslKrtL0%3D, PID: 29608179, References 58 and 59 present the first two methods for correcting batch effects to merge samples
-
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018). References 58 and 59 present the first two methods for correcting batch effects to merge samples.
-
(2018)
Nat. Biotechnol.
, vol.36
, pp. 411-420
-
-
Butler, A.1
Hoffman, P.2
Smibert, P.3
Papalexi, E.4
Satija, R.5
-
60
-
-
85043529587
-
Experimental design for single-cell RNA sequencing
-
Baran-Gale, J., Chandra, T. & Kirschner, K. Experimental design for single-cell RNA sequencing. Brief. Funct. Genom. 17, 233–239 (2018).
-
(2018)
Brief. Funct. Genom.
, vol.17
, pp. 233-239
-
-
Baran-Gale, J.1
Chandra, T.2
Kirschner, K.3
-
61
-
-
84903729955
-
RNA-seq: impact of RNA degradation on transcript quantification
-
PID: 24885439
-
Gallego Romero, I., Pai, A. A., Tung, J. & Gilad, Y. RNA-seq: impact of RNA degradation on transcript quantification. BMC Biol. 12, 42 (2014).
-
(2014)
BMC Biol.
, vol.12
-
-
Gallego Romero, I.1
Pai, A.A.2
Tung, J.3
Gilad, Y.4
-
62
-
-
85042028817
-
The effects of death and post-mortem cold ischemia on human tissue transcriptomes
-
PID: 29440659
-
Ferreira, P. G. et al. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nat. Commun. 9, 490 (2018).
-
(2018)
Nat. Commun.
, vol.9
-
-
Ferreira, P.G.1
-
63
-
-
85031794820
-
Detecting activated cell populations using single-cell RNA-seq
-
COI: 1:CAS:528:DC%2BC2sXhs1GlsLbI, PID: 29024657
-
Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-seq. Neuron 96, 313–329 (2017).
-
(2017)
Neuron
, vol.96
, pp. 313-329
-
-
Wu, Y.E.1
Pan, L.2
Zuo, Y.3
Li, X.4
Hong, W.5
-
64
-
-
85048936988
-
dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments
-
PID: 29921301
-
Petukhov, V. et al. dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments. Genome Biol. 19, 78 (2018).
-
(2018)
Genome Biol.
, vol.19
-
-
Petukhov, V.1
-
65
-
-
84958058589
-
Classification of low quality cells from single-cell RNA-seq data
-
PID: 26887813
-
Ilicic, T. et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016).
-
(2016)
Genome Biol.
, vol.17
-
-
Ilicic, T.1
-
66
-
-
85052908223
-
DoubletDecon: cell-state aware removal of single-cell RNA-seq doublets
-
Preprint at
-
DePasquale, E. A. K. et al. DoubletDecon: cell-state aware removal of single-cell RNA-seq doublets. Preprint at bioRxiv 10.1101/364810 (2018).
-
(2018)
bioRxiv
-
-
DePasquale, E.A.K.1
-
67
-
-
85052881156
-
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
-
Preprint at
-
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Preprint at bioRxiv 10.1101/357368 (2018).
-
(2018)
bioRxiv
-
-
Wolock, S.L.1
Lopez, R.2
Klein, A.M.3
-
68
-
-
85064579132
-
DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors
-
Preprint at
-
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Preprint at bioRxiv 10.1101/352484 (2018).
-
(2018)
bioRxiv
-
-
McGinnis, C.S.1
Murrow, L.M.2
Gartner, Z.J.3
-
69
-
-
85059492831
-
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. [version 1; referees: 1 approved, 2 approved with reservations]
-
PID: 30228881
-
Freytag, S., Tian, L., Lönnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. [version 1; referees: 1 approved, 2 approved with reservations]. F1000Res 7, 1297 (2018).
-
(2018)
F1000Res
, vol.7
, pp. 1297
-
-
Freytag, S.1
Tian, L.2
Lönnstedt, I.3
Ng, M.4
Bahlo, M.5
-
70
-
-
84923292191
-
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
-
COI: 1:CAS:528:DC%2BC2MXhtFKjsLs%3D, PID: 25599176
-
Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).
-
(2015)
Nat. Biotechnol.
, vol.33
, pp. 155-160
-
-
Buettner, F.1
-
71
-
-
84939772971
-
Computational assignment of cell-cycle stage from single-cell transcriptome data
-
COI: 1:CAS:528:DC%2BC2MXhtF2kurjF, PID: 26142758
-
Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015).
-
(2015)
Methods
, vol.85
, pp. 54-61
-
-
Scialdone, A.1
-
72
-
-
85016091925
-
Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma
-
PID: 27806376
-
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
-
(2016)
Nature
, vol.539
, pp. 309-313
-
-
Tirosh, I.1
-
73
-
-
85048731044
-
Performance assessment and selection of normalization procedures for single-cell RNA-seq
-
Preprint at
-
Cole, M. B. et al. Performance assessment and selection of normalization procedures for single-cell RNA-seq. Preprint at bioRxiv 10.1101/235382 (2017).
-
(2017)
bioRxiv
-
-
Cole, M.B.1
-
74
-
-
85009446777
-
Massively parallel digital transcriptional profiling of single cells
-
COI: 1:CAS:528:DC%2BC2sXht1WlsLo%3D, PID: 28091601
-
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
-
(2017)
Nat. Commun.
, vol.8
-
-
Zheng, G.X.Y.1
-
75
-
-
84976875133
-
GiniClust: detecting rare cell types from single-cell gene expression data with Gini index
-
PID: 27368803
-
Jiang, L., Chen, H., Pinello, L. & Yuan, G.-C. GiniClust: detecting rare cell types from single-cell gene expression data with Gini index. Genome Biol. 17, 144 (2016).
-
(2016)
Genome Biol.
, vol.17
-
-
Jiang, L.1
Chen, H.2
Pinello, L.3
Yuan, G.-C.4
-
76
-
-
85018582872
-
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
-
PID: 28428369, This study is a good example of how scRNA-seq was used to identify new cell types, which were subsequently confirmed by functional assays
-
Villani, A.-C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017). This study is a good example of how scRNA-seq was used to identify new cell types, which were subsequently confirmed by functional assays.
-
(2017)
Science
, vol.356
, pp. eaah4573
-
-
Villani, A.-C.1
-
77
-
-
85011634407
-
A molecular census of arcuate hypothalamus and median eminence cell types
-
COI: 1:CAS:528:DC%2BC2sXisV2kt7o%3D, PID: 28166221
-
Campbell, J. N. et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484–496 (2017).
-
(2017)
Nat. Neurosci.
, vol.20
, pp. 484-496
-
-
Campbell, J.N.1
-
80
-
-
84931072284
-
Identification of cell types from single-cell transcriptomes using a novel clustering method
-
COI: 1:CAS:528:DC%2BC28Xht1egu7fN, PID: 25805722
-
Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015).
-
(2015)
Bioinformatics
, vol.31
, pp. 1974-1980
-
-
Xu, C.1
Su, Z.2
-
81
-
-
84922321862
-
Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex
-
COI: 1:CAS:528:DC%2BC2cXht1Gqt7zJ, PID: 25086649, This study shows that shallow sequencing can be sufficient to distinguish cell types
-
Pollen, A. A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014). This study shows that shallow sequencing can be sufficient to distinguish cell types.
-
(2014)
Nat. Biotechnol.
, vol.32
, pp. 1053-1058
-
-
Pollen, A.A.1
-
82
-
-
84947748539
-
Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation
-
COI: 1:CAS:528:DC%2BC2MXhsFaqsbnO, PID: 26431182
-
Kolodziejczyk, A. A. et al. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17, 471–485 (2015).
-
(2015)
Cell Stem Cell
, vol.17
, pp. 471-485
-
-
Kolodziejczyk, A.A.1
-
83
-
-
84937703271
-
Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos
-
PID: 26201400
-
Fan, X. et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol. 16, 148 (2015).
-
(2015)
Genome Biol.
, vol.16
-
-
Fan, X.1
-
84
-
-
84992437479
-
In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus
-
COI: 1:CAS:528:DC%2BC28XhslSqtr%2FM, PID: 27764670
-
Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).
-
(2016)
Neuron
, vol.92
, pp. 342-357
-
-
Shah, S.1
Lubeck, E.2
Zhou, W.3
Cai, L.4
-
85
-
-
84555189571
-
RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues
-
COI: 1:CAS:528:DC%2BC38XhvVCrtL0%3D, PID: 22166544
-
Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).
-
(2012)
J. Mol. Diagn.
, vol.14
, pp. 22-29
-
-
Wang, F.1
-
86
-
-
84928395184
-
RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells
-
PID: 25858977
-
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
-
(2015)
Science
, vol.348
, pp. aaa6090
-
-
Chen, K.H.1
Boettiger, A.N.2
Moffitt, J.R.3
Wang, S.4
Zhuang, X.5
-
87
-
-
84994641696
-
A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure
-
COI: 1:CAS:528:DC%2BC2sXhtFalsrk%3D, PID: 27667365
-
Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).
-
(2016)
Cell Syst.
, vol.3
, pp. 346-360
-
-
Baron, M.1
-
88
-
-
84994589771
-
A single-cell transcriptome atlas of the human pancreas
-
COI: 1:CAS:528:DC%2BC2sXhtFamu74%3D, PID: 27693023
-
Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016).
-
(2016)
Cell Syst.
, vol.3
, pp. 385-394
-
-
Muraro, M.J.1
-
89
-
-
84989205113
-
Single-cell transcriptomics of the human endocrine pancreas
-
COI: 1:CAS:528:DC%2BC28XhvFOrtL7M, PID: 5033269
-
Wang, Y. J. et al. Single-cell transcriptomics of the human endocrine pancreas. Diabetes 65, 3028–3038 (2016).
-
(2016)
Diabetes
, vol.65
, pp. 3028-3038
-
-
Wang, Y.J.1
-
90
-
-
84992364302
-
Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes
-
COI: 1:CAS:528:DC%2BC28XhsFGru7zN, PID: 27667667
-
Segerstolpe, Å. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).
-
(2016)
Cell Metab.
, vol.24
, pp. 593-607
-
-
Segerstolpe, Å.1
-
91
-
-
84992427889
-
RNA sequencing of single human islet cells reveals type 2 diabetes genes
-
COI: 1:CAS:528:DC%2BC28XhsFGru7%2FN, PID: 27667665
-
Xin, Y. et al. RNA sequencing of single human islet cells reveals type 2 diabetes genes. Cell Metab. 24, 608–615 (2016).
-
(2016)
Cell Metab.
, vol.24
, pp. 608-615
-
-
Xin, Y.1
-
92
-
-
85046289245
-
scmap: projection of single-cell RNA-seq data across data sets
-
COI: 1:CAS:528:DC%2BC1cXmslKrurw%3D, PID: 29608555
-
Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).
-
(2018)
Nat. Methods
, vol.15
, pp. 359-362
-
-
Kiselev, V.Y.1
Yiu, A.2
Hemberg, M.3
-
93
-
-
85042801480
-
Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor
-
PID: 29491377, References 92 and 93 present methods for comparing clusters across data sets without merging
-
Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018). References 92 and 93 present methods for comparing clusters across data sets without merging.
-
(2018)
Nat. Commun.
, vol.9
-
-
Crow, M.1
Paul, A.2
Ballouz, S.3
Huang, Z.J.4
Gillis, J.5
-
94
-
-
0034069495
-
Gene ontology: tool for the unification of biology
-
COI: 1:CAS:528:DC%2BD3cXjtFSlsbc%3D, PID: 3037419
-
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
-
(2000)
Nat. Genet.
, vol.25
, pp. 25-29
-
-
Ashburner, M.1
-
95
-
-
85064579405
-
CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing
-
Preprint at
-
Sato, K., Tsuyuzaki, K., Shimizu, K. & Nikaido, I. CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing. Preprint at bioRxiv 10.1101/374462 (2018).
-
(2018)
bioRxiv
-
-
Sato, K.1
Tsuyuzaki, K.2
Shimizu, K.3
Nikaido, I.4
-
96
-
-
85050864655
-
CellAtlasSearch: a scalable search engine for single cells
-
PID: 29788498
-
Srivastava, D., Iyer, A., Kumar, V. & Sengupta, D. CellAtlasSearch: a scalable search engine for single cells. Nucleic Acids Res. 46, W141–W147 (2018).
-
(2018)
Nucleic Acids Res.
, vol.46
, pp. W141-W147
-
-
Srivastava, D.1
Iyer, A.2
Kumar, V.3
Sengupta, D.4
-
97
-
-
78650789238
-
Logical development of the cell ontology
-
PID: 21208450
-
Meehan, T. F. et al. Logical development of the cell ontology. BMC Bioinformatics 12, 6 (2011).
-
(2011)
BMC Bioinformatics
, vol.12
-
-
Meehan, T.F.1
-
98
-
-
85048609035
-
Cell type discovery using single-cell transcriptomics: implications for ontological representation
-
COI: 1:CAS:528:DC%2BC1cXitlGmtb7E, PID: 29590361
-
Aevermann, B. D. et al. Cell type discovery using single-cell transcriptomics: implications for ontological representation. Hum. Mol. Genet. 27, R40–R47 (2018).
-
(2018)
Hum. Mol. Genet.
, vol.27
, pp. R40-R47
-
-
Aevermann, B.D.1
-
99
-
-
85038899097
-
Cell type discovery and representation in the era of high-content single cell phenotyping
-
PID: 29322913
-
Bakken, T. et al. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics 18, 559 (2017).
-
(2017)
BMC Bioinformatics
, vol.18
-
-
Bakken, T.1
-
100
-
-
85048568809
-
A single-cell atlas of cell types, states, and other transcriptional patterns from nine regions of the adult mouse brain
-
Preprint at
-
Saunders, A. et al. A single-cell atlas of cell types, states, and other transcriptional patterns from nine regions of the adult mouse brain. Preprint at bioRxiv 10.1101/299081 (2018).
-
(2018)
bioRxiv
-
-
Saunders, A.1
-
101
-
-
85032583384
-
SCENIC: single-cell regulatory network inference and clustering
-
COI: 1:CAS:528:DC%2BC2sXhs1aitL7P, PID: 28991892
-
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
-
(2017)
Nat. Methods
, vol.14
, pp. 1083-1086
-
-
Aibar, S.1
-
102
-
-
84942940566
-
Defining cell types and states with single-cell genomics
-
COI: 1:CAS:528:DC%2BC2MXhs1Oitb%2FK, PID: 26430159
-
Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).
-
(2015)
Genome Res.
, vol.25
, pp. 1491-1498
-
-
Trapnell, C.1
-
103
-
-
85051624261
-
A revised airway epithelial hierarchy includes CFTR-expressing ionocytes
-
COI: 1:CAS:528:DC%2BC1cXhsVensrjJ, PID: 30069044
-
Montoro, D. T. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018).
-
(2018)
Nature
, vol.560
, pp. 319-324
-
-
Montoro, D.T.1
-
104
-
-
85051647983
-
A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte
-
COI: 1:CAS:528:DC%2BC1cXhsVensrnM, PID: 30069046
-
Plasschaert, L. W. et al. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature 560, 377–381 (2018).
-
(2018)
Nature
, vol.560
, pp. 377-381
-
-
Plasschaert, L.W.1
-
105
-
-
85034642667
-
Construction of developmental lineage relationships in the mouse mammary gland by single-cell RNA profiling
-
PID: 29158510
-
Pal, B. et al. Construction of developmental lineage relationships in the mouse mammary gland by single-cell RNA profiling. Nat. Commun. 8, 1627 (2017).
-
(2017)
Nat. Commun.
, vol.8
-
-
Pal, B.1
-
106
-
-
85046653336
-
Single cell multi-omics technology: methodology and application
-
PID: 29732369
-
Hu, Y. et al. Single cell multi-omics technology: methodology and application. Front. Cell Dev. Biol. 6, 28 (2018).
-
(2018)
Front. Cell Dev. Biol.
, vol.6
, pp. 28
-
-
Hu, Y.1
-
107
-
-
84969504939
-
Multi-omics of single cells: strategies and applications
-
COI: 1:CAS:528:DC%2BC28XmslSgurc%3D, PID: 27212022
-
Bock, C., Farlik, M. & Sheffield, N. C. Multi-omics of single cells: strategies and applications. Trends Biotechnol. 34, 605–608 (2016).
-
(2016)
Trends Biotechnol.
, vol.34
, pp. 605-608
-
-
Bock, C.1
Farlik, M.2
Sheffield, N.C.3
-
108
-
-
85009781625
-
Single-cell multiomics: multiple measurements from single cells
-
COI: 1:CAS:528:DC%2BC2sXlt12ktQ%3D%3D, PID: 28089370
-
Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33, 155–168 (2017).
-
(2017)
Trends Genet.
, vol.33
, pp. 155-168
-
-
Macaulay, I.C.1
Ponting, C.P.2
Voet, T.3
-
109
-
-
84872522528
-
Latent enhancers activated by stimulation in differentiated cells
-
COI: 1:CAS:528:DC%2BC3sXht1ygtbo%3D, PID: 23332752
-
Ostuni, R. et al. Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171 (2013).
-
(2013)
Cell
, vol.152
, pp. 157-171
-
-
Ostuni, R.1
-
110
-
-
85047617654
-
Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing
-
COI: 1:CAS:528:DC%2BC1cXhtVaisLnO, PID: 29802404
-
Gao, S. et al. Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing. Nat. Cell Biol. 20, 721–734 (2018).
-
(2018)
Nat. Cell Biol.
, vol.20
, pp. 721-734
-
-
Gao, S.1
-
111
-
-
85045437597
-
Identification of spatial expression trends in single-cell gene expression data
-
PID: 29553578
-
Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).
-
(2018)
Nat. Methods
, vol.15
, pp. 339-342
-
-
Edsgärd, D.1
Johnsson, P.2
Sandberg, R.3
-
112
-
-
85057943596
-
Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma
-
Preprint at
-
Moncada, R. et al. Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma. Preprint at bioRxiv 10.1101/254375 (2018).
-
(2018)
bioRxiv
-
-
Moncada, R.1
-
113
-
-
85044604944
-
Comprehensive identification and spatial mapping of habenular neuronal types using single-cell RNA-seq
-
COI: 1:CAS:528:DC%2BC1cXlvVOhtbc%3D, PID: 29576475
-
Pandey, S., Shekhar, K., Regev, A. & Schier, A. F. Comprehensive identification and spatial mapping of habenular neuronal types using single-cell RNA-seq. Curr. Biol. 28, 1052–1065 (2018).
-
(2018)
Curr. Biol.
, vol.28
, pp. 1052-1065
-
-
Pandey, S.1
Shekhar, K.2
Regev, A.3
Schier, A.F.4
-
114
-
-
84966667709
-
destiny: diffusion maps for large-scale single-cell data in R
-
COI: 1:CAS:528:DC%2BC28XhtlGqurvE
-
Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).
-
(2016)
Bioinformatics
, vol.32
, pp. 1241-1243
-
-
Angerer, P.1
-
115
-
-
84990895380
-
De novo prediction of stem cell identity using single-cell transcriptome data
-
PID: 27345837
-
Grün, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19, 266–277 (2016).
-
(2016)
Cell Stem Cell
, vol.19
, pp. 266-277
-
-
Grün, D.1
|