-
1
-
-
85044252958
-
Exponential scaling of single-cell RNA-seq in the past decade
-
Svensson, V., et al. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13 (2018), 599–604.
-
(2018)
Nat. Protoc.
, vol.13
, pp. 599-604
-
-
Svensson, V.1
-
2
-
-
85074743402
-
A curated database reveals trends in single cell transcriptomics
-
Published online August 21, 2019
-
Svensson, V., da Veiga Beltrame, E., A curated database reveals trends in single cell transcriptomics. bioRxiv, 2019, 10.1101/742304 Published online August 21, 2019.
-
(2019)
bioRxiv
-
-
Svensson, V.1
da Veiga Beltrame, E.2
-
3
-
-
0020322129
-
B-cell subpopulations identified by two-colour fluorescence analysis
-
Hardy, R.R., et al. B-cell subpopulations identified by two-colour fluorescence analysis. Nature 297 (1982), 589–591.
-
(1982)
Nature
, vol.297
, pp. 589-591
-
-
Hardy, R.R.1
-
4
-
-
43749098985
-
DNA methylation landscapes: provocative insights from epigenomics
-
Suzuki, M.M., Bird, A., DNA methylation landscapes: provocative insights from epigenomics. Nat. Rev. Genet. 9 (2008), 465–476.
-
(2008)
Nat. Rev. Genet.
, vol.9
, pp. 465-476
-
-
Suzuki, M.M.1
Bird, A.2
-
5
-
-
85053883946
-
High-resolution single-cell DNA methylation measurements reveal epigenetically distinct hematopoietic stem cell subpopulations
-
Hui, T., et al. High-resolution single-cell DNA methylation measurements reveal epigenetically distinct hematopoietic stem cell subpopulations. Stem Cell Rep. 11 (2018), 578–592.
-
(2018)
Stem Cell Rep.
, vol.11
, pp. 578-592
-
-
Hui, T.1
-
6
-
-
84865790047
-
An integrated encyclopedia of DNA elements in the human genome
-
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489 (2012), 57–74.
-
(2012)
Nature
, vol.489
, pp. 57-74
-
-
-
7
-
-
84937857359
-
Single-cell chromatin accessibility reveals principles of regulatory variation
-
Buenrostro, J.D., et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523 (2015), 486–490.
-
(2015)
Nature
, vol.523
, pp. 486-490
-
-
Buenrostro, J.D.1
-
8
-
-
84930006926
-
Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
-
Cusanovich, D.A., et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348 (2015), 910–914.
-
(2015)
Science
, vol.348
, pp. 910-914
-
-
Cusanovich, D.A.1
-
9
-
-
85058756191
-
A rapid and robust method for single cell chromatin accessibility profiling
-
Chen, X., et al. A rapid and robust method for single cell chromatin accessibility profiling. Nat. Commun., 9, 2018, 5345.
-
(2018)
Nat. Commun.
, vol.9
, pp. 5345
-
-
Chen, X.1
-
10
-
-
84885617426
-
Single-cell Hi-C reveals cell-to-cell variability in chromosome structure
-
Nagano, T., et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502 (2013), 59–64.
-
(2013)
Nature
, vol.502
, pp. 59-64
-
-
Nagano, T.1
-
11
-
-
85065063725
-
CUT&Tag for efficient epigenomic profiling of small samples and single cells
-
Kaya-Okur, H.S., et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun., 10, 2019, 30.
-
(2019)
Nat. Commun.
, vol.10
, pp. 30
-
-
Kaya-Okur, H.S.1
-
12
-
-
85072708469
-
CoBATCH for high-throughput single-cell epigenomic profiling
-
Published online August 27, 2019
-
Wang, Q., et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol. Cell, 2019, 10.1016/j.molcel.2019.07.015 Published online August 27, 2019.
-
(2019)
Mol. Cell
-
-
Wang, Q.1
-
13
-
-
85055073919
-
SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation
-
Budnik, B., et al. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol., 19, 2018, 161.
-
(2018)
Genome Biol.
, vol.19
, pp. 161
-
-
Budnik, B.1
-
14
-
-
85028316331
-
Simultaneous epitope and transcriptome measurement in single cells
-
Stoeckius, M., et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14 (2017), 865–868.
-
(2017)
Nat. Methods
, vol.14
, pp. 865-868
-
-
Stoeckius, M.1
-
15
-
-
85031308868
-
Multiplexed quantification of proteins and transcripts in single cells
-
Peterson, V.M., et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35 (2017), 936–939.
-
(2017)
Nat. Biotechnol.
, vol.35
, pp. 936-939
-
-
Peterson, V.M.1
-
16
-
-
85032583384
-
SCENIC: single-cell regulatory network inference and clustering
-
Aibar, S., et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14 (2017), 1083–1086.
-
(2017)
Nat. Methods
, vol.14
, pp. 1083-1086
-
-
Aibar, S.1
-
17
-
-
85053831649
-
Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data
-
Pliner, H.A., et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71 (2018), 858–871.
-
(2018)
Mol. Cell
, vol.71
, pp. 858-871
-
-
Pliner, H.A.1
-
18
-
-
85040564848
-
SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles
-
Papili Gao, N., et al. SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. Bioinformatics 34 (2018), 258–266.
-
(2018)
Bioinformatics
, vol.34
, pp. 258-266
-
-
Papili Gao, N.1
-
19
-
-
85049250191
-
Multi-omics factor analysis – a framework for unsupervised integration of multi-omics data sets
-
Argelaguet, R., et al. Multi-omics factor analysis – a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology, 14, 2018, e8124.
-
(2018)
Molecular Systems Biology
, vol.14
, pp. e8124
-
-
Argelaguet, R.1
-
20
-
-
85018466550
-
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
-
Angermueller, C., et al. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol., 18, 2017, 67.
-
(2017)
Genome Biol.
, vol.18
, pp. 67
-
-
Angermueller, C.1
-
21
-
-
85010898455
-
Pooled CRISPR screening with single-cell transcriptome readout
-
Datlinger, P., et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14 (2017), 297–301.
-
(2017)
Nat. Methods
, vol.14
, pp. 297-301
-
-
Datlinger, P.1
-
22
-
-
85006488344
-
Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens
-
Dixit, A., et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167 (2016), 1853–1866.
-
(2016)
Cell
, vol.167
, pp. 1853-1866
-
-
Dixit, A.1
-
23
-
-
85064079871
-
Towards inferring causal gene regulatory networks from single cell expression measurements
-
Published online September 25, 2018
-
Qiu, X., et al. Towards inferring causal gene regulatory networks from single cell expression measurements. bioRxiv, 2018, 10.1101/426981 Published online September 25, 2018.
-
(2018)
bioRxiv
-
-
Qiu, X.1
-
24
-
-
67349146589
-
mRNA-Seq whole-transcriptome analysis of a single cell
-
Tang, F., et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6 (2009), 377–382.
-
(2009)
Nat. Methods
, vol.6
, pp. 377-382
-
-
Tang, F.1
-
25
-
-
85055080981
-
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
-
Tabula Muris Consortium, et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562 (2018), 367–372.
-
(2018)
Nature
, vol.562
, pp. 367-372
-
-
Tabula Muris Consortium1
-
26
-
-
85056913352
-
Mapping the mouse cell atlas by microwell-seq
-
Han, X., et al. Mapping the mouse cell atlas by microwell-seq. Cell, 173, 2018, 1307.
-
(2018)
Cell
, vol.173
, pp. 1307
-
-
Han, X.1
-
27
-
-
85028303209
-
Comprehensive single-cell transcriptional profiling of a multicellular organism
-
Cao, J., et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357 (2017), 661–667.
-
(2017)
Science
, vol.357
, pp. 661-667
-
-
Cao, J.1
-
28
-
-
85062419120
-
The single-cell transcriptional landscape of mammalian organogenesis
-
Cao, J., et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566 (2019), 496–502.
-
(2019)
Nature
, vol.566
, pp. 496-502
-
-
Cao, J.1
-
29
-
-
85058891736
-
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics
-
Stoeckius, M., et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol, 19, 2018, 224.
-
(2018)
Genome Biol
, vol.19
, pp. 224
-
-
Stoeckius, M.1
-
30
-
-
85044434871
-
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
-
Rosenberg, A.B., et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360 (2018), 176–182.
-
(2018)
Science
, vol.360
, pp. 176-182
-
-
Rosenberg, A.B.1
-
31
-
-
85040446434
-
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
-
Kang, H.M., et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36 (2018), 89–94.
-
(2018)
Nat. Biotechnol.
, vol.36
, pp. 89-94
-
-
Kang, H.M.1
-
32
-
-
85044715062
-
Single-cell RNA sequencing identifies cell type-specific cis-eQTLs and co-expression QTLs
-
van der Wijst, M.G.P., et al. Single-cell RNA sequencing identifies cell type-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50 (2018), 493–497.
-
(2018)
Nat. Genet.
, vol.50
, pp. 493-497
-
-
van der Wijst, M.G.P.1
-
33
-
-
85046289245
-
Scmap: projection of single-cell RNA-seq data across data sets
-
Kiselev, V.Y., et al. Scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15 (2018), 359–362.
-
(2018)
Nat. Methods
, vol.15
, pp. 359-362
-
-
Kiselev, V.Y.1
-
34
-
-
85064582461
-
Moana: a robust and scalable cell type classification framework for single-cell RNA-Seq data
-
Published online October 30, 2018
-
Wagner, F., Yanai, I., Moana: a robust and scalable cell type classification framework for single-cell RNA-Seq data. bioRxiv, 2018, 10.1101/456129 Published online October 30, 2018.
-
(2018)
bioRxiv
-
-
Wagner, F.1
Yanai, I.2
-
35
-
-
85067883031
-
Supervised classification enables rapid annotation of cell atlases
-
Published online February 25, 2019
-
Pliner, H.A., et al. Supervised classification enables rapid annotation of cell atlases. bioRxiv, 2019, 10.1101/538652 Published online February 25, 2019.
-
(2019)
bioRxiv
-
-
Pliner, H.A.1
-
36
-
-
85064247021
-
Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species
-
Published online December 14, 2018
-
Lotfollahi, M., et al. Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species. bioRxiv, 2018, 10.1101/478503 Published online December 14, 2018.
-
(2018)
bioRxiv
-
-
Lotfollahi, M.1
-
37
-
-
85040459896
-
The human cell atlas
-
Regev, A., et al. The human cell atlas. Elife, 6, 2017, e27041.
-
(2017)
Elife
, vol.6
, pp. e27041
-
-
Regev, A.1
-
38
-
-
21944445759
-
An ontology for cell types
-
Bard, J., et al. An ontology for cell types. Genome Biol, 6, 2005, R21.
-
(2005)
Genome Biol
, vol.6
, pp. R21
-
-
Bard, J.1
-
39
-
-
78650789238
-
Logical development of the cell ontology
-
Meehan, T.F., et al. Logical development of the cell ontology. BMC Bioinformatics, 12, 2011, 6.
-
(2011)
BMC Bioinformatics
, vol.12
, pp. 6
-
-
Meehan, T.F.1
-
40
-
-
85021335524
-
Single-cell alternative splicing analysis with Expedition reveals splicing dynamics during neuron differentiation
-
Song, Y., et al. Single-cell alternative splicing analysis with Expedition reveals splicing dynamics during neuron differentiation. Mol. Cell 67 (2017), 148–161.
-
(2017)
Mol. Cell
, vol.67
, pp. 148-161
-
-
Song, Y.1
-
41
-
-
85058796569
-
Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells
-
Gupta, I., et al. Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells. Nat. Biotechnol. 36 (2018), 1197–1202.
-
(2018)
Nat. Biotechnol.
, vol.36
, pp. 1197-1202
-
-
Gupta, I.1
-
42
-
-
85093417555
-
-
Identification of transcriptional signatures for cell types from single-cell RNA-seq. bioRxiv Published online February 14.
-
Ntranos, V. et al. Identification of transcriptional signatures for cell types from single-cell RNA-seq. bioRxiv Published online February 14, 2018. https://doi.org/10.1101/258566.
-
(2018)
-
-
Ntranos, V.1
-
43
-
-
85061380809
-
Single-cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation
-
Miragaia, R.J., et al. Single-cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation. Immunity 50 (2019), 493–504.
-
(2019)
Immunity
, vol.50
, pp. 493-504
-
-
Miragaia, R.J.1
-
44
-
-
84960194523
-
T cell fate and clonality inference from single-cell transcriptomes
-
Stubbington, M.J.T., et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13 (2016), 329–332.
-
(2016)
Nat. Methods
, vol.13
, pp. 329-332
-
-
Stubbington, M.J.T.1
-
45
-
-
85051486287
-
BraCeR: B-cell-receptor reconstruction and clonality inference from single-cell RNA-seq
-
Lindeman, I., et al. BraCeR: B-cell-receptor reconstruction and clonality inference from single-cell RNA-seq. Nat. Methods 15 (2018), 563–565.
-
(2018)
Nat. Methods
, vol.15
, pp. 563-565
-
-
Lindeman, I.1
-
46
-
-
84891677425
-
Full-length RNA-seq from single cells using Smart-seq2
-
Picelli, S., et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9 (2014), 171–181.
-
(2014)
Nat. Protoc.
, vol.9
, pp. 171-181
-
-
Picelli, S.1
-
47
-
-
85048957607
-
Single-cell map of diverse immune phenotypes in the breast tumor microenvironment
-
Azizi, E., et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174 (2018), 1293–1308.
-
(2018)
Cell
, vol.174
, pp. 1293-1308
-
-
Azizi, E.1
-
48
-
-
85069532270
-
High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes
-
Singh, M., et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat. Commun., 10, 2019, 3120.
-
(2019)
Nat. Commun.
, vol.10
, pp. 3120
-
-
Singh, M.1
-
49
-
-
85022229481
-
Identifying specificity groups in the T cell receptor repertoire
-
Glanville, J., et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547 (2017), 94–98.
-
(2017)
Nature
, vol.547
, pp. 94-98
-
-
Glanville, J.1
-
50
-
-
85022212188
-
Quantifiable predictive features define epitope-specific T cell receptor repertoires
-
Dash, P., et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547 (2017), 89–93.
-
(2017)
Nature
, vol.547
, pp. 89-93
-
-
Dash, P.1
-
51
-
-
85040750667
-
Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria
-
Lönnberg, T., et al. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci Immunol, 2, 2017, eaal2192.
-
(2017)
Sci Immunol
, vol.2
, pp. eaal2192
-
-
Lönnberg, T.1
-
52
-
-
85045131214
-
Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain
-
Raj, B., et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36 (2018), 442–450.
-
(2018)
Nat. Biotechnol.
, vol.36
, pp. 442-450
-
-
Raj, B.1
-
53
-
-
85045136951
-
Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars
-
Spanjaard, B., et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36 (2018), 469–473.
-
(2018)
Nat. Biotechnol.
, vol.36
, pp. 469-473
-
-
Spanjaard, B.1
-
54
-
-
85056665737
-
Single-cell reconstruction of the early maternal-fetal interface in humans
-
Vento-Tormo, R., et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563 (2018), 347–353.
-
(2018)
Nature
, vol.563
, pp. 347-353
-
-
Vento-Tormo, R.1
-
55
-
-
85061736757
-
CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones
-
Müller, S., et al. CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones. Bioinformatics 34 (2018), 3217–3219.
-
(2018)
Bioinformatics
, vol.34
, pp. 3217-3219
-
-
Müller, S.1
-
56
-
-
85062864174
-
Clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers
-
Campbell, K.R., et al. Clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers. Genome Biol., 20, 2018, 54.
-
(2018)
Genome Biol.
, vol.20
, pp. 54
-
-
Campbell, K.R.1
-
57
-
-
85062941775
-
Cardelino: integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants
-
Published online November 26, 2018
-
McCarthy, D.J., et al. Cardelino: integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants. bioRxiv, 2018, 10.1101/413047 Published online November 26, 2018.
-
(2018)
bioRxiv
-
-
McCarthy, D.J.1
-
58
-
-
85074744215
-
Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA
-
Published online November 29, 2018
-
Xu, J., et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. bioRxiv, 2018, 10.1101/480202 Published online November 29, 2018.
-
(2018)
bioRxiv
-
-
Xu, J.1
-
59
-
-
85062426134
-
Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics
-
Ludwig, L.S., et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176 (2019), 1325–1339.
-
(2019)
Cell
, vol.176
, pp. 1325-1339
-
-
Ludwig, L.S.1
-
60
-
-
85020287843
-
A unique microglia type associated with restricting development of Alzheimer's disease
-
Keren-Shaul, H., et al. A unique microglia type associated with restricting development of Alzheimer's disease. Cell 169 (2017), 1276–1290.
-
(2017)
Cell
, vol.169
, pp. 1276-1290
-
-
Keren-Shaul, H.1
-
61
-
-
85063634593
-
A cellular census of healthy lung and asthmatic airway wall identifies novel cell states in health and disease
-
Published online January 23, 2019
-
Vieira Braga, F.A., et al. A cellular census of healthy lung and asthmatic airway wall identifies novel cell states in health and disease. bioRxiv, 2019, 10.1101/527408 Published online January 23, 2019.
-
(2019)
bioRxiv
-
-
Vieira Braga, F.A.1
-
62
-
-
85033226512
-
Single-cell analysis identifies cellular markers of the HIV permissive cell
-
Rato, S., et al. Single-cell analysis identifies cellular markers of the HIV permissive cell. PLoS Pathog., 13, 2017, e1006678.
-
(2017)
PLoS Pathog.
, vol.13
, pp. e1006678
-
-
Rato, S.1
-
63
-
-
85045284573
-
Single-cell RNA-seq reveals transcriptional heterogeneity in latent and reactivated HIV-infected cells
-
Golumbeanu, M., et al. Single-cell RNA-seq reveals transcriptional heterogeneity in latent and reactivated HIV-infected cells. Cell Rep. 23 (2018), 942–950.
-
(2018)
Cell Rep.
, vol.23
, pp. 942-950
-
-
Golumbeanu, M.1
-
64
-
-
85059214054
-
Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue
-
Zanini, F., et al. Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue. Proc. Natl. Acad. Sci. U. S. A. 115 (2018), E12363–E12369.
-
(2018)
Proc. Natl. Acad. Sci. U. S. A.
, vol.115
, pp. E12363-E12369
-
-
Zanini, F.1
-
65
-
-
85043531075
-
Extreme heterogeneity of influenza virus infection in single cells
-
Russell, A.B., et al. Extreme heterogeneity of influenza virus infection in single cells. Elife, 7, 2018, e32303.
-
(2018)
Elife
, vol.7
, pp. e32303
-
-
Russell, A.B.1
-
66
-
-
85074746227
-
Single-cell RNA-sequencing of herpes simplex virus 1-infected cells identifies NRF2 activation as an antiviral program
-
Published online March 4, 2019
-
Wyler, E., et al. Single-cell RNA-sequencing of herpes simplex virus 1-infected cells identifies NRF2 activation as an antiviral program. bioRxiv, 2019, 10.1101/566992 Published online March 4, 2019.
-
(2019)
bioRxiv
-
-
Wyler, E.1
-
67
-
-
85068196924
-
HSV-1 single cell analysis reveals anti-viral and developmental programs activation in distinct sub-populations
-
Drayman, N., et al. HSV-1 single cell analysis reveals anti-viral and developmental programs activation in distinct sub-populations. Elife, 8, 2019, e46330.
-
(2019)
Elife
, vol.8
, pp. e46330
-
-
Drayman, N.1
-
68
-
-
85045640021
-
Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites
-
Reid, A.J., et al. Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites. Elife, 7, 2018, e33105.
-
(2018)
Elife
, vol.7
, pp. e33105
-
-
Reid, A.J.1
-
69
-
-
85047409215
-
Single-cell transcriptome profiling of protozoan and metazoan parasites
-
Nötzel, C., et al. Single-cell transcriptome profiling of protozoan and metazoan parasites. Trends Parasitol 34 (2018), 731–734.
-
(2018)
Trends Parasitol
, vol.34
, pp. 731-734
-
-
Nötzel, C.1
-
70
-
-
84930178333
-
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
-
Macaulay, I.C., et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12 (2015), 519–522.
-
(2015)
Nat. Methods
, vol.12
, pp. 519-522
-
-
Macaulay, I.C.1
-
71
-
-
85009781625
-
Single-cell multiomics: multiple measurements from single cells
-
Macaulay, I.C., et al. Single-cell multiomics: multiple measurements from single cells. Trends Genet 33 (2017), 155–168.
-
(2017)
Trends Genet
, vol.33
, pp. 155-168
-
-
Macaulay, I.C.1
-
72
-
-
84937251378
-
A functional perspective on phenotypic heterogeneity in microorganisms
-
Ackermann, M., A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13 (2015), 497–508.
-
(2015)
Nat. Rev. Microbiol.
, vol.13
, pp. 497-508
-
-
Ackermann, M.1
-
73
-
-
85032571368
-
The trajectory of microbial single-cell sequencing
-
Woyke, T., et al. The trajectory of microbial single-cell sequencing. Nat. Methods 14 (2017), 1045–1054.
-
(2017)
Nat. Methods
, vol.14
, pp. 1045-1054
-
-
Woyke, T.1
-
74
-
-
85061775708
-
Single-cell analysis of diverse pathogen responses defines a molecular roadmap for generating antigen-specific immunity
-
Blecher-Gonen, R., et al. Single-cell analysis of diverse pathogen responses defines a molecular roadmap for generating antigen-specific immunity. Cell Syst. 8 (2019), 109–121.
-
(2019)
Cell Syst.
, vol.8
, pp. 109-121
-
-
Blecher-Gonen, R.1
-
75
-
-
85047186828
-
Mapping the physical network of cellular interactions
-
Boisset, J.-C., et al. Mapping the physical network of cellular interactions. Nat. Methods 15 (2018), 547–553.
-
(2018)
Nat. Methods
, vol.15
, pp. 547-553
-
-
Boisset, J.-C.1
-
76
-
-
84928395184
-
Spatially resolved, highly multiplexed RNA profiling in single cells
-
Chen, K.H., et al. Spatially resolved, highly multiplexed RNA profiling in single cells. Science, 348, 2015, aaa6090.
-
(2015)
Science
, vol.348
, pp. aaa6090
-
-
Chen, K.H.1
-
77
-
-
84976875145
-
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
-
Ståhl, P.L., et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353 (2016), 78–82.
-
(2016)
Science
, vol.353
, pp. 78-82
-
-
Ståhl, P.L.1
-
78
-
-
85074744679
-
High-density spatial transcriptomics arrays for in situ tissue profiling
-
Published online March 13, 2019
-
Vickovic, S., et al. High-density spatial transcriptomics arrays for in situ tissue profiling. bioRxiv, 2019, 10.1101/563338 Published online March 13, 2019.
-
(2019)
bioRxiv
-
-
Vickovic, S.1
-
79
-
-
85064114011
-
Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution
-
Rodriques, S.G., et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363 (2019), 1463–1467.
-
(2019)
Science
, vol.363
, pp. 1463-1467
-
-
Rodriques, S.G.1
-
80
-
-
85057586270
-
Deep generative modeling for single-cell transcriptomics
-
Lopez, R., et al. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15 (2018), 1053–1058.
-
(2018)
Nat. Methods
, vol.15
, pp. 1053-1058
-
-
Lopez, R.1
-
81
-
-
85066448459
-
Comprehensive integration of single cell data
-
P188–1902.E21
-
Stuart, T., et al. Comprehensive integration of single cell data. Cell, 177, 2019 P188–1902.E21.
-
(2019)
Cell
, vol.177
-
-
Stuart, T.1
-
82
-
-
85059455396
-
Integrative inference of brain cell similarities and differences from single-cell genomics
-
Published online November 2, 2018
-
Welch, J., et al. Integrative inference of brain cell similarities and differences from single-cell genomics. bioRxiv, 2018, 10.1101/459891 Published online November 2, 2018.
-
(2018)
bioRxiv
-
-
Welch, J.1
-
83
-
-
85057943596
-
Integrating single-cell RNA-seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis
-
Published online March 13, 2019
-
Moncada, R., et al. Integrating single-cell RNA-seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis. bioRxiv, 2019, 10.1101/254375 Published online March 13, 2019.
-
(2019)
bioRxiv
-
-
Moncada, R.1
-
84
-
-
85074743312
-
Harmonization and annotation of single-cell transcriptomics data with deep generative models
-
Published online January 29, 2019
-
Xu, C., et al. Harmonization and annotation of single-cell transcriptomics data with deep generative models. bioRxiv, 2019, 10.1101/532895 Published online January 29, 2019.
-
(2019)
bioRxiv
-
-
Xu, C.1
-
85
-
-
85016143105
-
Dermatologist-level classification of skin cancer with deep neural networks
-
Esteva, A., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (2017), 115–118.
-
(2017)
Nature
, vol.542
, pp. 115-118
-
-
Esteva, A.1
-
86
-
-
85050855414
-
Generative adversarial networks simulate gene expression and predict perturbations in single cells
-
Published online July 30, 2018
-
Ghahramani, A., et al. Generative adversarial networks simulate gene expression and predict perturbations in single cells. bioRxiv, 2018, 10.1101/262501 Published online July 30, 2018.
-
(2018)
bioRxiv
-
-
Ghahramani, A.1
-
87
-
-
84930630277
-
Deep learning
-
LeCun, Y., et al. Deep learning. Nature 521 (2015), 436–444.
-
(2015)
Nature
, vol.521
, pp. 436-444
-
-
LeCun, Y.1
-
88
-
-
85057576705
-
scVAE: variational auto-encoders for single-cell gene expression data
-
Published online May 21, 2019
-
Grønbech, C.H., et al. scVAE: variational auto-encoders for single-cell gene expression data. bioRxiv, 2019, 10.1101/318295 Published online May 21, 2019.
-
(2019)
bioRxiv
-
-
Grønbech, C.H.1
-
89
-
-
85031092991
-
Gene regulatory network inference from single-cell data using multivariate information measures
-
Chan, T.E., et al. Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst. 5 (2017), 251–267.
-
(2017)
Cell Syst.
, vol.5
, pp. 251-267
-
-
Chan, T.E.1
-
90
-
-
85055863346
-
Large-scale multi-class image-based cell classification with deep learning
-
Meng, N., et al. Large-scale multi-class image-based cell classification with deep learning. IEEE J. Biomed. Health Inform. 23 (2018), 2091–2098.
-
(2018)
IEEE J. Biomed. Health Inform.
, vol.23
, pp. 2091-2098
-
-
Meng, N.1
-
91
-
-
85060821631
-
Label-free classification of cells based on supervised machine learning of subcellular structures
-
Ozaki, Y., et al. Label-free classification of cells based on supervised machine learning of subcellular structures. PLoS One, 14, 2019, e0211347.
-
(2019)
PLoS One
, vol.14
, pp. e0211347
-
-
Ozaki, Y.1
-
92
-
-
85048928895
-
Generalizable and scalable visualization of single-cell data using neural networks
-
Cho, H., et al. Generalizable and scalable visualization of single-cell data using neural networks. Cell Syst. 7 (2018), 185–191.
-
(2018)
Cell Syst.
, vol.7
, pp. 185-191
-
-
Cho, H.1
-
93
-
-
85093617858
-
scCapsNet: a deep learning classifier with the capability of interpretable feature extraction, applicable for single cell RNA data analysis
-
Published online May 21, 2019
-
Wang, L., et al. scCapsNet: a deep learning classifier with the capability of interpretable feature extraction, applicable for single cell RNA data analysis. bioRxiv, 2019, 10.1101/506642 Published online May 21, 2019.
-
(2019)
bioRxiv
-
-
Wang, L.1
-
94
-
-
85074745558
-
Droplet scRNA-seq is not zero-inflated
-
Published online March 19, 2019
-
Svensson, V., Droplet scRNA-seq is not zero-inflated. bioRxiv, 2019, 10.1101/582064 Published online March 19, 2019.
-
(2019)
bioRxiv
-
-
Svensson, V.1
-
95
-
-
85066928572
-
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
-
Published online March 18, 2019
-
Hafemeister, C., Satija, R., Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. bioRxiv, 2019, 10.1101/576827 Published online March 18, 2019.
-
(2019)
bioRxiv
-
-
Hafemeister, C.1
Satija, R.2
-
96
-
-
84860909299
-
A theory of biological relativity: no privileged level of causation
-
Noble, D., A theory of biological relativity: no privileged level of causation. Interface Focus 2 (2012), 55–64.
-
(2012)
Interface Focus
, vol.2
, pp. 55-64
-
-
Noble, D.1
-
97
-
-
85062300354
-
A single-cell molecular map of mouse gastrulation and early organogenesis
-
Pijuan-Sala, B., et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566 (2019), 490–495.
-
(2019)
Nature
, vol.566
, pp. 490-495
-
-
Pijuan-Sala, B.1
-
98
-
-
85057333353
-
A primer on deep learning in genomics
-
Zou, J., et al. A primer on deep learning in genomics. Nat. Genet. 51 (2019), 12–18.
-
(2019)
Nat. Genet.
, vol.51
, pp. 12-18
-
-
Zou, J.1
-
99
-
-
85057555111
-
A primer on data analytics in functional genomics: how to move from data to insight?
-
Grabowski, P., Rappsilber, J., A primer on data analytics in functional genomics: how to move from data to insight?. Trends Biochem. Sci. 44 (2019), 21–32.
-
(2019)
Trends Biochem. Sci.
, vol.44
, pp. 21-32
-
-
Grabowski, P.1
Rappsilber, J.2
-
100
-
-
84990070233
-
Tutorial on variational autoencoders
-
Published online June 19, 2016
-
Doersch, C., Tutorial on variational autoencoders. arRxiv, 2016 Published online June 19, 2016 https://arxiv.org/abs/1606.05908.
-
(2016)
arRxiv
-
-
Doersch, C.1
|