%% Created using Papers on Wed, 01 Jan 2014.
%% http://papersapp.com/papers/

@article{Quinlan:2010km,
author = {Quinlan, A R and Hall, I M},
title = {{BEDTools: a flexible suite of utilities for comparing genomic features}},
journal = {Bioinformatics},
year = {2010},
volume = {26},
number = {6},
pages = {841--842},
month = mar
}

@article{Tobin:1958uq,
author = {Tobin, James},
title = {{Estimation of relationships for limited dependent variables}},
journal = {Econometrica. Journal of the Econometric Society},
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volume = {26},
pages = {24--36}
}

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author = {De Figueiredo, L H and de Miranda Gomes, J},
title = {{Computational morphology of curves}},
journal = {The Visual Computer},
year = {1994}
}

@article{Giesen:1999tw,
author = {Giesen, J},
title = {{Curve reconstruction in arbitrary dimension and the traveling salesman problem}},
journal = {Discrete Geometry for Computer Imagery},
year = {1999}
}

@article{Giesen:2000kj,
author = {Giesen, J},
title = {{Curve reconstruction, the traveling salesman problem, and Menger's theorem on length}},
journal = {Discrete \{\&} Computational Geometry. An International Journal of Mathematics and Computer Science},
year = {2000},
volume = {24},
number = {4},
pages = {577--603}
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@article{Moffat:2006ij,
author = {Moffat, Jason and Grueneberg, Dorre A and Yang, Xiaoping and Kim, So Young and Kloepfer, Angela M and Hinkle, Gregory and Piqani, Bruno and Eisenhaure, Thomas M and Luo, Biao and Grenier, Jennifer K and Carpenter, Anne E and Foo, Shi Yin and Stewart, Sheila A and Stockwell, Brent R and Hacohen, Nir and Hahn, William C and Lander, Eric S and Sabatini, David M and Root, David E},
title = {{A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen.}},
journal = {Cell},
year = {2006},
volume = {124},
number = {6},
pages = {1283--1298},
month = mar
}

@article{Trapnell:2012gg,
author = {Trapnell, Cole and Hendrickson, David G and Sauvageau, Martin and Goff, Loyal and Rinn, John L and Pachter, Lior},
title = {{Differential analysis of gene regulation at transcript resolution with RNA-seq}},
journal = {Nature Biotechnology},
year = {2012},
pages = {1--9},
month = dec
}

@article{Neph:2013cy,
author = {Neph, Shane and Vierstra, Jeff and Stergachis, Andrew B and Reynolds, Alex P and Haugen, Eric and Vernot, Benjamin and Thurman, Robert E and John, Sam and Sandstrom, Richard and Johnson, Audra K and Maurano, Matthew T and Humbert, Richard and Rynes, Eric and Wang, Hao and Vong, Shinny and Lee, Kristen and Bates, Daniel and Diegel, Morgan and Roach, Vaughn and Dunn, Douglas and Neri, Jun and Schafer, Anthony and Hansen, R Scott and Kutyavin, Tanya and Giste, Erika and Weaver, Molly and Canfield, Theresa and Sabo, Peter and Zhang, Miaohua and Balasundaram, Gayathri and Byron, Rachel and MacCoss, Michael J and Akey, Joshua M and Bender, M A and Groudine, Mark and Kaul, Rajinder and Stamatoyannopoulos, John A},
title = {{An expansive human regulatory lexicon encoded in transcription factor footprints}},
journal = {Nature},
year = {2013},
volume = {488},
number = {7414},
pages = {83--90},
month = apr
}

@article{Trapnell:2012kp,
author = {Trapnell, Cole and Roberts, Adam and Goff, Loyal and Pertea, Geo and Kim, Daehwan and Kelley, David R and Pimentel, Harold and Salzberg, Steven L and Rinn, John L and Pachter, Lior},
title = {{Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.}},
journal = {Nature Protocols},
year = {2012},
volume = {7},
number = {3},
pages = {562--578},
month = mar
}

@article{Magwene:2003kq,
author = {Magwene, P M and Lizardi, P and Kim, J},
title = {{Reconstructing the temporal ordering of biological samples using microarray data}},
journal = {Bioinformatics},
year = {2003},
volume = {19},
number = {7},
pages = {842--850},
month = may
}

@article{Ernst:2012ii,
author = {Ernst, Jason and Kellis, Manolis},
title = {{correspondence}},
journal = {Nature methods},
year = {2012},
volume = {9},
number = {3},
pages = {215--216},
month = mar
}

@article{Harrow:2006ee,
author = {Harrow, Jennifer and Denoeud, France and Frankish, Adam and Reymond, Alexandre and Chen, Chao-Kung and Chrast, Jacqueline and Lagarde, Julien and Gilbert, James G R and Storey, Roy and Swarbreck, David and Rossier, Colette and Ucla, Catherine and Hubbard, Tim and Antonarakis, Stylianos E and Guigo, Roderic},
title = {{GENCODE: producing a reference annotation for ENCODE.}},
journal = {Genome biology},
year = {2006},
volume = {7 Suppl 1},
pages = {S4.1--9}
}

@article{Zhao:2005ku,
author = {Zhao, Sheng and Fernald, Russell D},
title = {{Comprehensive algorithm for quantitative real-time polymerase chain reaction.}},
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year = {2005},
volume = {12},
number = {8},
pages = {1047--1064},
month = oct
}

@article{Kim:2013eo,
author = {Kim, Daehwan and Pertea, Geo and Trapnell, Cole and Pimentel, Harold and Kelley, Ryan and Salzberg, Steven L},
title = {{TopHat2: accurate alignment of transcriptomes inthe presence of insertions, deletions and gene fusions}},
journal = {Genome biology},
year = {2013},
volume = {14},
number = {4},
pages = {R36},
month = apr
}

@article{Langmead:2012jh,
author = {Langmead, Ben and Salzberg, Steven L},
title = {{Fast gapped-read alignment with Bowtie 2}},
journal = {Nature methods},
year = {2012},
volume = {9},
number = {4},
pages = {357--359},
month = mar
}

@article{Yee:2008tz,
author = {Yee, T W},
title = {{The VGAM package}},
journal = {R News},
year = {2008}
}

@article{Booth:1976ub,
author = {Booth, K S and Lueker, G S},
title = {{Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms}},
journal = {Journal of Computer and System Sciences},
year = {1976}
}

@article{Wu:2012kp,
author = {Wu, D and Smyth, G K},
title = {{Camera: a competitive gene set test accounting for inter-gene correlation}},
journal = {Nucleic acids research},
year = {2012},
volume = {40},
number = {17},
pages = {e133--e133},
month = sep
}

@article{Hastie:1986vg,
author = {Hastie, T and Tibshirani, R},
title = {{Generalized additive models}},
journal = {Statistical science},
year = {1986}
}

@article{Wood:2006wz,
author = {Wood, S},
title = {{Generalized additive models: an introduction with R}},
year = {2006}
}

@article{Hyvarinen:2000vk,
author = {Hyv{\"a}rinen, A and Oja, E},
title = {{Independent component analysis: algorithms and applications}},
journal = {Neural networks},
year = {2000}
}

@article{Cacchiarelli:2011cb,
author = {Cacchiarelli, Davide and Incitti, Tania and Martone, Julie and Cesana, Marcella and Cazzella, Valentina and Santini, Tiziana and Sthandier, Olga and Bozzoni, Irene},
title = {{miR-31 modulates dystrophin expression: new implications for Duchenne muscular dystrophy therapy}},
journal = {EMBO reports},
year = {2011},
volume = {12},
number = {2},
pages = {136--141},
month = jan
}

@article{TRAPNELL_CACCHIARELLI,
author = {Trapnell, Cole and Cacchiarelli, Davide and Grimsby, Jonna and Pokharel, Prapti and Li, Shuqiang and Morse, Michael and Lennon, Niall J. and Livak, Kenneth J. and Mikkelsen, Tarjei S. and Rinn, John L.},
title = {{The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells}},
journal = {Nature Biotechnology},
year = {2014}
}


@inproceedings{Mao2015-cs,
  title={Dimensionality reduction via graph structure learning},
  author={Mao, Qi and Wang, Li and Goodison, Steve and Sun, Yijun},
  booktitle={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={765--774},
  year={2015},
  organization={ACM}
}



@INCOLLECTION{Mao_undated-xi,
  title     = "{SimplePPT}: A Simple Principal Tree Algorithm",
  booktitle = "Proceedings of the 2015 {SIAM} International Conference on Data
               Mining",
  author    = "Mao, Qi and Yang, Le and Wang, Li and Goodison, Steve and Sun,
               Yijun",
  abstract  = "Abstract Many scientific datasets are of high dimension, and the
               analysis usually requires visual manipulation by retaining the
               most important structures of data. Principal curve is a widely
               used approach for this purpose. However, many existing methods
               work only for data with structures that are not
               self-intersected, which is quite restrictive for real
               applications. To address this issue, we develop a new model,
               which captures the local information of the underlying graph
               structure based on reversed graph embedding. A generalization
               bound is derived that show that the model is consistent if the
               number of data points is sufficiently large. As a special case,
               a principal tree model is proposed and a new algorithm is
               developed that learns a tree structure automatically from data.
               The new algorithm is simple and parameter-free with guaranteed
               convergence. Experimental results on synthetic and breast cancer
               datasets show that the proposed method compares favorably with
               baselines and can discover a breast cancer progression path with
               multiple branches.",
  pages     = "792--800"
}


@ARTICLE{Anders2010-oc,
  title    = "Differential expression analysis for sequence count data",
  author   = "Anders, Simon and Huber, Wolfgang",
  abstract = "High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or
              barcode counting provide quantitative readouts in the form of
              count data. To infer differential signal in such data correctly
              and with good statistical power, estimation of data variability
              throughout the dynamic range and a suitable error model are
              required. We propose a method based on the negative binomial
              distribution, with variance and mean linked by local regression
              and present an implementation, DESeq, as an R/Bioconductor
              package.",
  journal  = "Genome Biol.",
  volume   =  11,
  number   =  10,
  pages    = "R106",
  year     =  2010
}


@ARTICLE{Treutlein2014-ee,
  title     = "Reconstructing lineage hierarchies of the distal lung epithelium
               using single-cell {RNA-seq}",
  author    = "Treutlein, Barbara and Brownfield, Doug G and Wu, Angela R and
               Neff, Norma F and Mantalas, Gary L and Hernan Espinoza, F and
               Desai, Tushar J and Krasnow, Mark A and Quake, Stephen R",
  abstract  = "The mammalian lung is a highly branched network in which the
               distal regions of the bronchial tree transform during
               development into a densely packed honeycomb of alveolar air sacs
               that mediate gas exchange. Although this transformation has been
               studied by marker expression analysis and fate-mapping, the
               mechanisms that control the progression of lung progenitors
               along distinct lineages into mature alveolar cell types are
               still incompletely known, in part because of the limited number
               of lineage markers and the effects of ensemble averaging in
               conventional transcriptome analysis experiments on cell
               populations. Here we show that single-cell transcriptome
               analysis circumvents these problems and enables direct
               measurement of the various cell types and hierarchies in the
               developing lung. We used microfluidic single-cell RNA sequencing
               (RNA-seq) on 198 individual cells at four different stages
               encompassing alveolar differentiation to measure the
               transcriptional states which define the developmental and
               cellular hierarchy of the distal mouse lung epithelium. We
               empirically classified cells into distinct groups by using an
               unbiased genome-wide approach that did not require a priori
               knowledge of the underlying cell types or the previous
               purification of cell populations. The results confirmed the
               basic outlines of the classical model of epithelial cell-type
               diversity in the distal lung and led to the discovery of many
               previously unknown cell-type markers, including transcriptional
               regulators that discriminate between the different populations.
               We reconstructed the molecular steps during maturation of
               bipotential progenitors along both alveolar lineages and
               elucidated the full life cycle of the alveolar type[thinsp]2
               cell lineage. This single-cell genomics approach is applicable
               to any developing or mature tissue to robustly delineate
               molecularly distinct cell types, define progenitors and lineage
               hierarchies, and identify lineage-specific regulatory factors.",
  journal   = "Nature",
  publisher = "Nature Research",
  volume    =  509,
  number    =  7500,
  pages     = "371--375",
  month     =  "13~" # apr,
  year      =  2014,
  language  = "en"
}


@ARTICLE{Macosko2015-fh,
  title   = "Highly Parallel Genome-wide Expression Profiling of Individual
             Cells Using Nanoliter Droplets",
  author  = "Macosko, Evan Z and Basu, Anindita and Satija, Rahul and Nemesh,
             James and Shekhar, Karthik and Goldman, Melissa and Tirosh, Itay
             and Bialas, Allison R and Kamitaki, Nolan and Martersteck, Emily M
             and Trombetta, John J and Weitz, David A and Sanes, Joshua R and
             Shalek, Alex K and Regev, Aviv and McCarroll, Steven A",
  journal = "Cell",
  volume  =  161,
  number  =  5,
  pages   = "1202--1214",
  year    =  2015
}

@ARTICLE{qiu2017single,
  title={Single-cell mRNA quantification and differential analysis with Census.},
  author={Qiu, X and Hill, A and Packer, J and Lin, D and Ma, YA and Trapnell, C},
  journal={Nature methods},
  year={2017}
}


@ARTICLE{Maaten2008-om,
  title    = "Visualizing Data using {t-SNE}",
  author   = "Maaten, Laurens van der and Hinton, Geoffrey",
  journal  = "J. Mach. Learn. Res.",
  volume   =  9,
  number   = "Nov",
  pages    = "2579--2605",
  year     =  2008
}


@ARTICLE{RodriguezLaio,
  title={Clustering by fast search and find of density peaks.},
  author={Alex Rodriguez, Alessandro Laio.},
  journal={Science},
  volume  =  344,
  pages   = "1492--1496",
  year={2014}
}

@Book{Wickham,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2009},
    isbn = {978-0-387-98140-6},
    url = {http://ggplot2.org},
  }

@ARTICLE{Mao2016-ru,
  title     = "Principal Graph and Structure Learning Based on Reversed Graph
               Embedding",
  author    = "Mao, Qi and Wang, Li and Tsang, Ivor and Sun, Yijun",
  abstract  = "Many scientific datasets are of high dimension, and the analysis
               usually requires retaining the most important structures of
               data. Principal curve is a widely used approach for this
               purpose. However, many existing methods work only for data with
               structures that are mathematically formulated by curves, which
               is quite restrictive for real applications. A few methods can
               overcome the above problem, but they either require complicated
               human-made rules for a specific task with lack of adaption
               flexibility to different tasks, or cannot obtain explicit
               structures of data. To address these issues, we develop a novel
               principal graph and structure learning framework that captures
               the local information of the underlying graph structure based on
               reversed graph embedding. As showcases, models that can learn a
               spanning tree or a weighted undirected `1 graph are proposed,
               and a new learning algorithm is developed that learns a set of
               principal points and a graph structure from data,
               simultaneously. The new algorithm is simple with guaranteed
               convergence. We then extend the proposed framework to deal with
               large-scale data. Experimental results on various synthetic and
               six real world datasets show that the proposed method compares
               favorably with baselines and can uncover the underlying
               structure correctly.",
  journal   = "IEEE Trans. Pattern Anal. Mach. Intell.",
  publisher = "ieeexplore.ieee.org",
  month     =  "5~" # dec,
  year      =  2016,
  language  = "en"
}


@ARTICLE{Satija2015-xb,
  title       = "Spatial reconstruction of single-cell gene expression data",
  author      = "Satija, Rahul and Farrell, Jeffrey A and Gennert, David and
                 Schier, Alexander F and Regev, Aviv",
  affiliation = "Broad Institute of MIT and Harvard, Cambridge, Massachusetts,
                 USA. Department of Molecular and Cellular Biology, Harvard
                 University, Cambridge, Massachusetts, USA. Broad Institute of
                 MIT and Harvard, Cambridge, Massachusetts, USA. 1] Broad
                 Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
                 [2] Department of Molecular and Cellular Biology, Harvard
                 University, Cambridge, Massachusetts, USA. [3] Center for
                 Brain Science, Harvard University, Cambridge, Massachusetts,
                 USA. [4] Harvard Stem Cell Institute, Harvard University,
                 Cambridge, Massachusetts, USA. [5] Center for Systems Biology,
                 Harvard University, Cambridge, Massachusetts, USA. 1] Broad
                 Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
                 [2] Howard Hughes Medical Institute, Department of Biology,
                 Massachusetts Institute of Technology, Cambridge,
                 Massachusetts, USA.",
  abstract    = "Spatial localization is a key determinant of cellular fate and
                 behavior, but methods for spatially resolved,
                 transcriptome-wide gene expression profiling across complex
                 tissues are lacking. RNA staining methods assay only a small
                 number of transcripts, whereas single-cell RNA-seq, which
                 measures global gene expression, separates cells from their
                 native spatial context. Here we present Seurat, a
                 computational strategy to infer cellular localization by
                 integrating single-cell RNA-seq data with in situ RNA
                 patterns. We applied Seurat to spatially map 851 single cells
                 from dissociated zebrafish (Danio rerio) embryos and generated
                 a transcriptome-wide map of spatial patterning. We confirmed
                 Seurat's accuracy using several experimental approaches, then
                 used the strategy to identify a set of archetypal expression
                 patterns and spatial markers. Seurat correctly localizes rare
                 subpopulations, accurately mapping both spatially restricted
                 and scattered groups. Seurat will be applicable to mapping
                 cellular localization within complex patterned tissues in
                 diverse systems.",
  journal     = "Nat. Biotechnol.",
  volume      =  33,
  number      =  5,
  pages       = "495--502",
  month       =  may,
  year        =  2015,
  language    = "en"
}


@UNPUBLISHED{Qiu2017-nx,
  title    = "Reversed graph embedding resolves complex single-cell
              developmental trajectories",
  author   = "Qiu, Xiaojie and Mao, Qi and Tang, Ying and Wang, Li and Chawla,
              Raghav and Pliner, Hannah and Trapnell, Cole",
  abstract = "Organizing single cells along a developmental trajectory has
              emerged as a powerful tool for understanding how gene regulation
              governs cell fate decisions. However, learning the structure of
              complex single-cell trajectories with two or more branches
              remains a challenging computational problem. We present Monocle
              2, which uses reversed graph embedding to reconstruct single-cell
              trajectories in a fully unsupervised manner. Monocle 2 learns an
              explicit principal graph to describe the data, greatly improving
              the robustness and accuracy of its trajectories compared to other
              algorithms. Monocle 2 uncovered a new, alternative cell fate in
              what we previously reported to be a linear trajectory for
              differentiating myoblasts. We also reconstruct branched
              trajectories for two studies of blood development, and show that
              loss of function mutations in key lineage transcription factors
              diverts cells to alternative branches on the a trajectory.
              Monocle 2 is thus a powerful tool for analyzing cell fate
              decisions with single-cell genomics.",
  journal  = "bioRxiv",
  pages    = "110668",
  month    =  "21~" # feb,
  year     =  2017,
  language = "en"
}

@ARTICLE{Treutlein2014-za,
  title       = "Reconstructing lineage hierarchies of the distal lung
                 epithelium using single-cell {RNA-seq}",
  author      = "Treutlein, Barbara and Brownfield, Doug G and Wu, Angela R and
                 Neff, Norma F and Mantalas, Gary L and Espinoza, F Hernan and
                 Desai, Tushar J and Krasnow, Mark A and Quake, Stephen R",
  affiliation = "1] Departments of Bioengineering and Applied Physics, Stanford
                 University School of Medicine and Howard Hughes Medical
                 Institute, Stanford, California 94305, USA [2]. 1] Department
                 of Biochemistry, Stanford University School of Medicine and
                 Howard Hughes Medical Institute, Stanford, California 94305,
                 USA [2]. Departments of Bioengineering and Applied Physics,
                 Stanford University School of Medicine and Howard Hughes
                 Medical Institute, Stanford, California 94305, USA.
                 Departments of Bioengineering and Applied Physics, Stanford
                 University School of Medicine and Howard Hughes Medical
                 Institute, Stanford, California 94305, USA. Departments of
                 Bioengineering and Applied Physics, Stanford University School
                 of Medicine and Howard Hughes Medical Institute, Stanford,
                 California 94305, USA. Department of Biochemistry, Stanford
                 University School of Medicine and Howard Hughes Medical
                 Institute, Stanford, California 94305, USA. Department of
                 Internal Medicine, Division of Pulmonary and Critical Care
                 Medicine, Stanford University School of Medicine, Stanford,
                 California 94305, USA. Department of Biochemistry, Stanford
                 University School of Medicine and Howard Hughes Medical
                 Institute, Stanford, California 94305, USA. Departments of
                 Bioengineering and Applied Physics, Stanford University School
                 of Medicine and Howard Hughes Medical Institute, Stanford,
                 California 94305, USA.",
  abstract    = "The mammalian lung is a highly branched network in which the
                 distal regions of the bronchial tree transform during
                 development into a densely packed honeycomb of alveolar air
                 sacs that mediate gas exchange. Although this transformation
                 has been studied by marker expression analysis and
                 fate-mapping, the mechanisms that control the progression of
                 lung progenitors along distinct lineages into mature alveolar
                 cell types are still incompletely known, in part because of
                 the limited number of lineage markers and the effects of
                 ensemble averaging in conventional transcriptome analysis
                 experiments on cell populations. Here we show that single-cell
                 transcriptome analysis circumvents these problems and enables
                 direct measurement of the various cell types and hierarchies
                 in the developing lung. We used microfluidic single-cell RNA
                 sequencing (RNA-seq) on 198 individual cells at four different
                 stages encompassing alveolar differentiation to measure the
                 transcriptional states which define the developmental and
                 cellular hierarchy of the distal mouse lung epithelium. We
                 empirically classified cells into distinct groups by using an
                 unbiased genome-wide approach that did not require a priori
                 knowledge of the underlying cell types or the previous
                 purification of cell populations. The results confirmed the
                 basic outlines of the classical model of epithelial cell-type
                 diversity in the distal lung and led to the discovery of many
                 previously unknown cell-type markers, including
                 transcriptional regulators that discriminate between the
                 different populations. We reconstructed the molecular steps
                 during maturation of bipotential progenitors along both
                 alveolar lineages and elucidated the full life cycle of the
                 alveolar type 2 cell lineage. This single-cell genomics
                 approach is applicable to any developing or mature tissue to
                 robustly delineate molecularly distinct cell types, define
                 progenitors and lineage hierarchies, and identify
                 lineage-specific regulatory factors.",
  journal     = "Nature",
  volume      =  509,
  number      =  7500,
  pages       = "371--375",
  month       =  "15~" # may,
  year        =  2014,
  language    = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Paul2015-bh,
  title       = "Transcriptional Heterogeneity and Lineage Commitment in
                 Myeloid Progenitors",
  author      = "Paul, Franziska and Arkin, Ya'ara and Giladi, Amir and Jaitin,
                 Diego Adhemar and Kenigsberg, Ephraim and Keren-Shaul, Hadas
                 and Winter, Deborah and Lara-Astiaso, David and Gury, Meital
                 and Weiner, Assaf and David, Eyal and Cohen, Nadav and
                 Lauridsen, Felicia Kathrine Bratt and Haas, Simon and
                 Schlitzer, Andreas and Mildner, Alexander and Ginhoux, Florent
                 and Jung, Steffen and Trumpp, Andreas and Porse, Bo Torben and
                 Tanay, Amos and Amit, Ido",
  affiliation = "Department of Immunology, Weizmann Institute of Science,
                 Rehovot 76100, Israel. Department of Computer Science and
                 Applied Mathematics and Department of Biological Regulation,
                 Weizmann Institute of Science, Rehovot 76100, Israel.
                 Department of Immunology, Weizmann Institute of Science,
                 Rehovot 76100, Israel. Department of Immunology, Weizmann
                 Institute of Science, Rehovot 76100, Israel. Department of
                 Computer Science and Applied Mathematics and Department of
                 Biological Regulation, Weizmann Institute of Science, Rehovot
                 76100, Israel. Department of Immunology, Weizmann Institute of
                 Science, Rehovot 76100, Israel. Department of Immunology,
                 Weizmann Institute of Science, Rehovot 76100, Israel.
                 Department of Immunology, Weizmann Institute of Science,
                 Rehovot 76100, Israel. Department of Immunology, Weizmann
                 Institute of Science, Rehovot 76100, Israel. Department of
                 Immunology, Weizmann Institute of Science, Rehovot 76100,
                 Israel. Department of Immunology, Weizmann Institute of
                 Science, Rehovot 76100, Israel. Department of Computer Science
                 and Applied Mathematics and Department of Biological
                 Regulation, Weizmann Institute of Science, Rehovot 76100,
                 Israel. The Finsen Laboratory, Rigshospitalet, University of
                 Copenhagen, Copenhagen 2200, Denmark; Biotech Research and
                 Innovation Centre (BRIC), Copenhagen 2200, Denmark; Danish
                 Stem Cell Centre (DanStem) Faculty of Health Sciences,
                 University of Copenhagen, 2200 Copenhagen, Denmark. Division
                 of Stem Cells and Cancer, Deutsches Krebsforschungszentrum
                 (DKFZ), 69120 Heidelberg, Germany. Singapore Immunology
                 Network (SIgN), Agency for Science, Technology and Research
                 (A(∗)STAR), BIOPOLIS 138648, Singapore; Genomics and
                 Immunoregulation, Life and Medical Sciences (LIMES) Institute,
                 University of Bonn, 53115 Bonn, Germany. Department of
                 Immunology, Weizmann Institute of Science, Rehovot 76100,
                 Israel. Singapore Immunology Network (SIgN), Agency for
                 Science, Technology and Research (A(∗)STAR), BIOPOLIS 138648,
                 Singapore. Department of Immunology, Weizmann Institute of
                 Science, Rehovot 76100, Israel. Division of Stem Cells and
                 Cancer, Deutsches Krebsforschungszentrum (DKFZ), 69120
                 Heidelberg, Germany. The Finsen Laboratory, Rigshospitalet,
                 University of Copenhagen, Copenhagen 2200, Denmark; Biotech
                 Research and Innovation Centre (BRIC), Copenhagen 2200,
                 Denmark; Danish Stem Cell Centre (DanStem) Faculty of Health
                 Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
                 Department of Computer Science and Applied Mathematics and
                 Department of Biological Regulation, Weizmann Institute of
                 Science, Rehovot 76100, Israel. Electronic address:
                 amos.tanay@weizmann.ac.il. Department of Immunology, Weizmann
                 Institute of Science, Rehovot 76100, Israel. Electronic
                 address: ido.amit@weizmann.ac.il.",
  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.",
  journal     = "Cell",
  volume      =  163,
  number      =  7,
  pages       = "1663--1677",
  month       =  "17~" # dec,
  year        =  2015,
  language    = "en"
}





