Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment
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
Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
Bhattacharyya, 1990, On a geometrical representation of probability distributions and its use in statistical inference, Calcutta Statist. Assoc. Bull., 40, 23, 10.1177/0008068319900504
Bray, 2016, Near-optimal probabilistic RNA-seq quantification, Nat. Biotechnol., 34, 525, 10.1038/nbt.3519
Chevrier, 2017, An immune atlas of clear cell renal cell carcinoma, Cell, 169, 736, 10.1016/j.cell.2017.04.016
Coifman, 2005, Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps, Proc. Natl. Acad. Sci. USA, 102, 7426, 10.1073/pnas.0500334102
Dobin, 2013, STAR: ultrafast universal RNA-seq aligner, Bioinformatics, 29, 15, 10.1093/bioinformatics/bts635
Dushyanthen, 2015, Relevance of tumor-infiltrating lymphocytes in breast cancer, BMC Med., 13, 202, 10.1186/s12916-015-0431-3
Engblom, 2016, The role of myeloid cells in cancer therapies, Nat. Rev. Cancer, 16, 447, 10.1038/nrc.2016.54
Faircloth, 2012, Not all sequence tags are created equal: designing and validating sequence identification tags robust to indels, PLoS One, 7, e42543, 10.1371/journal.pone.0042543
Fan, 2016, Hallmarks of tissue-resident lymphocytes, Cell, 164, 1198, 10.1016/j.cell.2016.02.048
Finck, 2013, Normalization of mass cytometry data with bead standards, Cytometry A, 83, 483, 10.1002/cyto.a.22271
Finger, 2010, Hypoxia, inflammation, and the tumor microenvironment in metastatic disease, Cancer Metastasis Rev., 29, 285, 10.1007/s10555-010-9224-5
Franklin, 2014, The cellular and molecular origin of tumor-associated macrophages, Science, 344, 921, 10.1126/science.1252510
Gaublomme, 2015, Single-cell genomics unveils critical regulators of Th17 cell pathogenicity, Cell, 163, 1400, 10.1016/j.cell.2015.11.009
Görür, 2010, Dirichlet process gaussian mixture models: choice of the base distribution, J. Comput. Sci. Technol., 25, 653, 10.1007/s11390-010-9355-8
Haghverdi, 2015, Diffusion maps for high-dimensional single-cell analysis of differentiation data, Bioinformatics, 31, 2989, 10.1093/bioinformatics/btv325
Hartigan, 1985, The dip test of unimodality, Ann. Stat., 13, 70, 10.1214/aos/1176346577
Jaitin, 2014, Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types, Science, 343, 776, 10.1126/science.1247651
Jebara, 2004, Probability product kernels, J. Mach. Learn. Res., 5, 819
Jeffrey, 2006, Positive regulation of immune cell function and inflammatory responses by phosphatase PAC-1, Nat. Immunol., 7, 274, 10.1038/ni1310
Jiménez-Sánchez, 2017, Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient, Cell, 170, 927, 10.1016/j.cell.2017.07.025
Joller, 2014, Treg cells expressing the coinhibitory molecule TIGIT selectively inhibit proinflammatory Th1 and Th17 cell responses, Immunity, 40, 569, 10.1016/j.immuni.2014.02.012
Josefowicz, 2012, Regulatory T cells: mechanisms of differentiation and function, Annu. Rev. Immunol., 30, 531, 10.1146/annurev.immunol.25.022106.141623
Klein, 2015, Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells, Cell, 161, 1187, 10.1016/j.cell.2015.04.044
Lavin, 2017, Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses, Cell, 169, 750, 10.1016/j.cell.2017.04.014
Levine, 2015, Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis, Cell, 162, 184, 10.1016/j.cell.2015.05.047
Li, 2011, RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome, BMC Bioinformatics, 12, 323, 10.1186/1471-2105-12-323
Mantovani, 2013, Tumor-associated macrophages as a paradigm of macrophage plasticity, diversity, and polarization: lessons and open questions, Arterioscler. Thromb. Vasc. Biol., 33, 1478, 10.1161/ATVBAHA.113.300168
Martinez, 2014, The M1 and M2 paradigm of macrophage activation: time for reassessment, F1000Prime Rep., 6, 13, 10.12703/P6-13
Müller, 2017, Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment, Genome Biol., 18, 234, 10.1186/s13059-017-1362-4
Murphy, 2007, Conjugate Bayesian analysis of the Gaussian distribution, def, 1, 16
Novershtern, 2011, Densely interconnected transcriptional circuits control cell states in human hematopoiesis, Cell, 144, 296, 10.1016/j.cell.2011.01.004
Pauken, 2015, Overcoming T cell exhaustion in infection and cancer, Trends Immunol., 36, 265, 10.1016/j.it.2015.02.008
Perdiguero, 2016, The development and maintenance of resident macrophages, Nat. Immunol., 17, 2, 10.1038/ni.3341
Philip, 2017, Chromatin states define tumour-specific T cell dysfunction and reprogramming, Nature, 545, 452, 10.1038/nature22367
Pitman, J. (2002). Combinatorial stochastic processes. http://statistics.berkeley.edu/tech-reports/621.
Plitas, 2016, Regulatory T cells exhibit distinct features in human breast cancer, Immunity, 45, 1122, 10.1016/j.immuni.2016.10.032
Prabhakaran, S., Azizi, E., Carr, A., and Pe’er, D. (2016). Dirichlet process mixture model for correcting technical variation in single-cell gene expression data. In Proceedings of the 33rd International Conference on Machine Learning, B. Maria Florina, and Q.W. Kilian, eds. (Proceedings of Machine Learning Research: PMLR), pp. 1070–1079.
Şenbabaoğlu, 2016, Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures, Genome Biol., 17, 231, 10.1186/s13059-016-1092-z
Singer, 2016, A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells, Cell, 166, 1500, 10.1016/j.cell.2016.08.052
Sun, 2017, Between-region genetic divergence reflects the mode and tempo of tumor evolution, Nat. Genet., 49, 1015, 10.1038/ng.3891
Tanaka, 2017, Regulatory T cells in cancer immunotherapy, Cell Res., 27, 109, 10.1038/cr.2016.151
Tao, 2006, On random±1 matrices: singularity and determinant, Random Structures Algorithms, 28, 1, 10.1002/rsa.20109
Tirosh, 2016, Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq, Science, 352, 189, 10.1126/science.aad0501
Topalian, 2015, Immune checkpoint blockade: a common denominator approach to cancer therapy, Cancer Cell, 27, 450, 10.1016/j.ccell.2015.03.001
van der Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579
Verdegaal, 2016, Neoantigen landscape dynamics during human melanoma-T cell interactions, Nature, 536, 91, 10.1038/nature18945
Wherry, 2015, Molecular and cellular insights into T cell exhaustion, Nat. Rev. Immunol., 15, 486, 10.1038/nri3862
Yates, 2015, Subclonal diversification of primary breast cancer revealed by multiregion sequencing, Nat. Med., 21, 751, 10.1038/nm.3886
Zemmour, 2018, Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR, Nat. Immunol., 19, 291, 10.1038/s41590-018-0051-0
Zheng, 2017, Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing, Cell, 169, 1342, 10.1016/j.cell.2017.05.035
Zilionis, 2017, Single-cell barcoding and sequencing using droplet microfluidics, Nat. Protoc., 12, 44, 10.1038/nprot.2016.154
Zunder, 2015, Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm, Nat Protoc, 10, 316, 10.1038/nprot.2015.020