Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis
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Ying-Lian Gao | Xiang-Zhen Kong | Cui-Na Jiao | Jin-Xing Liu | Chuan-Yuan Wang | Chun-Hou Zheng | C. Zheng | Jin-Xing Liu | Ying-Lian Gao | Xiangzhen Kong | Cui-Na Jiao | Chuan-Yuan Wang | Xiang-zhen Kong | Chuan-yuan Wang
[1] N. Neff,et al. Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.
[2] James Bailey,et al. Adjusting for Chance Clustering Comparison Measures , 2015, J. Mach. Learn. Res..
[3] Tianrui Li,et al. Nonnegative matrix factorization for clustering ensemble based on dark knowledge , 2019, Knowl. Based Syst..
[4] Hao Jiang,et al. Single cell clustering based on cell‐pair differentiability correlation and variance analysis , 2018, Bioinform..
[5] Yi Pan,et al. SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation , 2019, Bioinform..
[6] Charlotte Soneson,et al. A systematic performance evaluation of clustering methods for single-cell RNA-seq data , 2018, F1000Research.
[7] Wei Xing Zheng,et al. Distributed $k$ -Means Algorithm and Fuzzy $c$ -Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory , 2017, IEEE Transactions on Cybernetics.
[8] Wei Zhang,et al. SCCLRR: A Robust Computational Method for Accurate Clustering Single Cell RNA-Seq Data , 2020, IEEE Journal of Biomedical and Health Informatics.
[9] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[10] Bo Wang,et al. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning , 2016, Nature Methods.
[11] Aleksandra A. Kolodziejczyk,et al. Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation , 2015, Cell stem cell.
[12] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[13] Yun Fu,et al. Entropy‐based consensus clustering for patient stratification , 2017, Bioinform..
[14] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[15] Shuicheng Yan,et al. Convex Sparse Spectral Clustering: Single-View to Multi-View , 2015, IEEE Transactions on Image Processing.
[16] Jiguo Yu,et al. Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[17] D. Mock,et al. Innate-like functions of natural killer T cell subsets result from highly divergent gene programs , 2016, Nature Immunology.
[18] Sarah A. Teichmann,et al. Computational approaches for interpreting scRNA‐seq data , 2017, FEBS letters.
[19] S. Quake,et al. A survey of human brain transcriptome diversity at the single cell level , 2015, Proceedings of the National Academy of Sciences.
[20] Aaron M. Streets,et al. Microfluidic single-cell whole-transcriptome sequencing , 2014, Proceedings of the National Academy of Sciences.
[21] Fabian J Theis,et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.
[22] Sandy L. Klemm,et al. Single-Cell Expression Analyses during Cellular Reprogramming Reveal an Early Stochastic and a Late Hierarchic Phase , 2012, Cell.
[23] Staci A. Sorensen,et al. Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics , 2016 .
[24] R. Sandberg,et al. Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells , 2014, Science.
[25] J. Marioni,et al. Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos , 2016, Cell.
[26] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[27] Rona S. Gertner,et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.