Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks

Cell Systems - Tập 7 - Trang 185-191.e4 - 2018
Hyunghoon Cho1, Bonnie Berger1,2, Jian Peng3
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
2Department of Mathematics, MIT, Cambridge, MA 02139, USA
3Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

Tài liệu tham khảo

Amir, 2013, viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia, Nat. Biotechnol., 31, 545, 10.1038/nbt.2594 Amodio, 2017, Exploring single-cell data with deep multitasking neural networks, bioRxiv Anchang, 2016, Visualization and cellular hierarchy inference of single-cell data using SPADE, Nat. Protoc., 11, 1264, 10.1038/nprot.2016.066 Biase, 2014, Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing, Genome Res., 24, 1787, 10.1101/gr.177725.114 Bousquet, 2008, The tradeoffs of large scale learning, 161 Buettner, 2015, Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells, Nat. Biotechnol., 33, 155, 10.1038/nbt.3102 Deng, 2014, Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells, Science, 343, 193, 10.1126/science.1245316 Dzwinel, 2015, Very fast interactive visualization of large sets of high-dimensional data, Procedia Comput. Sci., 51, 572, 10.1016/j.procs.2015.05.325 Gawad, 2016, Single-cell genome sequencing: current state of the science, Nat. Rev. Genet., 17, 175, 10.1038/nrg.2015.16 Goolam, 2016, Heterogeneity in Oct4 and Sox2 targets biases cell fate in 4-cell mouse embryos, Cell, 165, 61, 10.1016/j.cell.2016.01.047 Grün, 2015, Single-cell messenger RNA sequencing reveals rare intestinal cell types, Nature, 525, 251, 10.1038/nature14966 Haghverdi, 2017, Correcting batch effects in single-cell RNA sequencing data by matching mutual nearest neighbours, bioRxiv Hartigan, 1979, Algorithm AS 136: a k-means clustering algorithm, J. R. Stat. Soc. Ser. C Appl. Stat., 28, 100 Hutchison, 2017, C. elegans exhibits coordinated oscillation in gene expression during development, bioRxiv Jackson, 2005 Jaitin, 2014, Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types, Science, 343, 776, 10.1126/science.1247651 Kikuchi-Taura, 2006, A new protocol for quantifying CD34+ cells in peripheral blood of patients with cardiovascular disease, Tex. Heart Inst. J., 33, 427 Kiselev, 2017, SC3: consensus clustering of single-cell RNA-seq data, Nat. Methods, 14, 483, 10.1038/nmeth.4236 Klein, 2015, Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells, Cell, 161, 1187, 10.1016/j.cell.2015.04.044 Kolodziejczyk, 2015, Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation, Cell Stem Cell, 17, 471, 10.1016/j.stem.2015.09.011 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Loh, 2012, Compressive genomics, Nat. Biotechnol., 30, 627, 10.1038/nbt.2241 Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579 Moon, 2017, Visualizing transitions and structure for high dimensional data exploration, bioRxiv Palmer, 2012, A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers, Genome Biol., 13, R71, 10.1186/gb-2012-13-8-r71 Patel, 2014, Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma, Science, 344, 1396, 10.1126/science.1254257 Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825 Pierson, 2015, ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis, Genome Biol., 16, 241, 10.1186/s13059-015-0805-z Pollen, 2014, Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex, Nat. Biotechnol., 32, 1053, 10.1038/nbt.2967 Qiu, 2017, Reversed graph embedding resolves complex single-cell trajectories, Nat. Methods, 14, 979, 10.1038/nmeth.4402 Rand, 1971, Objective criteria for the evaluation of clustering methods, J. Am. Stat. Assoc., 66, 846, 10.1080/01621459.1971.10482356 Regev, 2017, The human cell atlas, Elife, 6, 10.7554/eLife.27041 Samet, 1984, The quadtree and related hierarchical data structures, ACM Comput. Surv., 16, 187, 10.1145/356924.356930 Simmons, 2015, Discovering what dimensionality reduction really tells us about RNA-seq data, J. Comput. Biol., 22, 715, 10.1089/cmb.2015.0085 Stubbington, 2017, Single-cell transcriptomics to explore the immune system in health and disease, Science, 358, 58, 10.1126/science.aan6828 Svensson, 2018, Exponential scaling of single-cell RNA-seq in the past decade, Nat. Protoc., 13, 599, 10.1038/nprot.2017.149 Tang, J., Liu, J., Zhang, M., and Mei, Q. (2016). Visualizing large-scale and high-dimensional data. Proceedings of the 25th International Conference on World Wide Web 287–297. https://doi.org/10.1145/2872427.2883041. Ting, 2014, Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells, Cell Rep., 8, 1905, 10.1016/j.celrep.2014.08.029 Treutlein, 2014, Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq, Nature, 509, 371, 10.1038/nature13173 Tung, 2017, Batch effects and the effective design of single-cell gene expression studies, Sci. Rep., 7, 39921, 10.1038/srep39921 Usoskin, 2015, Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing, Nat. Neurosci., 18, 145, 10.1038/nn.3881 Van Der Maaten, 2014, Accelerating t-SNE using tree-based algorithms, J. Mach. Learn. Res., 15, 3221 Van Der Maaten, 2009, Learning a parametric embedding by preserving local structure, RBM, 500, 26 Wang, 2017, Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning, Nat. Methods, 14, 414, 10.1038/nmeth.4207 Wang, 2014, Clonal evolution in breast cancer revealed by single nucleus genome sequencing, Nature, 512, 155, 10.1038/nature13600 Yoon, 2011, Single-cell genomics reveals organismal interactions in uncultivated marine protists, Science, 332, 714, 10.1126/science.1203163 Yu, 2015, Entropy-scaling search of massive biological data, Cell Syst., 1, 130, 10.1016/j.cels.2015.08.004 Zeisel, 2015, Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq, Science, 347, 1138, 10.1126/science.aaa1934 Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat. Commun., 8, 14049, 10.1038/ncomms14049