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@article{trapnell_dynamics_2014,
	title = {The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells},
	volume = {32},
	issn = {1087-0156, 1546-1696},
	url = {http://www.nature.com/articles/nbt.2859},
	doi = {10.1038/nbt.2859},
	pages = {381--386},
	number = {4},
	journaltitle = {Nat Biotechnol},
	author = {<NAME> <NAME>, <NAME> <NAME>, <NAME>, <NAME> <NAME>},
	urldate = {2019-05-22},
	date = {2014-04},
	langid = {english},
	keywords = {Monocle1, Monocle {ICA}, {MST}},
	file = {Accepted Version:/Users/guosiyuan/Zotero/storage/HHDLRP47/Trapnell et al. - 2014 - The dynamics and regulators of cell fate decisions.pdf:application/pdf}
}

@article{street_slingshot:_2018,
	title = {Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics},
	volume = {19},
	issn = {1471-2164},
	url = {https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4772-0},
	doi = {10.1186/s12864-018-4772-0},
	shorttitle = {Slingshot},
	pages = {477},
	number = {1},
	journaltitle = {{BMC} Genomics},
	author = {Street, <NAME>, <NAME>. and <NAME> and <NAME> and <NAME> and Purdom, Elizabeth and <NAME>},
	urldate = {2019-05-22},
	date = {2018-12},
	langid = {english},
	keywords = {Slingshot},
	file = {Full Text:/Users/guosiyuan/Zotero/storage/SC2WRWZA/Street et al. - 2018 - Slingshot cell lineage and pseudotime inference f.pdf:application/pdf}
}

@article{qiu_reversed_2017,
	title = {Reversed graph embedding resolves complex single-cell trajectories},
	volume = {14},
	issn = {1548-7091, 1548-7105},
	url = {http://www.nature.com/articles/nmeth.4402},
	doi = {10.1038/nmeth.4402},
	pages = {979--982},
	number = {10},
	journaltitle = {Nat Methods},
	author = {<NAME> and <NAME> <NAME>, <NAME>, <NAME> and <NAME>},
	urldate = {2019-05-22},
	date = {2017-10},
	langid = {english},
	keywords = {Monocle2, Monocle {DDRTree}}
}

@article{grun_novo_2016,
	title = {De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data},
	volume = {19},
	issn = {19345909},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1934590916300947},
	doi = {10.1016/j.stem.2016.05.010},
	pages = {266--277},
	number = {2},
	journaltitle = {Cell Stem Cell},
	author = {<NAME>. and <NAME> and <NAME> and Lyubimova, Anna and <NAME> and van den Born, Maaike and <NAME>, <NAME>, <NAME>, Hans and <NAME>, <NAME>. and <NAME>, Alexander},
	urldate = {2019-05-22},
	date = {2016-08},
	langid = {english},
	keywords = {{RaceID} / {StemID}}
}

@article{herring_unsupervised_2018,
	title = {Unsupervised Trajectory Analysis of Single-Cell {RNA}-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut},
	volume = {6},
	issn = {24054712},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2405471217304490},
	doi = {10.1016/j.cels.2017.10.012},
	pages = {37--51.e9},
	number = {1},
	journaltitle = {Cell Systems},
	author = {Herring, <NAME>. and Banerjee, Amrita and {McKinley}, <NAME>. and Simmons, <NAME>. and <NAME> Roland, <NAME>. and Franklin, <NAME>. and <NAME> and Gerdes, <NAME>. and Coffey, <NAME>. and Lau, <NAME>.},
	urldate = {2019-05-22},
	date = {2018-01},
	langid = {english},
	keywords = {{pCreode}},
	file = {Accepted Version:/Users/guosiyuan/Zotero/storage/N5REU52T/Herring et al. - 2018 - Unsupervised Trajectory Analysis of Single-Cell RN.pdf:application/pdf}
}

@article{marco_bifurcation_2014,
	title = {Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape},
	volume = {111},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1408993111},
	doi = {10.1073/pnas.1408993111},
	pages = {E5643--E5650},
	number = {52},
	journaltitle = {Proc Natl Acad Sci {USA}},
	author = {<NAME> Karp, <NAME>. and <NAME> and <NAME> Hart, <NAME>. and Trippa, <NAME> <NAME>},
	urldate = {2019-05-22},
	date = {2014-12-30},
	langid = {english},
	keywords = {{SCUBA}},
	file = {Full Text:/Users/guosiyuan/Zotero/storage/WYTYJXAV/Marco et al. - 2014 - Bifurcation analysis of single-cell gene expressio.pdf:application/pdf}
}

@article{velten_human_2017,
	title = {Human haematopoietic stem cell lineage commitment is a continuous process},
	volume = {19},
	issn = {1465-7392, 1476-4679},
	url = {http://www.nature.com/articles/ncb3493},
	doi = {10.1038/ncb3493},
	pages = {271--281},
	number = {4},
	journaltitle = {Nat Cell Biol},
	author = {<NAME>, <NAME>. and Raffel, <NAME>, Sandra and Islam, Saiful and Hennig, <NAME>. and Hirche, Christoph and Lutz, Christoph and Buss, <NAME>. and Nowak, Daniel and Boch, <NAME>, <NAME>. and Huber, <NAME>, <NAME>, <NAME>. and Steinmetz, <NAME>.},
	urldate = {2019-05-22},
	date = {2017-04},
	langid = {english},
	keywords = {{STEMNET}, ref9},
	file = {Accepted Version:/Users/guosiyuan/Zotero/storage/CTAZG4JS/Velten et al. - 2017 - Human haematopoietic stem cell lineage commitment .pdf:application/pdf}
}

@article{herman_fateid_2018,
	title = {{FateID} infers cell fate bias in multipotent progenitors from single-cell {RNA}-seq data},
	volume = {15},
	issn = {1548-7091, 1548-7105},
	url = {http://www.nature.com/articles/nmeth.4662},
	doi = {10.1038/nmeth.4662},
	pages = {379--386},
	number = {5},
	journaltitle = {Nat Methods},
	author = {<NAME> and {Sagar} and <NAME>},
	urldate = {2019-05-22},
	date = {2018-05},
	langid = {english},
	keywords = {{FateID}}
}

@article{campbell_probabilistic_2017,
	title = {Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers},
	volume = {2},
	issn = {2398-502X},
	url = {https://wellcomeopenresearch.org/articles/2-19/v1},
	doi = {10.12688/wellcomeopenres.11087.1},
	pages = {19},
	journaltitle = {Wellcome Open Res},
	author = {<NAME> and Yau, Christopher},
	urldate = {2019-05-22},
	date = {2017-03-15},
	langid = {english},
	keywords = {{MFA}}
}

@article{haghverdi_diffusion_2016,
	title = {Diffusion pseudotime robustly reconstructs lineage branching},
	volume = {13},
	issn = {1548-7091, 1548-7105},
	url = {http://www.nature.com/articles/nmeth.3971},
	doi = {10.1038/nmeth.3971},
	pages = {845--848},
	number = {10},
	journaltitle = {Nat Methods},
	author = {<NAME> Büttner, <NAME> <NAME> Buettner, <NAME>, <NAME>},
	urldate = {2019-05-22},
	date = {2016-10},
	langid = {english},
	keywords = {{DPT}, 胚胎发育}
}

@article{bendall_single-cell_2014,
	title = {Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development},
	volume = {157},
	issn = {00928674},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0092867414004711},
	doi = {10.1016/j.cell.2014.04.005},
	pages = {714--725},
	number = {3},
	journaltitle = {Cell},
	author = {<NAME>, <NAME>. and <NAME> Shenfeld, <NAME>. and Nolan, <NAME>. and <NAME>},
	urldate = {2019-05-22},
	date = {2014-04},
	langid = {english},
	keywords = {Wishbone, B-cell}
}

@report{cannoodt_scorpius_2016,
	title = {{SCORPIUS} improves trajectory inference and identifies novel modules in dendritic cell development},
	url = {http://biorxiv.org/lookup/doi/10.1101/079509},
	abstract = {Recent advances in {RNA} sequencing enable the generation of genome-wide expression data at the single-cell level, opening up new avenues for transcriptomics and systems biology. A new application of single-cell whole-transcriptomics is the unbiased ordering of cells according to their progression along a dynamic process of interest. We introduce {SCORPIUS}, a method which can effectively reconstruct an ordering of individual cells without any prior information about the dynamic process. Comprehensive evaluation using ten {scRNA}-seq datasets shows that {SCORPIUS} consistently outperforms state-of-the-art techniques. We used {SCORPIUS} to generate novel hypotheses regarding dendritic cell development, which were subsequently validated in vivo. This work enables data-driven investigation and characterization of dynamic processes and lays the foundation for objective benchmarking of future trajectory inference methods.},
	institution = {Bioinformatics},
	type = {preprint},
	author = {<NAME> <NAME> <NAME> <NAME>, <NAME> <NAME>, <NAME>},
	urldate = {2019-05-22},
	date = {2016-10-06},
	langid = {english},
	doi = {10.1101/079509},
	keywords = {{SCORPIUS}}
}

@report{campbell_laplacian_2015,
	title = {Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell {RNA}-seq profiles},
	url = {http://biorxiv.org/lookup/doi/10.1101/027219},
	abstract = {Advances in {RNA}-seq technologies provide unprecedented insight into the variability and heterogeneity of gene expression at the single-cell level. However, such data offers only a snapshot of the transcriptome, whereas it is often the progression of cells through dynamic biological processes that is of interest. As a result, one outstanding challenge is to infer such progressions by ordering gene expression from single cell data alone, known as the cell ordering problem. Here, we introduce a new method that constructs a low-dimensional non-linear embedding of the data using laplacian eigenmaps before assigning each cell a pseudotime using principal curves. We characterise why on a theoretical level our method is more robust to the high levels of noise typical of single-cell {RNA}-seq data before demonstrating its utility on two existing datasets of differentiating cells.},
	institution = {Bioinformatics},
	type = {preprint},
	author = {<NAME>, <NAME> and Webber, Caleb},
	urldate = {2019-05-22},
	date = {2015-09-18},
	langid = {english},
	doi = {10.1101/027219},
	keywords = {Embeddr}
}

@article{welch_matcher:_2017,
	title = {{MATCHER}: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics},
	volume = {18},
	issn = {1474-760X},
	url = {http://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1269-0},
	doi = {10.1186/s13059-017-1269-0},
	shorttitle = {{MATCHER}},
	pages = {138},
	number = {1},
	journaltitle = {Genome Biol},
	author = {Welch, <NAME>. and Hartemink, <NAME>. and Prins, <NAME>.},
	urldate = {2019-05-22},
	date = {2017-12},
	langid = {english},
	keywords = {{MATCHER}}
}

@article{ji_tscan:_2016,
	title = {{TSCAN}: Pseudo-time reconstruction and evaluation in single-cell {RNA}-seq analysis},
	volume = {44},
	issn = {0305-1048, 1362-4962},
	url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw430},
	doi = {10.1093/nar/gkw430},
	shorttitle = {{TSCAN}},
	pages = {e117--e117},
	number = {13},
	journaltitle = {Nucleic Acids Res},
	author = {<NAME> and <NAME>},
	urldate = {2019-05-22},
	date = {2016-07-27},
	langid = {english},
	keywords = {{TSCAN}}
}

@article{hummerich_improvement_1975,
	title = {Improvement of renin determination in human plasma using a commonly available renin standard in a radioimmunological method},
	volume = {53},
	issn = {0023-2173},
	abstract = {A new method for the measurement of renin in human plasma is described. The method is based on the introduction of the internationally available renin standard of the Medical Research Council ({MRC}) London, as a calibration system. Thus, some principal disadvantages of methods expressing results in renin reaction velocity (angiotensin generation rate) only are avoided. Both renins, unknown and standard, react with a sheep substrate preparation and are handled identically throughout the whole procedure including the angiotensin I radioimmunoassay ({RIA}). The plasma renin concentration ({PRC}) is given in 10(-6) {MRC}-renin units ({muM}/ml).
{RESULTS}: the renin standard is free of angiotensin, angiotensinases, and angiotensinogen; it is stable on storage. Identical enzyme kinetics are shown for both renins. An interference between endogenous and exogenous substrate could be avoided. The potentially harmful influences of proteins from the enzyme incubation mixture of the {RIA} dose response curve are shown. The use of an angiotensin I calibration system could be omitted. Using a standard renin dilution from 250-0.9 {muU}/ml also the full biological range is covered. When giving an unrestricted diet the preliminary normal values of {PRC} are 21.9 +/- 12.6 {muU}/ml in recumbent and 40.1 +/- 19.8 {muU}/ml in upright position (n = 16,x +/- s, age 20-35 years). Earlier findings of age-dependency of {PRC} were confirmed.},
	pages = {559--569},
	number = {12},
	journaltitle = {Klin. Wochenschr.},
	author = {<NAME>. and <NAME>.},
	date = {1975-06-15},
	pmid = {541},
	keywords = {Adult, Angiotensinogen, Humans, Hydrogen-Ion Concentration, Methods, Preservation, Biological, Radioimmunoassay, Renin}
}

@article{tanay_scaling_2017,
	title = {Scaling single-cell genomics from phenomenology to mechanism},
	volume = {541},
	issn = {0028-0836, 1476-4687},
	url = {http://www.nature.com/articles/nature21350},
	doi = {10.1038/nature21350},
	pages = {331--338},
	number = {7637},
	journaltitle = {Nature},
	author = {<NAME> and <NAME>},
	urldate = {2019-05-22},
	date = {2017-01},
	langid = {english},
	keywords = {ref1}
}

@article{etzrodt_quantitative_2014,
	title = {Quantitative Single-Cell Approaches to Stem Cell Research},
	volume = {15},
	issn = {19345909},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1934590914004664},
	doi = {10.1016/j.stem.2014.10.015},
	pages = {546--558},
	number = {5},
	journaltitle = {Cell Stem Cell},
	author = {Etzrodt, <NAME> Endele, Max and Schroeder, Timm},
	urldate = {2019-05-22},
	date = {2014-11},
	langid = {english},
	keywords = {ref2}
}

@article{trapnell_defining_2015,
	title = {Defining cell types and states with single-cell genomics},
	volume = {25},
	issn = {1088-9051, 1549-5469},
	url = {http://genome.cshlp.org/lookup/doi/10.1101/gr.190595.115},
	doi = {10.1101/gr.190595.115},
	pages = {1491--1498},
	number = {10},
	journaltitle = {Genome Res.},
	author = {<NAME>},
	urldate = {2019-05-22},
	date = {2015-10},
	langid = {english},
	keywords = {ref3},
	file = {Full Text:/Users/guosiyuan/Zotero/storage/QZEDEZSC/Trapnell - 2015 - Defining cell types and states with single-cell ge.pdf:application/pdf}
}

@article{cannoodt_computational_2016,
	title = {Computational methods for trajectory inference from single-cell transcriptomics},
	volume = {46},
	issn = {00142980},
	url = {http://doi.wiley.com/10.1002/eji.201646347},
	doi = {10.1002/eji.201646347},
	pages = {2496--2506},
	number = {11},
	journaltitle = {Eur. J. Immunol.},
	author = {Cannoodt, Robrecht and Saelens, Wouter and Saeys, Yvan},
	urldate = {2019-05-22},
	date = {2016-11},
	langid = {english},
	keywords = {ref4}
}

@article{moon_manifold_2018,
	title = {Manifold learning-based methods for analyzing single-cell {RNA}-sequencing data},
	volume = {7},
	issn = {24523100},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2452310017301877},
	doi = {10.1016/j.coisb.2017.12.008},
	pages = {36--46},
	journaltitle = {Current Opinion in Systems Biology},
	author = {Moon, <NAME>. and Stanley, <NAME>. and Burkhardt, Daniel and <NAME>, David and Wolf, Guy and Krishnaswamy, Smita},
	urldate = {2019-05-22},
	date = {2018-02},
	langid = {english},
	keywords = {ref5}
}

@article{liu_reconstructing_2017,
	title = {Reconstructing cell cycle pseudo time-series via single-cell transcriptome data},
	volume = {8},
	issn = {2041-1723},
	url = {http://www.nature.com/articles/s41467-017-00039-z},
	doi = {10.1038/s41467-017-00039-z},
	pages = {22},
	number = {1},
	journaltitle = {Nat Commun},
	author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and Aparicio, <NAME>. and <NAME>. and <NAME> and <NAME>},
	urldate = {2019-05-22},
	date = {2017-12},
	langid = {english},
	keywords = {ref6},
	file = {Full Text:/Users/guosiyuan/Zotero/storage/6CTUNXH2/Liu et al. - 2017 - Reconstructing cell cycle pseudo time-series via s.pdf:application/pdf}
}

@article{wolf_paga:_2019,
	title = {{PAGA}: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells},
	volume = {20},
	issn = {1474-760X},
	url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1663-x},
	doi = {10.1186/s13059-019-1663-x},
	shorttitle = {{PAGA}},
	pages = {59},
	number = {1},
	journaltitle = {Genome Biol},
	author = {<NAME> and Hamey, <NAME>. and <NAME> and <NAME> and Dahlin, <NAME>. and Göttgens, Berthold and Rajewsky, Nikolaus and <NAME> and Theis, <NAME>.},
	urldate = {2019-05-22},
	date = {2019-12},
	langid = {english},
	keywords = {{PAGA}, {PAGA} Tree, ref7}
}

@article{schlitzer_identification_2015,
	title = {Identification of {cDC}1- and {cDC}2-committed {DC} progenitors reveals early lineage priming at the common {DC} progenitor stage in the bone marrow},
	volume = {16},
	issn = {1529-2908, 1529-2916},
	url = {http://www.nature.com/articles/ni.3200},
	doi = {10.1038/ni.3200},
	pages = {718--728},
	number = {7},
	journaltitle = {Nat Immunol},
	author = {Schlitzer, <NAME> Sivakamasundari, <NAME> <NAME> <NAME>, <NAME>, <NAME>, <NAME> <NAME>, Florent},
	urldate = {2019-05-22},
	date = {2015-07},
	langid = {english},
	keywords = {ref8}
}

@article{see_mapping_2017,
	title = {Mapping the human {DC} lineage through the integration of high-dimensional techniques},
	volume = {356},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aag3009},
	doi = {10.1126/science.aag3009},
	pages = {eaag3009},
	number = {6342},
	journaltitle = {Science},
	author = {<NAME>, Charles-Antoine and <NAME> and <NAME> and {McGovern}, Naomi and Irac, <NAME> and Gunawan, Merry and Beyer, Marc and Händler, Kristian and Duan, Kaibo and Sumatoh, <NAME> and Ruffin, Nicolas and Jouve, Mabel and Gea-Mallorquí, Ester and Hennekam, <NAME>. and Lim, Tony and Yip, <NAME> and <NAME> and <NAME> and <NAME> and Shadan, <NAME> and Fen, <NAME>, <NAME>, <NAME>, Michael and Chan, <NAME>. and <NAME> and Rénia, Laurent and <NAME> and Benaroch, <NAME>, <NAME>, <NAME>, <NAME>, Florent},
	urldate = {2019-05-22},
	date = {2017-06-09},
	langid = {english},
	keywords = {ref10}
}

@article{noauthor_single-cell_2017,
	title = {Single-cell biology},
	volume = {547},
	issn = {0028-0836, 1476-4687},
	url = {http://www.nature.com/articles/547019a},
	doi = {10.1038/547019a},
	pages = {19--19},
	number = {7661},
	journaltitle = {Nature},
	urldate = {2019-05-22},
	date = {2017-07},
	langid = {english}
}

@article{zahn_fantastic_2017,
	title = {A Fantastic Voyage in Genomics},
	volume = {358},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/lookup/doi/10.1126/science.358.6359.56},
	doi = {10.1126/science.358.6359.56},
	pages = {56--57},
	number = {6359},
	journaltitle = {Science},
	author = {Zahn, <NAME>.},
	urldate = {2019-05-22},
	date = {2017-10-06},
	langid = {english}
}

@article{nowogrodzki_how_2017,
	title = {How to build a human cell atlas},
	volume = {547},
	issn = {0028-0836, 1476-4687},
	url = {http://www.nature.com/articles/547024a},
	doi = {10.1038/547024a},
	pages = {24--26},
	number = {7661},
	journaltitle = {Nature},
	author = {<NAME>},
	urldate = {2019-05-22},
	date = {2017-07},
	langid = {english}
}

@article{regev_human_2017,
	title = {The Human Cell Atlas},
	volume = {6},
	issn = {2050-084X},
	url = {https://elifesciences.org/articles/27041},
	doi = {10.7554/eLife.27041},
	abstract = {The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.},
	pages = {e27041},
	journaltitle = {{eLife}},
	author = {Regev, <NAME> Teichmann, <NAME> and Lander, <NAME> and Amit, <NAME>, Christophe and Birney, Ewan and Bodenmiller, Bernd and Campbell, Peter and Carninci, Piero and Clatworthy, Menna and Clevers, Hans and Deplancke, Bart and Dunham, Ian and Eberwine, James and Eils, Roland and Enard, Wolfgang and Farmer, Andrew and Fugger, Lars and Göttgens, Berthold and Hacohen, Nir and Haniffa, Muzlifah and Hemberg, Martin and Kim, Seung and Klenerman, Paul and Kriegstein, Arnold and Lein, Ed and Linnarsson, Sten and Lundberg, Emma and Lundeberg, Joakim and Majumder, Partha and Marioni, <NAME> and Merad, Miriam and Mhlanga, Musa and Nawijn, Martijn and Netea, Mihai and Nolan, Garry and Pe'er, Dana and Phillipakis, Anthony and Ponting, <NAME> and <NAME> and Reik, Wolf and Rozenblatt-Rosen, Orit and Sanes, Joshua and Satija, Rahul and Schumacher, <NAME> and Shalek, Alex and Shapiro, Ehud and Sharma, Padmanee and <NAME> and Stegle, Oliver and Stratton, Michael and Stubbington, <NAME> and Theis, <NAME> and Uhlen, Matthias and <NAME>, Alexander and Wagner, Allon and <NAME> and Weissman, Jonathan and Wold, Barbara and <NAME> and <NAME> and {Human Cell Atlas Meeting Participants}},
	urldate = {2019-05-22},
	date = {2017-12-05},
	langid = {english}
}

@article{perkel_single-cell_2017,
	title = {Single-cell sequencing made simple},
	volume = {547},
	issn = {0028-0836, 1476-4687},
	url = {http://www.nature.com/articles/547125a},
	doi = {10.1038/547125a},
	pages = {125--126},
	number = {7661},
	journaltitle = {Nature},
	author = {Perkel, <NAME>.},
	urldate = {2019-05-22},
	date = {2017-07},
	langid = {english}
}

@article{aibar_scenic:_2017,
	title = {{SCENIC}: single-cell regulatory network inference and clustering},
	volume = {14},
	issn = {1548-7091, 1548-7105},
	url = {http://www.nature.com/articles/nmeth.4463},
	doi = {10.1038/nmeth.4463},
	shorttitle = {{SCENIC}},
	pages = {1083--1086},
	number = {11},
	journaltitle = {Nat Methods},
	author = {<NAME>, <NAME> and <NAME>, <NAME>, Florian and <NAME>, <NAME>},
	urldate = {2019-05-22},
	date = {2017-11},
	langid = {english},
	keywords = {ref11}
}

@article{nestorowa_single-cell_2016,
	title = {A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation},
	volume = {128},
	issn = {0006-4971, 1528-0020},
	url = {http://www.bloodjournal.org/cgi/doi/10.1182/blood-2016-05-716480},
	doi = {10.1182/blood-2016-05-716480},
	pages = {e20--e31},
	number = {8},
	journaltitle = {Blood},
	author = {<NAME> <NAME> <NAME> <NAME> <NAME> <NAME> <NAME>.},
	urldate = {2019-05-23},
	date = {2016-08-25},
	langid = {english},
	keywords = {早期造血细胞谱系}
}

@article{habib_div-seq:_2016,
	title = {Div-Seq: Single-nucleus {RNA}-Seq reveals dynamics of rare adult newborn neurons},
	volume = {353},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aad7038},
	doi = {10.1126/science.aad7038},
	shorttitle = {Div-Seq},
	pages = {925--928},
	number = {6302},
	journaltitle = {Science},
	author = {<NAME> Li, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, Aviv},
	urldate = {2019-05-23},
	date = {2016-08-26},
	langid = {english},
	keywords = {神经发生},
	file = {Full Text:/Users/guosiyuan/Zotero/storage/ML9KZJEE/Habib et al. - 2016 - Div-Seq Single-nucleus RNA-Seq reveals dynamics o.pdf:application/pdf}
}

@article{shin_single-cell_2015,
	title = {Single-Cell {RNA}-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis},
	volume = {17},
	issn = {19345909},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1934590915003124},
	doi = {10.1016/j.stem.2015.07.013},
	pages = {360--372},
	number = {3},
	journaltitle = {Cell Stem Cell},
	author = {<NAME> and <NAME> A. and <NAME> and Shin, Joseph Y. and <NAME> and <NAME> A. and <NAME> and Nauen, David W. and <NAME> M. and <NAME> and <NAME>},
	urldate = {2019-05-23},
	date = {2015-09},
	langid = {english},
	keywords = {神经发生}
}

@article{chen_mpath_2016,
	title = {Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development},
	volume = {7},
	issn = {2041-1723},
	url = {http://www.nature.com/articles/ncomms11988},
	doi = {10.1038/ncomms11988},
	pages = {11988},
	number = {1},
	journaltitle = {Nat Commun},
	author = {<NAME> Schlitzer, <NAME> Chakarov, <NAME> Ginhoux, <NAME>, Michael},
	urldate = {2019-05-23},
	date = {2016-09},
	langid = {english},
	keywords = {神经发生, 树突细胞（{cDC}）祖细胞}
}

@article{haghverdi_diffusion_2015,
	title = {Diffusion maps for high-dimensional single-cell analysis of differentiation data},
	volume = {31},
	issn = {1367-4803, 1460-2059},
	url = {https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btv325},
	doi = {10.1093/bioinformatics/btv325},
	pages = {2989--2998},
	number = {18},
	journaltitle = {Bioinformatics},
	author = {<NAME> and Buettner, Florian and Theis, <NAME>.},
	urldate = {2019-05-23},
	date = {2015-09-15},
	langid = {english},
	keywords = {胚胎发育}
}

@article{olsson_single-cell_2016,
	title = {Single-cell analysis of mixed-lineage states leading to a binary cell fate choice},
	volume = {537},
	issn = {0028-0836, 1476-4687},
	url = {http://www.nature.com/articles/nature19348},
	doi = {10.1038/nature19348},
	pages = {698--702},
	number = {7622},
	journaltitle = {Nature},
	author = {<NAME> and Venkatasubramanian, Meenakshi and Chaudhri, <NAME>. and Aronow, <NAME>. and Salomonis, Nathan and Singh, Harinder and <NAME>},
	urldate = {2019-05-23},
	date = {2016-09},
	langid = {english},
	keywords = {骨髓细胞}
}

@article{paul_transcriptional_2015,
	title = {Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors},
	volume = {163},
	issn = {1097-4172},
	doi = {10.1016/j.cell.2015.11.013},
	abstract = {Within the bone marrow, stem cells differentiate and give rise to diverse blood cell types and functions. Currently, hematopoietic progenitors are defined using surface markers combined with functional assays that are not directly linked with in vivo differentiation potential or gene regulatory mechanisms. Here, we comprehensively map myeloid progenitor subpopulations by transcriptional sorting of single cells from the bone marrow. We describe multiple progenitor subgroups, showing unexpected transcriptional priming toward seven differentiation fates but no progenitors with a mixed state. Transcriptional differentiation is correlated with combinations of known and previously undefined transcription factors, suggesting that the process is tightly regulated. Histone maps and knockout assays are consistent with early transcriptional priming, while traditional transplantation experiments suggest that in vivo priming may still allow for plasticity given strong perturbations. These data establish a reference model and general framework for studying hematopoiesis at single-cell resolution.},
	pages = {1663--1677},
	number = {7},
	journaltitle = {Cell},
	author = {<NAME>, <NAME>, <NAME>, <NAME> and Kenigsberg, Ephraim and Keren-Shaul, Hadas and <NAME> and Lara-Astiaso, David and Gury, Meital and Weiner, Assaf and <NAME> and Cohen, Nadav and Lauridsen, <NAME> and <NAME> and Schlitzer, Andreas and Mildner, Alexander and Ginhoux, Florent and Jung, Steffen and Trumpp, Andreas and Porse, <NAME> and Tanay, <NAME> <NAME>},
	date = {2015-12-17},
	pmid = {26627738},
	keywords = {骨髓细胞, Animals, Bone Marrow Transplantation, {CCAAT}-Enhancer-Binding Proteins, Gene Knockout Techniques, Hematopoiesis, High-Throughput Nucleotide Sequencing, Mice, Mice, Inbred C57BL, Myeloid Progenitor Cells, Sequence Analysis, {RNA}, Single-Cell Analysis, Transcription Factors, Transcriptome}
}

@article{ferrell_bistability_2012,
	title = {Bistability, bifurcations, and Waddington's epigenetic landscape},
	volume = {22},
	issn = {1879-0445},
	doi = {10.1016/j.cub.2012.03.045},
	abstract = {Waddington's epigenetic landscape is probably the most famous and most powerful metaphor in developmental biology. Cells, represented by balls, roll downhill through a landscape of bifurcating valleys. Each new valley represents a possible cell fate and the ridges between the valleys maintain the cell fate once it has been chosen. Here I examine models of two important developmental processes - cell-fate induction and lateral inhibition - and ask whether the landscapes for these models at least qualitatively resemble Waddington's picture. For cell-fate induction, the answer is no. The commitment of a cell to a new fate corresponds to the disappearance of a valley from the landscape, not the splitting of one valley into two, and it occurs through a type of bifurcation - a saddle-node bifurcation - that possesses an intrinsic irreversibility that is missing from Waddington's picture. Lateral inhibition, a symmetrical cell-cell competition process, corresponds better to Waddington's picture, with one valley reversibly splitting into two through a pitchfork bifurcation. I propose an alternative epigenetic landscape that has numerous valleys and ridges right from the start, with the process of cell-fate commitment corresponding to the irreversible disappearance of some of these valleys and ridges, via cell-fate induction, complemented by the creation of new valleys and ridges through processes like cell-cell competition.},
	pages = {R458--466},
	number = {11},
	journaltitle = {Curr. Biol.},
	author = {Ferrell, <NAME>.},
	date = {2012-06-05},
	pmid = {22677291},
	pmcid = {PMC3372930},
	keywords = {Animals, Cell Differentiation, Developmental Biology, Epigenesis, Genetic, Models, Biological, 细胞路径},
	file = {Accepted Version:/Users/guosiyuan/Zotero/storage/5EJBVIS3/Ferrell - 2012 - Bistability, bifurcations, and Waddington's epigen.pdf:application/pdf}
}

@report{wolf_graph_2017,
	title = {Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells},
	url = {http://biorxiv.org/lookup/doi/10.1101/208819},
	abstract = {Single-cell {RNA}-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction ({PAGA}) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions (https://github.com/theislab/paga). {PAGA} maps provide interpretable discrete and continuous latent coordinates for both disconnected and continuous structure in data, preserve the global topology of data, allow analyzing data at different resolutions and result in much higher computational efficiency of the typical exploratory data analysis workflow -- one million cells take on the order of a minute, a speedup of 130 times compared to {UMAP}. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, confirm the reconstruction of lineage relations of adult planaria and the zebrafish embryo, benchmark computational performance on a neuronal dataset and detect a  biological trajectory in one deep-learning processed image dataset.},
	institution = {Bioinformatics},
	type = {preprint},
	author = {<NAME> <NAME>. and <NAME> and <NAME>.},
	urldate = {2019-05-23},
	date = {2017-10-25},
	langid = {english},
	doi = {10.1101/208819},
	keywords = {{PAGA}, {PAGA} Tree},
	file = {Full Text:/Users/guosiyuan/Zotero/storage/Z4DJFM4G/Wolf et al. - 2017 - Graph abstraction reconciles clustering with traje.pdf:application/pdf}
}

@report{saelens_comparison_2018,
	title = {A comparison of single-cell trajectory inference methods: towards more accurate and robust tools},
	url = {http://biorxiv.org/lookup/doi/10.1101/276907},
	shorttitle = {A comparison of single-cell trajectory inference methods},
	abstract = {Using single-cell -omics data, it is now possible to computationally order cells along trajectories, allowing the unbiased study of cellular dynamic processes. Since 2014, more than 50 trajectory inference methods have been developed, each with its own set of methodological characteristics. As a result, choosing a method to infer trajectories is often challenging, since a comprehensive assessment of the performance and robustness of each method is still lacking. In order to facilitate the comparison of the results of these methods to each other and to a gold standard, we developed a global framework to benchmark trajectory inference tools. Using this framework, we compared the trajectories from a total of 29 trajectory inference methods, on a large collection of real and synthetic datasets. We evaluate methods using several metrics, including accuracy of the inferred ordering, correctness of the network topology, code quality and user friendliness. We found that some methods, including Slingshot, {TSCAN} and Monocle {DDRTree}, clearly outperform other methods, although their performance depended on the type of trajectory present in the data. Based on our benchmarking results, we therefore developed a set of guidelines for method users. However, our analysis also indicated that there is still a lot of room for improvement, especially for methods detecting complex trajectory topologies. Our evaluation pipeline can therefore be used to spearhead the development of new scalable and more accurate methods, and is available at github.com/dynverse/dynverse. To our knowledge, this is the first comprehensive assessment of trajectory inference methods. For now, we exclusively evaluated the methods on their default parameters, but plan to add a detailed parameter tuning procedure in the future. We gladly welcome any discussion and feedback on key decisions made as part of this study, including the metrics used in the benchmark, the quality control checklist, and the implementation of the method wrappers. These discussions can be held at github.com/dynverse/dynverse/issues.},
	institution = {Bioinformatics},
	type = {preprint},
	author = {<NAME> Cannoodt, <NAME> Todorov, <NAME> Saeys, Yvan},
	urldate = {2019-05-23},
	date = {2018-03-05},
	langid = {english},
	doi = {10.1101/276907},
	keywords = {lineage review}
}

@article{saelens_comparison_2019,
	title = {A comparison of single-cell trajectory inference methods},
	volume = {37},
	issn = {1087-0156, 1546-1696},
	url = {http://www.nature.com/articles/s41587-019-0071-9},
	doi = {10.1038/s41587-019-0071-9},
	pages = {547--554},
	number = {5},
	journaltitle = {Nat Biotechnol},
	author = {<NAME> and <NAME> and Todorov, Helena and Saeys, Yvan},
	urldate = {2019-05-23},
	date = {2019-05},
	langid = {english},
	keywords = {lineage review}
}

@article{wagner_single-cell_2018,
	title = {Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo},
	volume = {360},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aar4362},
	doi = {10.1126/science.aar4362},
	abstract = {High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations of vertebrate development and disease. Here we applied single-cell {RNA} sequencing to {\textgreater}92,000 cells from zebrafish embryos during the first day of development. Using a graph-based approach, we mapped a cell-state landscape that describes axis patterning, germ layer formation, and organogenesis. We tested how clonally related cells traverse this landscape by developing a transposon-based barcoding approach ({TracerSeq}) for reconstructing single-cell lineage histories. Clonally related cells were often restricted by the state landscape, including a case in which two independent lineages converge on similar fates. Cell fates remained restricted to this landscape in embryos lacking the
              chordin
              gene. We provide web-based resources for further analysis of the single-cell data.},
	pages = {981--987},
	number = {6392},
	journaltitle = {Science},
	author = {Wagner, <NAME>. and Weinreb, Caleb and Collins, <NAME>. and Briggs, <NAME>. and Megason, <NAME>. and Klein, <NAME>.},
	urldate = {2019-05-23},
	date = {2018-06-01},
	langid = {english},
	keywords = {胚胎发育, new1}
}

@article{briggs_dynamics_2018,
	title = {The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution},
	volume = {360},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aar5780},
	doi = {10.1126/science.aar5780},
	abstract = {Time series of single-cell transcriptome measurements can reveal dynamic features of cell differentiation pathways. From measurements of whole frog embryos spanning zygotic genome activation through early organogenesis, we derived a detailed catalog of cell states in vertebrate development and a map of differentiation across all lineages over time. The inferred map recapitulates most if not all developmental relationships and associates new regulators and marker genes with each cell state. We find that many embryonic cell states appear earlier than previously appreciated. We also assess conflicting models of neural crest development. Incorporating a matched time series of zebrafish development from a companion paper, we reveal conserved and divergent features of vertebrate early developmental gene expression programs.},
	pages = {eaar5780},
	number = {6392},
	journaltitle = {Science},
	author = {Briggs, <NAME>. and Weinreb, <NAME> Wagner, <NAME>. and Megason, <NAME>, <NAME> Kirschner, <NAME>. and Klein, <NAME>.},
	urldate = {2019-05-23},
	date = {2018-06-01},
	langid = {english},
	keywords = {胚胎发育, new2}
}

@article{farrell_single-cell_2018,
	title = {Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis},
	volume = {360},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aar3131},
	doi = {10.1126/science.aar3131},
	abstract = {During embryogenesis, cells acquire distinct fates by transitioning through transcriptional states. To uncover these transcriptional trajectories during zebrafish embryogenesis, we sequenced 38,731 cells and developed {URD}, a simulated diffusion-based computational reconstruction method. {URD} identified the trajectories of 25 cell types through early somitogenesis, gene expression along them, and their spatial origin in the blastula. Analysis of Nodal signaling mutants revealed that their transcriptomes were canalized into a subset of wild-type transcriptional trajectories. Some wild-type developmental branch points contained cells that express genes characteristic of multiple fates. These cells appeared to trans-specify from one fate to another. These findings reconstruct the transcriptional trajectories of a vertebrate embryo, highlight the concurrent canalization and plasticity of embryonic specification, and provide a framework with which to reconstruct complex developmental trees from single-cell transcriptomes.},
	pages = {eaar3131},
	number = {6392},
	journaltitle = {Science},
	author = {Farrell, <NAME>, <NAME>, Aviv and Schier, <NAME>.},
	urldate = {2019-05-23},
	date = {2018-06-01},
	langid = {english},
	keywords = {胚胎发育, new3}
}

@article{fincher_cell_2018,
	title = {Cell type transcriptome atlas for the planarian \textit{Schmidtea mediterranea}},
	volume = {360},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aaq1736},
	doi = {10.1126/science.aaq1736},
	pages = {eaaq1736},
	number = {6391},
	journaltitle = {Science},
	author = {Fincher, <NAME>. and <NAME>, <NAME> Kravarik, <NAME> Reddien, <NAME>.},
	urldate = {2019-05-23},
	date = {2018-05-25},
	langid = {english},
	keywords = {地中海圆头涡虫, new4}
}

@article{plass_cell_2018,
	title = {Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics},
	volume = {360},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/lookup/doi/10.1126/science.aaq1723},
	doi = {10.1126/science.aaq1723},
	pages = {eaaq1723},
	number = {6391},
	journaltitle = {Science},
	author = {<NAME> and <NAME> and Misios, Aristotelis and <NAME> and Theis, <NAME>. and Kocks, Christine and Rajewsky, Nikolaus},
	urldate = {2019-05-23},
	date = {2018-05-25},
	langid = {english},
	keywords = {new5}
}

@article{henry_omictools:_2014,
	title = {{OMICtools}: an informative directory for multi-omic data analysis},
	volume = {2014},
	issn = {1758-0463},
	url = {https://academic.oup.com/database/article-lookup/doi/10.1093/database/bau069},
	doi = {10.1093/database/bau069},
	shorttitle = {{OMICtools}},
	pages = {bau069--bau069},
	number = {0},
	journaltitle = {Database},
	author = {<NAME>. and <NAME>. and <NAME>. and <NAME>.},
	urldate = {2019-05-24},
	date = {2014-07-14},
	langid = {english},
	keywords = {Omictools.org},
	file = {Full Text:/Users/guosiyuan/Zotero/storage/CDUKSLZH/Henry et al. - 2014 - OMICtools an informative directory for multi-omic.pdf:application/pdf}
}

@software{davis_seandavi/awesome-single-cell:_2018,
	title = {Seandavi/Awesome-Single-Cell: 2018-06-20-1},
	rights = {Open Access},
	url = {https://zenodo.org/record/1117762},
	shorttitle = {Seandavi/Awesome-Single-Cell},
	abstract = {List of software packages for single-cell data analysis, including {RNA}-seq, {ATAC}-seq, etc.},
	version = {2018-06-20-1},
	publisher = {Zenodo},
	author = {<NAME> <NAME> <NAME> <NAME> and <NAME> and <NAME> and {MikeDMorgan} and Markrobinsonuzh and <NAME>, Nathan and Dyl4nm4rsh4ll and <NAME> and {YAN}, Yun and Petukhov, Viktor and Wolfgruber, Thomas and Aboukhalil, Robert and Roelli, Patrick and Kelly, <NAME>, John and {DeTomaso}, David and {AlessandraDM} and <NAME>},
	urldate = {2019-05-24},
	date = {2018-06-20},
	doi = {10.5281/zenodo.1117762},
	keywords = {awesome-single-cell}
}

@article{zappia_exploring_2018,
	title = {Exploring the single-cell {RNA}-seq analysis landscape with the {scRNA}-tools database},
	volume = {14},
	issn = {1553-7358},
	url = {https://dx.plos.org/10.1371/journal.pcbi.1006245},
	doi = {10.1371/journal.pcbi.1006245},
	pages = {e1006245},
	number = {6},
	journaltitle = {{PLoS} Comput Biol},
	author = {<NAME> <NAME> <NAME>},
	editor = {<NAME>},
	urldate = {2019-05-24},
	date = {2018-06-25},
	langid = {english},
	keywords = {{scRNA}-tools}
}

@article{levine_data-driven_2015,
	title = {Data-Driven Phenotypic Dissection of {AML} Reveals Progenitor-like Cells that Correlate with Prognosis},
	volume = {162},
	issn = {1097-4172},
	doi = {10.1016/j.cell.2015.05.047},
	abstract = {Acute myeloid leukemia ({AML}) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed {PhenoGraph}, which algorithmically defines phenotypes in high-dimensional single-cell data. {PhenoGraph} revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into {AML} pathophysiology.},
	pages = {184--197},
	number = {1},
	journaltitle = {Cell},
	author = {Levine, <NAME>. and Simonds, <NAME>. and Bendall, <NAME>. and Davis, <NAME>. and Amir, <NAME>. and Tadmor, <NAME>. and <NAME> and Fienberg, <NAME>. and Jager, Astraea and Zunder, <NAME>. and Finck, Rachel and Gedman, <NAME>. and Radtke, Ina and Downing, <NAME>. and Pe'er, Dana and Nolan, <NAME>.},
	date = {2015-07-02},
	pmid = {26095251},
	pmcid = {PMC4508757},
	keywords = {Humans, Single-Cell Analysis, Transcriptome, Bone Marrow, Child, Cohort Studies, Computational Biology, Genetic Heterogeneity, Leukemia, Myeloid, Acute, Neoplastic Stem Cells, 造血系统},
	file = {Accepted Version:/Users/guosiyuan/Zotero/storage/99IPNHY3/Levine et al. - 2015 - Data-Driven Phenotypic Dissection of AML Reveals P.pdf:application/pdf}
}

@article{tirosh_dissecting_2016,
	title = {Dissecting the multicellular ecosystem of metastatic melanoma by single-cell {RNA}-seq},
	volume = {352},
	issn = {1095-9203},
	doi = {10.1126/science.aad0501},
	abstract = {To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell {RNA} sequencing ({RNA}-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the {MITF} transcription factor also contained cells with low {MITF} and elevated levels of the {AXL} kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.},
	pages = {189--196},
	number = {6282},
	journaltitle = {Science},
	author = {<NAME>, <NAME> Prakadan, <NAME> Wadsworth, <NAME>. and <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME> Shah, <NAME>, Diana and Genshaft, <NAME>. and Hughes, <NAME>. and Ziegler, <NAME>. and Kazer, <NAME>. and Gaillard, <NAME>, <NAME>. and Villani, Alexandra-Chloé and Johannessen, <NAME>. and Andreev, <NAME>. and <NAME>, <NAME>. and Bertagnolli, Monica and Sorger, <NAME>. and Sullivan, <NAME>. and Flaherty, <NAME>. and Frederick, <NAME>. and Jané-Valbuena, <NAME>, <NAME>. and Rozenblatt-Rosen, <NAME> Shalek, <NAME>, <NAME>, <NAME>.},
	date = {2016-04-08},
	pmid = {27124452},
	pmcid = {PMC4944528},
	keywords = {Humans, Sequence Analysis, {RNA}, Single-Cell Analysis, Transcriptome, Base Sequence, Cell Communication, Cell Cycle, Drug Resistance, Neoplasm, Endothelial Cells, Genomics, Immunotherapy, Lymphocyte Activation, Melanoma, Microphthalmia-Associated Transcription Factor, Neoplasm Metastasis, {RNA}, Skin Neoplasms, Stromal Cells, T-Lymphocytes, Tumor Microenvironment},
	file = {Full Text:/Users/guosiyuan/Zotero/storage/Q5VHEPA9/Tirosh et al. - 2016 - Dissecting the multicellular ecosystem of metastat.pdf:application/pdf}
}

@article{kiselev_sc3:_2017,
	title = {{SC}3: consensus clustering of single-cell {RNA}-seq data},
	volume = {14},
	issn = {1548-7105},
	doi = {10.1038/nmeth.4236},
	shorttitle = {{SC}3},
	abstract = {Single-cell {RNA}-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering ({SC}3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/{SC}3). We demonstrate that {SC}3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.},
	pages = {483--486},
	number = {5},
	journaltitle = {Nat. Methods},
	author = {<NAME> Kirschner, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>. and <NAME>},
	date = {2017-05},
	pmid = {28346451},
	pmcid = {PMC5410170},
	keywords = {Humans, High-Throughput Nucleotide Sequencing, Sequence Analysis, {RNA}, Single-Cell Analysis, Cluster Analysis, Datasets as Topic, Gene Expression Profiling, Hematopoietic Stem Cells, Support Vector Machine}
}

@article{patel_single-cell_2014,
	title = {Single-cell {RNA}-seq highlights intratumoral heterogeneity in primary glioblastoma},
	volume = {344},
	issn = {1095-9203},
	doi = {10.1126/science.1254257},
	abstract = {Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell {RNA} sequencing ({RNA}-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.},
	pages = {1396--1401},
	number = {6190},
	journaltitle = {Science},
	author = {<NAME>. and Tirosh, Itay and Trombetta, <NAME>. and Shalek, <NAME>. and Gillespie, <NAME>. and <NAME> and Cahill, <NAME>. and Nahed, <NAME>. and Curry, <NAME>. and Martuza, <NAME>. and Louis, <NAME>. and Rozenblatt-Rosen, Orit and Suvà, <NAME>. and Regev, Aviv and Bernstein, <NAME>.},
	date = {2014-06-20},
	pmid = {24925914},
	pmcid = {PMC4123637},
	keywords = {Humans, Sequence Analysis, {RNA}, Single-Cell Analysis, Gene Expression Profiling, Brain Neoplasms, Genetic Variation, Glioblastoma, Prognosis, {RNA}, Messenger}
}

@article{tirosh_single-cell_2016,
	title = {Single-cell {RNA}-seq supports a developmental hierarchy in human oligodendroglioma},
	volume = {539},
	issn = {1476-4687},
	doi = {10.1038/nature20123},
	abstract = {Although human tumours are shaped by the genetic evolution of cancer cells, evidence also suggests that they display hierarchies related to developmental pathways and epigenetic programs in which cancer stem cells ({CSCs}) can drive tumour growth and give rise to differentiated progeny. Yet, unbiased evidence for {CSCs} in solid human malignancies remains elusive. Here we profile 4,347 single cells from six {IDH}1 or {IDH}2 mutant human oligodendrogliomas by {RNA} sequencing ({RNA}-seq) and reconstruct their developmental programs from genome-wide expression signatures. We infer that most cancer cells are differentiated along two specialized glial programs, whereas a rare subpopulation of cells is undifferentiated and associated with a neural stem cell expression program. Cells with expression signatures for proliferation are highly enriched in this rare subpopulation, consistent with a model in which {CSCs} are primarily responsible for fuelling the growth of oligodendroglioma in humans. Analysis of copy number variation ({CNV}) shows that distinct {CNV} sub-clones within tumours display similar cellular hierarchies, suggesting that the architecture of oligodendroglioma is primarily dictated by developmental programs. Subclonal point mutation analysis supports a similar model, although a full phylogenetic tree would be required to definitively determine the effect of genetic evolution on the inferred hierarchies. Our single-cell analyses provide insight into the cellular architecture of oligodendrogliomas at single-cell resolution and support the cancer stem cell model, with substantial implications for disease management.},
	pages = {309--313},
	number = {7628},
	journaltitle = {Nature},
	author = {<NAME>, <NAME>, <NAME>, <NAME>, <NAME>. and <NAME>, <NAME> Rodman, <NAME>, <NAME> Filbin, <NAME>. and Neftel, <NAME>, Niyati and Nyman, Jackson and Izar, <NAME>, <NAME>. and Francis, <NAME>. and Patel, <NAME>. and Onozato, <NAME>. and Riggi, Nicolo and Livak, <NAME>. and Gennert, Dave and Satija, Rahul and Nahed, <NAME>. and Curry, <NAME>. and Martuza, <NAME>. and Mylvaganam, Ravindra and <NAME>, <NAME>. and Golub, <NAME>. and Rivera, <NAME>. and Getz, <NAME>, <NAME>, <NAME>. and Monje, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>.},
	date = {2016},
	pmid = {27806376},
	pmcid = {PMC5465819},
	keywords = {Humans, Sequence Analysis, {RNA}, Single-Cell Analysis, Cell Differentiation, Neoplastic Stem Cells, Cell Proliferation, {DNA} Copy Number Variations, Isocitrate Dehydrogenase, Neural Stem Cells, Neuroglia, Oligodendroglioma, Phylogeny, Point Mutation}
}

@article{diehn_association_2009,
	title = {Association of reactive oxygen species levels and radioresistance in cancer stem cells},
	volume = {458},
	issn = {1476-4687},
	doi = {10.1038/nature07733},
	abstract = {The metabolism of oxygen, although central to life, produces reactive oxygen species ({ROS}) that have been implicated in processes as diverse as cancer, cardiovascular disease and ageing. It has recently been shown that central nervous system stem cells and haematopoietic stem cells and early progenitors contain lower levels of {ROS} than their more mature progeny, and that these differences are critical for maintaining stem cell function. We proposed that epithelial tissue stem cells and their cancer stem cell ({CSC}) counterparts may also share this property. Here we show that normal mammary epithelial stem cells contain lower concentrations of {ROS} than their more mature progeny cells. Notably, subsets of {CSCs} in some human and murine breast tumours contain lower {ROS} levels than corresponding non-tumorigenic cells ({NTCs}). Consistent with {ROS} being critical mediators of ionizing-radiation-induced cell killing, {CSCs} in these tumours develop less {DNA} damage and are preferentially spared after irradiation compared to {NTCs}. Lower {ROS} levels in {CSCs} are associated with increased expression of free radical scavenging systems. Pharmacological depletion of {ROS} scavengers in {CSCs} markedly decreases their clonogenicity and results in radiosensitization. These results indicate that, similar to normal tissue stem cells, subsets of {CSCs} in some tumours contain lower {ROS} levels and enhanced {ROS} defences compared to their non-tumorigenic progeny, which may contribute to tumour radioresistance.},
	pages = {780--783},
	number = {7239},
	journaltitle = {Nature},
	author = {Diehn, <NAME>, <NAME>. and Lobo, <NAME>. and Kalisky, <NAME>, <NAME>, <NAME>, <NAME>, <NAME> <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME> Quake, <NAME>, <NAME>. and Clarke, <NAME>.},
	date = {2009-04-09},
	pmid = {19194462},
	pmcid = {PMC2778612},
	keywords = {Humans, Animals, Mice, Mice, Inbred C57BL, Neoplastic Stem Cells, Breast Neoplasms, Cells, Cultured, {DNA} Damage, Female, Gene Expression, Mammary Glands, Human, Radiation Tolerance, Reactive Oxygen Species},
	file = {Accepted Version:/Users/guosiyuan/Zotero/storage/ZBMRIMGT/Diehn et al. - 2009 - Association of reactive oxygen species levels and .pdf:application/pdf}
}

@article{zellner_[transplantation_1975,
	title = {[Transplantation and preservation of tissue-typized skin in burns]},
	volume = {46},
	issn = {0009-4722},
	abstract = {At the Department of Plastic Surgery and Burns, Berufsgenossenschaftliche Unfalklinik Ludwigshafen-Oggersheim, a skinbank with typed skin has been established. The storage system consists of deep-freezing at -196 degrees C in liquid nitrogen. The transplantation of {HL}-A typed skin was evaluated from the clinical and histological point of view. Due to the effort necessary for such an undertaking this method is critically reviewed.},
	pages = {319--322},
	number = {7},
	journaltitle = {Chirurg},
	author = {<NAME>. and <NAME>. and <NAME>.},
	date = {1975-07},
	pmid = {220},
	keywords = {Humans, Burns, Freezing, Graft vs Host Reaction, Histocompatibility Testing, {HLA} Antigens, Skin Transplantation, Tissue Preservation, Transplantation, Homologous}
}

@article{negrutiu_selection_1978,
	title = {Selection and characterization of cell lines of Arabidopsis thaliana resistant to amino acid analogs [proceedings]},
	volume = {86},
	issn = {0003-9799},
	pages = {442--443},
	number = {2},
	journaltitle = {Arch. Int. Physiol. Biochim.},
	author = {<NAME>. and <NAME> <NAME>.},
	date = {1978-05},
	pmid = {81019},
	keywords = {Cell Line, Cysteine, Drug Resistance, Plants, Tryptophan}
}

@article{wang_clonal_2014,
	title = {Clonal evolution in breast cancer revealed by single nucleus genome sequencing},
	volume = {512},
	issn = {1476-4687},
	doi = {10.1038/nature13600},
	abstract = {Sequencing studies of breast tumour cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumours. Here we developed a whole-genome and exome single cell sequencing approach called nuc-seq that uses G2/M nuclei to achieve 91\% mean coverage breadth. We applied this method to sequence single normal and tumour nuclei from an oestrogen-receptor-positive ({ER}(+)) breast cancer and a triple-negative ductal carcinoma. In parallel, we performed single nuclei copy number profiling. Our data show that aneuploid rearrangements occurred early in tumour evolution and remained highly stable as the tumour masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Using targeted single-molecule sequencing, many of the diverse mutations were shown to occur at low frequencies ({\textless}10\%) in the tumour mass. Using mathematical modelling we found that the triple-negative tumour cells had an increased mutation rate (13.3×), whereas the {ER}(+) tumour cells did not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer.},
	pages = {155--160},
	number = {7513},
	journaltitle = {Nature},
	author = {<NAME>. and Unruh, <NAME> <NAME> <NAME>, <NAME>.},
	date = {2014-08-14},
	pmid = {25079324},
	pmcid = {PMC4158312},
	keywords = {Humans, Single-Cell Analysis, Genetic Variation, Breast Neoplasms, Female, Cell Line, Tumor, Clonal Evolution, {DNA} Fingerprinting, Genome, Models, Theoretical, Mutation, Sequence Analysis, {DNA}, Triple Negative Breast Neoplasms}
}

@article{gawad_dissecting_2014,
	title = {Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics},
	volume = {111},
	issn = {1091-6490},
	doi = {10.1073/pnas.1420822111},
	abstract = {Many cancers have substantial genomic heterogeneity within a given tumor, and to fully understand that diversity requires the ability to perform single cell analysis. We performed targeted sequencing of a panel of single nucleotide variants ({SNVs}), deletions, and {IgH} sequences in 1,479 single tumor cells from six acute lymphoblastic leukemia ({ALL}) patients. By accurately segregating groups of cooccurring mutations into distinct clonal populations, we identified codominant clones in the majority of patients. Evaluation of intraclonal mutation patterns identified clone-specific punctuated cytosine mutagenesis events, showed that most structural variants are acquired before {SNVs}, determined that {KRAS} mutations occur late in disease development but are not sufficient for clonal dominance, and identified clones within the same patient that are arrested at varied stages in B-cell development. Taken together, these data order the sequence of genetic events that underlie childhood {ALL} and provide a framework for understanding the development of the disease at single-cell resolution.},
	pages = {17947--17952},
	number = {50},
	journaltitle = {Proc. Natl. Acad. Sci. U.S.A.},
	author = {<NAME> Koh, <NAME> Quake, <NAME>.},
	date = {2014-12-16},
	pmid = {25425670},
	pmcid = {PMC4273416},
	keywords = {Humans, Single-Cell Analysis, Child, Base Sequence, Genomics, Genetic Variation, Sequence Analysis, {DNA}, acute lymphoblastic leukemia, clonal evolution, Clone Cells, cytosine mutagenesis, {DNA} Mutational Analysis, Exome, intratumor heterogeneity, Microfluidic Analytical Techniques, Molecular Sequence Data, Polymorphism, Single Nucleotide, Precursor Cell Lymphoblastic Leukemia-Lymphoma, single-cell genomics}
}

@article{corces-zimmerman_preleukemic_2014,
	title = {Preleukemic mutations in human acute myeloid leukemia affect epigenetic regulators and persist in remission},
	volume = {111},
	issn = {1091-6490},
	doi = {10.1073/pnas.1324297111},
	abstract = {Cancer is widely characterized by the sequential acquisition of genetic lesions in a single lineage of cells. Our previous studies have shown that, in acute myeloid leukemia ({AML}), mutation acquisition occurs in functionally normal hematopoietic stem cells ({HSCs}). These preleukemic {HSCs} harbor some, but not all, of the mutations found in the leukemic cells. We report here the identification of patterns of mutation acquisition in human {AML}. Our findings support a model in which mutations in "landscaping" genes, involved in global chromatin changes such as {DNA} methylation, histone modification, and chromatin looping, occur early in the evolution of {AML}, whereas mutations in "proliferative" genes occur late. Additionally, we analyze the persistence of preleukemic mutations in patients in remission and find {CD}34+ progenitor cells and various mature cells that harbor preleukemic mutations. These findings indicate that preleukemic {HSCs} can survive induction chemotherapy, identifying these cells as a reservoir for the reevolution of relapsed disease. Finally, through the study of several cases of relapsed {AML}, we demonstrate various evolutionary patterns for the generation of relapsed disease and show that some of these patterns are consistent with involvement of preleukemic {HSCs}. These findings provide key insights into the monitoring of minimal residual disease and the identification of therapeutic targets in human {AML}.},
	pages = {2548--2553},
	number = {7},
	journaltitle = {Proc. Natl. Acad. Sci. U.S.A.},
	author = {<NAME> <NAME> <NAME> <NAME> <NAME>},
	date = {2014-02-18},
	pmid = {24550281},
	pmcid = {PMC3932921},
	keywords = {Humans, Epigenesis, Genetic, Models, Biological, Leukemia, Myeloid, Acute, Base Sequence, Hematopoietic Stem Cells, Clonal Evolution, Mutation, Sequence Analysis, {DNA}, clonal evolution, Exome, Molecular Sequence Data, Antigens, {CD}34, Flow Cytometry, Genotype, preleukemia, Preleukemia}
}

@article{jan_clonal_2012,
	title = {Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia},
	volume = {4},
	issn = {1946-6242},
	doi = {10.1126/scitranslmed.3004315},
	abstract = {Given that most bone marrow cells are short-lived, the accumulation of multiple leukemogenic mutations in a single clonal lineage has been difficult to explain. We propose that serial acquisition of mutations occurs in self-renewing hematopoietic stem cells ({HSCs}). We investigated this model through genomic analysis of {HSCs} from six patients with de novo acute myeloid leukemia ({AML}). Using exome sequencing, we identified mutations present in individual {AML} patients harboring the {FLT}3-{ITD} (internal tandem duplication) mutation. We then screened the residual {HSCs} and detected some of these mutations including mutations in the {NPM}1, {TET}2, and {SMC}1A genes. Finally, through single-cell analysis, we determined that a clonal progression of multiple mutations occurred in the {HSCs} of some {AML} patients. These preleukemic {HSCs} suggest the clonal evolution of {AML} genomes from founder mutations, revealing a potential mechanism contributing to relapse. Such preleukemic {HSCs} may constitute a cellular reservoir that should be targeted therapeutically for more durable remissions.},
	pages = {149ra118},
	number = {149},
	journaltitle = {Sci Transl Med},
	author = {<NAME> Snyder, <NAME>. and Corces-Zimmerman, <NAME> <NAME> <NAME> Quake, <NAME>. and Majeti, Ravindra},
	date = {2012-08-29},
	pmid = {22932223},
	pmcid = {PMC4045621},
	keywords = {Humans, Animals, Mice, Leukemia, Myeloid, Acute, Neoplastic Stem Cells, Hematopoietic Stem Cells, Prognosis, Cell Proliferation, Clonal Evolution, Mutation, {DNA} Mutational Analysis, Exome, Flow Cytometry, Preleukemia, Cell Cycle Proteins, Cell Lineage, Cell Separation, Chromosomal Proteins, Non-Histone, {DNA}-Binding Proteins, fms-Like Tyrosine Kinase 3, Founder Effect, Gene Expression Regulation, Leukemic, Genetic Markers, Genetic Predisposition to Disease, Hematopoietic Stem Cell Transplantation, Nuclear Proteins, Phenotype, Proto-Oncogene Proteins}
}

@article{dalerba_single-cell_2011,
	title = {Single-cell dissection of transcriptional heterogeneity in human colon tumors},
	volume = {29},
	issn = {1546-1696},
	doi = {10.1038/nbt.2038},
	abstract = {Cancer is often viewed as a caricature of normal developmental processes, but the extent to which its cellular heterogeneity truly recapitulates multilineage differentiation processes of normal tissues remains unknown. Here we implement single-cell {PCR} gene-expression analysis to dissect the cellular composition of primary human normal colon and colon cancer epithelia. We show that human colon cancer tissues contain distinct cell populations whose transcriptional identities mirror those of the different cellular lineages of normal colon. By creating monoclonal tumor xenografts from injection of a single (n = 1) cell, we demonstrate that the transcriptional diversity of cancer tissues is largely explained by in vivo multilineage differentiation and not only by clonal genetic heterogeneity. Finally, we show that the different gene-expression programs linked to multilineage differentiation are strongly associated with patient survival. We develop two-gene classifier systems ({KRT}20 versus {CA}1, {MS}4A12, {CD}177, {SLC}26A3) that predict clinical outcomes with hazard ratios superior to those of pathological grade and comparable to those of microarray-derived multigene expression signatures.},
	pages = {1120--1127},
	number = {12},
	journaltitle = {Nat. Biotechnol.},
	author = {<NAME>, <NAME>, Debashis and Rajendran, <NAME>. and Rothenberg, <NAME>. and Leyrat, <NAME>. and <NAME> and Okamoto, Jennifer and Johnston, <NAME>. and <NAME> and <NAME> and <NAME> and Neff, <NAME>. and Wang, Jianbin and Shelton, <NAME>. and Visser, Brendan and Hisamori, Shigeo and Shimono, Yohei and <NAME>, Marc and Clevers, Hans and Clarke, <NAME>. and Quake, <NAME>.},
	date = {2011-11-13},
	pmid = {22081019},
	pmcid = {PMC3237928},
	keywords = {Adult, Humans, Animals, Mice, Single-Cell Analysis, Cell Differentiation, Female, Flow Cytometry, Cell Lineage, Adenocarcinoma, Aged, Aged, 80 and over, Colonic Neoplasms, Disease-Free Survival, Gene Expression Regulation, Neoplastic, {HCT}116 Cells, Kaplan-Meier Estimate, Male, Middle Aged, Neoplasm Staging, Transcription, Genetic, Transplantation, Heterologous, Treatment Outcome}
}

@article{zappia_splatter:_2017,
	title = {Splatter: simulation of single-cell {RNA} sequencing data},
	volume = {18},
	issn = {1474-760X},
	url = {http://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0},
	doi = {10.1186/s13059-017-1305-0},
	shorttitle = {Splatter},
	pages = {174},
	number = {1},
	journaltitle = {Genome Biol},
	author = {<NAME> <NAME> <NAME>},
	urldate = {2019-05-31},
	date = {2017-12},
	langid = {english},
	keywords = {simulation}
}