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Volumn 35, Issue 16, 2019, Pages 2809-2817

Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CELL POPULATION; CLUSTERING ALGORITHM; DIMENSIONALITY REDUCTION; GENE EXPRESSION; MEMORY; MULTIOBJECTIVE OPTIMIZATION; NOISE; SIMULATION; SINGLE CELL RNA SEQ; VALIDITY; ALGORITHM; CLUSTER ANALYSIS; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; WHOLE EXOME SEQUENCING;

EID: 85064195515     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bty1056     Document Type: Article
Times cited : (27)

References (42)
  • 1
    • 34547852262 scopus 로고    scopus 로고
    • An ensemble framework for clustering protein-protein interaction networks
    • Asur, S. et al. (2007) An ensemble framework for clustering protein-protein interaction networks. Bioinformatics, 23, i29-i40.
    • (2007) Bioinformatics , vol.23 , pp. i29-i40
    • Asur, S.1
  • 2
    • 61449214257 scopus 로고    scopus 로고
    • Fuzzy ensemble clustering based on random projections for DNAmicroarray data analysis
    • Avogadri, R. And Valentini, G. (2009) Fuzzy ensemble clustering based on random projections for DNAmicroarray data analysis. Artif. Intell. Med., 45, 173-183.
    • (2009) Artif. Intell. Med. , vol.45 , pp. 173-183
    • Avogadri, R.1    Valentini, G.2
  • 3
    • 84923292191 scopus 로고    scopus 로고
    • Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
    • Buettner, F. et al. (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol., 33, 155.
    • (2015) Nat. Biotechnol. , vol.33 , pp. 155
    • Buettner, F.1
  • 4
    • 33645316810 scopus 로고    scopus 로고
    • Link-based similarity measures for the classification of web documents
    • Calado, P. et al. (2006) Link-based similarity measures for the classification of web documents. J. Am. Soc. Inform. Sci. Technol., 57, 208-221.
    • (2006) J. Am. Soc. Inform. Sci. Technol. , vol.57 , pp. 208-221
    • Calado, P.1
  • 5
    • 79952003251 scopus 로고    scopus 로고
    • Differential evolution: A survey of the state-of-the-art
    • Das, S. And Suganthan, P. N. (2011) Differential evolution: A survey of the state-of-the-art. IEEE Trans. Evol. Comput., 15, 4-31.
    • (2011) IEEE Trans. Evol. Comput. , vol.15 , pp. 4-31
    • Das, S.1    Suganthan, P.N.2
  • 6
    • 84905581607 scopus 로고    scopus 로고
    • An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problemswith box constraints
    • Deb, K. And Jain, H. (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problemswith box constraints. IEEE Trans. Evol. Comput., 18, 577-601.
    • (2014) IEEE Trans. Evol. Comput. , vol.18 , pp. 577-601
    • Deb, K.1    Jain, H.2
  • 7
    • 84892179132 scopus 로고    scopus 로고
    • Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells
    • Deng, Q. et al. (2014) Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science, 343, 193-196.
    • (2014) Science , vol.343 , pp. 193-196
    • Deng, Q.1
  • 9
    • 48249087654 scopus 로고    scopus 로고
    • Ensemble non-negative matrix factorization methods for clustering proteinymposiumonDeng>/snam
    • Greene, D. et al. (2008) Ensemble non-negative matrix factorization methods for clustering proteinymposiumonDeng>/snam. Bioinformatics, 24, 1722-1728.
    • (2008) Bioinformatics , vol.24 , pp. 1722-1728
    • Greene, D.1
  • 10
    • 80052878044 scopus 로고    scopus 로고
    • Non-negative matrix factorization as a feature selection tool for maximum margin classifiers
    • Colorado Springs, CO, USA
    • Gupta, M. D. And Xiao, J. (2011) Non-negative matrix factorization as a feature selection tool for maximum margin classifiers. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, Colorado Springs, CO, USA, pp 2841-2848.
    • (2011) Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on , pp. 2841-2848
    • Gupta, M.D.1    Xiao, J.2
  • 11
    • 77954195198 scopus 로고    scopus 로고
    • Lce: A link-based cluster ensemble method for improved gene expression data analysis
    • Iam-On, N. et al. (2010a) Lce: A link-based cluster ensemble method for improved gene expression data analysis. Bioinformatics, 26, 1513-1519.
    • (2010) Bioinformatics , vol.26 , pp. 1513-1519
    • Iam-On, N.1
  • 12
    • 77958118466 scopus 로고    scopus 로고
    • Linkclue: A matlab package for link-based cluster ensembles
    • Iam-On, N. et al. (2010b) Linkclue: A matlab package for link-based cluster ensembles. J. Stat. Softw., 36, 1-36.
    • (2010) J. Stat. Softw. , vol.36 , pp. 1-36
    • Iam-On, N.1
  • 13
    • 84856480658 scopus 로고    scopus 로고
    • A link-based cluster ensemble approach for categorical data clustering
    • Iam-On, N. et al. (2012) A link-based cluster ensemble approach for categorical data clustering. IEEE Trans. Knowl. Data Eng., 24, 413-425.
    • (2012) IEEE Trans. Knowl. Data Eng. , vol.24 , pp. 413-425
    • Iam-On, N.1
  • 14
    • 85062535444 scopus 로고    scopus 로고
    • Single cell clustering based on cell-pair differentiability correlation and variance analysis
    • Jiang, H. et al. (2018) Single cell clustering based on cell-pair differentiability correlation and variance analysis. Bioinformatics, 1, 11.
    • (2018) Bioinformatics , vol.1 , pp. 11
    • Jiang, H.1
  • 15
    • 84954562766 scopus 로고    scopus 로고
    • A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages
    • Kimmerling, R. J. et al. (2016) A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat. Commun., 7, 10220.
    • (2016) Nat. Commun. , vol.7 , pp. 10220
    • Kimmerling, R.J.1
  • 16
    • 85016121177 scopus 로고    scopus 로고
    • Sc3: Consensus clustering of single-cell RNA-seq data
    • Kiselev, V. Y. et al. (2017) Sc3: consensus clustering of single-cell RNA-seq data. Nat. Methods, 14, 483.
    • (2017) Nat. Methods , vol.14 , pp. 483
    • Kiselev, V.Y.1
  • 17
    • 33749410797 scopus 로고    scopus 로고
    • Analysing social networks within bibliographical data
    • Stephane, B. et al. (eds). Springer, Berlin, Heidelberg.
    • Klink, S. et al. (2006) Analysing social networks within bibliographical data. In: International Conference on Database and Expert Systems Applications, Stephane, B. et al. (eds) pp. 234-243. Springer, Berlin, Heidelberg.
    • (2006) International Conference on Database and Expert Systems Applications , pp. 234-243
    • Klink, S.1
  • 18
    • 84898964201 scopus 로고    scopus 로고
    • Algorithms for non-negative matrix factorization
    • MIT Press, Cambridge, MA, USA
    • Lee, D. D. And Seung, H. S. (2001) Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, USA, pp. 556-562.
    • (2001) Advances in Neural Information Processing Systems , pp. 556-562
    • Lee, D.D.1    Seung, H.S.2
  • 19
    • 85063924923 scopus 로고    scopus 로고
    • Evolutionarymultiobjective clustering and its applications to patient stratification
    • Li, X. And Wong, K.-C. (2018) Evolutionarymultiobjective clustering and its applications to patient stratification. IEEE Trans. Cybernetics, 99, 1-14.
    • (2018) IEEE Trans. Cybernetics , vol.99 , pp. 1-14
    • Li, X.1    Wong, K.-C.2
  • 20
    • 85023603302 scopus 로고    scopus 로고
    • Evolving spatial clusters of genomic regions from high-throughput chromatin conformation capture data
    • Li, X. et al. (2017) Evolving spatial clusters of genomic regions from high-throughput chromatin conformation capture data. IEEE Trans. Nanobiosci., 16, 400-407.
    • (2017) IEEE Trans. Nanobiosci. , vol.16 , pp. 400-407
    • Li, X.1
  • 21
    • 85032672184 scopus 로고    scopus 로고
    • Entropy-based consensus clustering for patient stratification
    • Liu, H. et al. (2017) Entropy-based consensus clustering for patient stratification. Bioinformatics, 33, 2691-2698.
    • (2017) Bioinformatics , vol.33 , pp. 2691-2698
    • Liu, H.1
  • 22
  • 23
    • 84930656510 scopus 로고    scopus 로고
    • A survey of multiobjective evolutionary clustering
    • Mukhopadhyay, A. et al. (2015) A survey of multiobjective evolutionary clustering. ACM Comput. Surveys, 47, 1.
    • (2015) ACM Comput. Surveys , vol.47 , pp. 1
    • Mukhopadhyay, A.1
  • 24
    • 85083447077 scopus 로고    scopus 로고
    • Spectral clustering based on learning similarity matrix
    • Park, S. et al. (2018) Spectral clustering based on learning similarity matrix. Bioinformatics, 1, 8.
    • (2018) Bioinformatics , vol.1 , pp. 8
    • Park, S.1
  • 25
    • 84922321862 scopus 로고    scopus 로고
    • Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex
    • Pollen, A. A. et al. (2014) Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol., 32, 1053.
    • (2014) Nat. Biotechnol. , vol.32 , pp. 1053
    • Pollen, A.A.1
  • 26
    • 84864880991 scopus 로고    scopus 로고
    • Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells
    • Ramsköld, D. et al. (2012) Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol., 30, 777.
    • (2012) Nat. Biotechnol. , vol.30 , pp. 777
    • Ramsköld, D.1
  • 27
    • 84903289127 scopus 로고    scopus 로고
    • Clustering by fast search and find of density peaks
    • Rodriguez, A. And Laio, A. (2014) Clustering by fast search and find of density peaks. Science, 344, 1492-1496.
    • (2014) Science , vol.344 , pp. 1492-1496
    • Rodriguez, A.1    Laio, A.2
  • 28
    • 84931394611 scopus 로고    scopus 로고
    • Identification of cdc1-and cdc2-committed dc progenitors reveals early lineage priming at the common dc progenitor stage in the bone marrow
    • Schlitzer, A. et al. (2015) Identification of cdc1-and cdc2-committed dc progenitors reveals early lineage priming at the common dc progenitor stage in the bone marrow. Nat. Immunol., 16, 718.
    • (2015) Nat. Immunol. , vol.16 , pp. 718
    • Schlitzer, A.1
  • 29
    • 84922334171 scopus 로고    scopus 로고
    • How deep is enough in single-cell RNA-seq Nat
    • Streets, A. M. And Huang, Y. (2014) How deep is enough in single-cell RNA-seq Nat. Biotechnol., 32, 1005.
    • (2014) Biotechnol. , vol.32 , pp. 1005
    • Streets, A.M.1    Huang, Y.2
  • 30
    • 84907414338 scopus 로고    scopus 로고
    • Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells
    • Ting, D. T. et al. (2014) Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep., 8, 1905-1918.
    • (2014) Cell Rep. , vol.8 , pp. 1905-1918
    • Ting, D.T.1
  • 31
    • 84900529199 scopus 로고    scopus 로고
    • Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
    • Treutlein, B. et al. (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature, 509, 371.
    • (2014) Nature , vol.509 , pp. 371
    • Treutlein, B.1
  • 32
    • 34548583274 scopus 로고    scopus 로고
    • A tutorial on spectral clustering
    • Von Luxburg, U. (2007) A tutorial on spectral clustering. Stat. Comput., 17, 395-416.
    • (2007) Stat. Comput. , vol.17 , pp. 395-416
    • Von Luxburg, U.1
  • 33
    • 85014528252 scopus 로고    scopus 로고
    • Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
    • Wang, B. et al. (2017) Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat. Methods, 14, 414.
    • (2017) Nat. Methods , vol.14 , pp. 414
    • Wang, B.1
  • 34
    • 85030322452 scopus 로고    scopus 로고
    • Saic: An iterative clustering approach for analysis of single cell RNA-seq data
    • Yang, L. et al. (2017) Saic: An iterative clustering approach for analysis of single cell RNA-seq data. BMC Genomics, 18, 689.
    • (2017) BMC Genomics , vol.18 , pp. 689
    • Yang, L.1
  • 35
    • 78951491903 scopus 로고    scopus 로고
    • A review of ensemble methods in bioinformatics
    • Yang, P. et al. (2010) A review of ensemble methods in bioinformatics. Curr. Bioinformatics, 5, 296-308.
    • (2010) Curr. Bioinformatics , vol.5 , pp. 296-308
    • Yang, P.1
  • 36
    • 36448947175 scopus 로고    scopus 로고
    • Graph-based consensus clustering for class discovery from gene expression data
    • Yu, Z. et al. (2007) Graph-based consensus clustering for class discovery from gene expression data. Bioinformatics, 23, 2888-2896.
    • (2007) Bioinformatics , vol.23 , pp. 2888-2896
    • Yu, Z.1
  • 37
    • 80051750853 scopus 로고    scopus 로고
    • Knowledge based cluster ensemble for cancer discovery from biomolecular data
    • Yu, Z. et al. (2011) Knowledge based cluster ensemble for cancer discovery from biomolecular data. IEEE Trans. Nanobiosci., 10, 76-85.
    • (2011) IEEE Trans. Nanobiosci. , vol.10 , pp. 76-85
    • Yu, Z.1
  • 38
    • 84924565530 scopus 로고    scopus 로고
    • Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
    • Zeisel, A. et al. (2015) Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347, 1138-1142.
    • (2015) Science , vol.347 , pp. 1138-1142
    • Zeisel, A.1
  • 39
    • 85046337732 scopus 로고    scopus 로고
    • A multitask clustering approach for single-cell RNA-seq analysis in recessive dystrophic epidermolysis bullosa
    • Zhang, H. et al. (2018a) A multitask clustering approach for single-cell RNA-seq analysis in recessive dystrophic epidermolysis bullosa. PLoS Comput. Biol., 14, e1006053.
    • (2018) PLoS Comput. Biol. , vol.14 , pp. e1006053
    • Zhang, H.1
  • 40
    • 85043484245 scopus 로고    scopus 로고
    • An interpretable framework for clustering single-cell RNA-seq datasets
    • Zhang, J. M. et al. (2018b) An interpretable framework for clustering single-cell RNA-seq datasets. BMC Bioinformatics, 19, 93.
    • (2018) BMC Bioinformatics , vol.19 , pp. 93
    • Zhang, J.M.1
  • 41
    • 34548108555 scopus 로고    scopus 로고
    • Moea/d: A multiobjective evolutionary algorithm based on decomposition
    • Zhang, Q. And Li, H. (2007) Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evolution. Comput., 11, 712-731.
    • (2007) IEEE Trans. Evolution. Comput. , vol.11 , pp. 712-731
    • Zhang, Q.1    Li, H.2
  • 42
    • 85013204492 scopus 로고    scopus 로고
    • Detecting heterogeneity in single-cell RNA-seq data by non-negative matrix factorization
    • Zhu, X. et al. (2017) Detecting heterogeneity in single-cell RNA-seq data by non-negative matrix factorization. PeerJ., 5, e2888.
    • (2017) PeerJ. , vol.5 , pp. e2888
    • Zhu, X.1


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