book.bib

@ARTICLE{Deng2014-mx,
  title       = "Single-cell {RNA-seq} reveals dynamic, random monoallelic gene
                 expression in mammalian cells",
  author      = "Deng, Qiaolin and Ramsk{\"{o}}ld, Daniel and Reinius,
                 Bj{\"{o}}rn and Sandberg, Rickard",
  affiliation = "Ludwig Institute for Cancer Research, Box 240, 171 77
                 Stockholm, Sweden.",
  abstract    = "Expression from both alleles is generally observed in analyses
                 of diploid cell populations, but studies addressing allelic
                 expression patterns genome-wide in single cells are lacking.
                 Here, we present global analyses of allelic expression across
                 individual cells of mouse preimplantation embryos of mixed
                 background (CAST/EiJ \texttimes{} C57BL/6J). We discovered
                 abundant (12 to 24\%) monoallelic expression of autosomal
                 genes and that expression of the two alleles occurs
                 independently. The monoallelic expression appeared random and
                 dynamic because there was considerable variation among closely
                 related embryonic cells. Similar patterns of monoallelic
                 expression were observed in mature cells. Our allelic
                 expression analysis also demonstrates the de novo inactivation
                 of the paternal X chromosome. We conclude that independent and
                 stochastic allelic transcription generates abundant random
                 monoallelic expression in the mammalian cell.",
  journal     = "Science",
  volume      =  343,
  number      =  6167,
  pages       = "193--196",
  month       =  "10~" # jan,
  year        =  2014
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Macosko2015-ix,
  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",
  abstract = "Summary Cells, the basic units of biological structure and
              function, vary broadly in type and state. Single-cell genomics
              can characterize cell identity and function, but limitations of
              ease and scale have prevented its broad application. Here we
              describe Drop-seq, a strategy for quickly profiling thousands of
              individual cells by separating them into nanoliter-sized aqueous
              droplets, associating a different barcode with each cell’s RNAs,
              and sequencing them all together. Drop-seq analyzes mRNA
              transcripts from thousands of individual cells simultaneously
              while remembering transcripts’ cell of origin. We analyzed
              transcriptomes from 44,808 mouse retinal cells and identified 39
              transcriptionally distinct cell populations, creating a molecular
              atlas of gene expression for known retinal cell classes and novel
              candidate cell subtypes. Drop-seq will accelerate biological
              discovery by enabling routine transcriptional profiling at
              single-cell resolution. Video Abstract",
  journal  = "Cell",
  volume   =  161,
  number   =  5,
  pages    = "1202--1214",
  month    =  "21~" # may,
  year     =  2015
}

@ARTICLE{Xu2015-vf,
  title       = "Identification of cell types from single-cell transcriptomes
                 using a novel clustering method",
  author      = "Xu, Chen and Su, Zhengchang",
  affiliation = "Department of Bioinformatics and Genomics, University of North
                 Carolina at Charlotte, Charlotte, NC 28223, USA. Department of
                 Bioinformatics and Genomics, University of North Carolina at
                 Charlotte, Charlotte, NC 28223, USA.",
  abstract    = "MOTIVATION: The recent advance of single-cell technologies has
                 brought new insights into complex biological phenomena. In
                 particular, genome-wide single-cell measurements such as
                 transcriptome sequencing enable the characterization of
                 cellular composition as well as functional variation in
                 homogenic cell populations. An important step in the
                 single-cell transcriptome analysis is to group cells that
                 belong to the same cell types based on gene expression
                 patterns. The corresponding computational problem is to
                 cluster a noisy high dimensional dataset with substantially
                 fewer objects (cells) than the number of variables (genes).
                 RESULTS: In this article, we describe a novel algorithm named
                 shared nearest neighbor (SNN)-Cliq that clusters single-cell
                 transcriptomes. SNN-Cliq utilizes the concept of shared
                 nearest neighbor that shows advantages in handling
                 high-dimensional data. When evaluated on a variety of
                 synthetic and real experimental datasets, SNN-Cliq
                 outperformed the state-of-the-art methods tested. More
                 importantly, the clustering results of SNN-Cliq reflect the
                 cell types or origins with high accuracy. AVAILABILITY AND
                 IMPLEMENTATION: The algorithm is implemented in MATLAB and
                 Python. The source code can be downloaded at
                 http://bioinfo.uncc.edu/SNNCliq. CONTACT: zcsu@uncc.edu
                 Supplementary information: Supplementary data are available at
                 Bioinformatics online.",
  journal     = "Bioinformatics",
  month       =  "11~" # feb,
  year        =  2015
}

@ARTICLE{Zurauskiene2016-kg,
  title       = "pcaReduce: hierarchical clustering of single cell
                 transcriptional profiles",
  author      = "\v{Z}urauskien\.{e}, Justina and Yau, Christopher",
  affiliation = "Wellcome Trust Centre for Human Genetics, University of
                 Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK. Wellcome Trust
                 Centre for Human Genetics, University of Oxford, Roosevelt
                 Drive, Oxford, OX3 7BN, UK. cyau@well.ox.ac.uk. Department of
                 Statistics, University of Oxford, 1 S. Parks Rd, Oxford, OX1
                 3TG, UK. cyau@well.ox.ac.uk.",
  abstract    = "BACKGROUND: Advances in single cell genomics provide a way of
                 routinely generating transcriptomics data at the single cell
                 level. A frequent requirement of single cell expression
                 analysis is the identification of novel patterns of
                 heterogeneity across single cells that might explain complex
                 cellular states or tissue composition. To date, classical
                 statistical analysis tools have being routinely applied, but
                 there is considerable scope for the development of novel
                 statistical approaches that are better adapted to the
                 challenges of inferring cellular hierarchies. RESULTS: We have
                 developed a novel agglomerative clustering method that we call
                 pcaReduce to generate a cell state hierarchy where each
                 cluster branch is associated with a principal component of
                 variation that can be used to differentiate two cell states.
                 Using two real single cell datasets, we compared our approach
                 to other commonly used statistical techniques, such as K-means
                 and hierarchical clustering. We found that pcaReduce was able
                 to give more consistent clustering structures when compared to
                 broad and detailed cell type labels. CONCLUSIONS: Our novel
                 integration of principal components analysis and hierarchical
                 clustering establishes a connection between the representation
                 of the expression data and the number of cell types that can
                 be discovered. In doing so we found that pcaReduce performs
                 better than either technique in isolation in terms of
                 characterising putative cell states. Our methodology is
                 complimentary to other single cell clustering techniques and
                 adds to a growing palette of single cell bioinformatics tools
                 for profiling heterogeneous cell populations.",
  journal     = "BMC Bioinformatics",
  volume      =  17,
  pages       = "140",
  month       =  "22~" # mar,
  year        =  2016,
  keywords    = "Gene expression; Hierarchical clustering; Single cell RNA-Seq"
}

@ARTICLE{Guo2015-ok,
  title       = "{SINCERA}: A Pipeline for {Single-Cell} {RNA-Seq} Profiling
                 Analysis",
  author      = "Guo, Minzhe and Wang, Hui and Potter, S Steven and Whitsett,
                 Jeffrey A and Xu, Yan",
  affiliation = "The Perinatal Institute, Section of Neonatology, Perinatal and
                 Pulmonary Biology, Cincinnati Children's Hospital Medical
                 Center, Cincinnati, Ohio, United States of America. Department
                 of Electrical Engineering and Computing Systems, College of
                 Engineering and Applied Science, University of Cincinnati,
                 Cincinnati, Ohio, United States of America. The Perinatal
                 Institute, Section of Neonatology, Perinatal and Pulmonary
                 Biology, Cincinnati Children's Hospital Medical Center,
                 Cincinnati, Ohio, United States of America. Division of
                 Developmental Biology, Cincinnati Children's Hospital Medical
                 Center, Cincinnati, Ohio, United States of America. The
                 Perinatal Institute, Section of Neonatology, Perinatal and
                 Pulmonary Biology, Cincinnati Children's Hospital Medical
                 Center, Cincinnati, Ohio, United States of America. The
                 Perinatal Institute, Section of Neonatology, Perinatal and
                 Pulmonary Biology, Cincinnati Children's Hospital Medical
                 Center, Cincinnati, Ohio, United States of America. Division
                 of Biomedical Informatics, Cincinnati Children's Hospital
                 Medical Center, Cincinnati, Ohio, United States of America.",
  abstract    = "A major challenge in developmental biology is to understand
                 the genetic and cellular processes/programs driving organ
                 formation and differentiation of the diverse cell types that
                 comprise the embryo. While recent studies using single cell
                 transcriptome analysis illustrate the power to measure and
                 understand cellular heterogeneity in complex biological
                 systems, processing large amounts of RNA-seq data from
                 heterogeneous cell populations creates the need for readily
                 accessible tools for the analysis of single-cell RNA-seq
                 (scRNA-seq) profiles. The present study presents a generally
                 applicable analytic pipeline (SINCERA: a computational
                 pipeline for SINgle CEll RNA-seq profiling Analysis) for
                 processing scRNA-seq data from a whole organ or sorted cells.
                 The pipeline supports the analysis for: 1) the distinction and
                 identification of major cell types; 2) the identification of
                 cell type specific gene signatures; and 3) the determination
                 of driving forces of given cell types. We applied this
                 pipeline to the RNA-seq analysis of single cells isolated from
                 embryonic mouse lung at E16.5. Through the pipeline analysis,
                 we distinguished major cell types of fetal mouse lung,
                 including epithelial, endothelial, smooth muscle, pericyte,
                 and fibroblast-like cell types, and identified cell type
                 specific gene signatures, bioprocesses, and key regulators.
                 SINCERA is implemented in R, licensed under the GNU General
                 Public License v3, and freely available from CCHMC PBGE
                 website, https://research.cchmc.org/pbge/sincera.html.",
  journal     = "PLoS Comput. Biol.",
  volume      =  11,
  number      =  11,
  pages       = "e1004575",
  month       =  nov,
  year        =  2015
}

@UNPUBLISHED{Kiselev2016-bq,
  title    = "{SC3} - consensus clustering of single-cell {RNA-Seq} data",
  author   = "Kiselev, Vladimir Yu and Kirschner, Kristina and Schaub, Michael
              T and Andrews, Tallulah and Chandra, Tamir and Natarajan, Kedar N
              and Reik, Wolf and Barahona, Mauricio and Green, Anthony R and
              Hemberg, Martin",
  abstract = "Using single-cell RNA-seq (scRNA-seq), the full transcriptome of
              individual cells can be acquired, enabling a quantitative
              cell-type characterisation based on expression profiles. Due to
              the large variability in gene expression, assigning cells into
              groups based on the transcriptome remains challenging. We present
              Single-Cell Consensus Clustering (SC3), a tool for unsupervised
              clustering of scRNA-seq data. SC3 achieves high accuracy and
              robustness by consistently integrating different clustering
              solutions through a consensus approach. Tests on nine published
              datasets show that SC3 outperforms 4 existing methods, while
              remaining scalable for large datasets, as shown by the analysis
              of a dataset containing 44,808 cells. Moreover, an interactive
              graphical implementation makes SC3 accessible to a wide audience
              of users, and SC3 also aids biological interpretation by
              identifying marker genes, differentially expressed genes and
              outlier cells. We illustrate the capabilities of SC3 by
              characterising newly obtained transcriptomes from subclones of
              neoplastic cells collected from patients.",
  journal  = "bioRxiv",
  pages    = "036558",
  month    =  "1~" # jan,
  year     =  2016,
  language = "en"
}



@ARTICLE{Tang2009-bu,
  title       = "{mRNA-Seq} whole-transcriptome analysis of a single cell",
  author      = "Tang, Fuchou and Barbacioru, Catalin and Wang, Yangzhou and
                 Nordman, Ellen and Lee, Clarence and Xu, Nanlan and Wang,
                 Xiaohui and Bodeau, John and Tuch, Brian B and Siddiqui, Asim
                 and Lao, Kaiqin and Surani, M Azim",
  affiliation = "Wellcome Trust-Cancer Research UK Gurdon Institute of Cancer
                 and Developmental Biology, University of Cambridge, Cambridge,
                 UK.",
  abstract    = "Next-generation sequencing technology is a powerful tool for
                 transcriptome analysis. However, under certain conditions,
                 only a small amount of material is available, which requires
                 more sensitive techniques that can preferably be used at the
                 single-cell level. Here we describe a single-cell digital gene
                 expression profiling assay. Using our mRNA-Seq assay with only
                 a single mouse blastomere, we detected the expression of 75\%
                 (5,270) more genes than microarray techniques and identified
                 1,753 previously unknown splice junctions called by at least 5
                 reads. Moreover, 8-19\% of the genes with multiple known
                 transcript isoforms expressed at least two isoforms in the
                 same blastomere or oocyte, which unambiguously demonstrated
                 the complexity of the transcript variants at whole-genome
                 scale in individual cells. Finally, for Dicer1(-/-) and
                 Ago2(-/-) (Eif2c2(-/-)) oocytes, we found that 1,696 and 1,553
                 genes, respectively, were abnormally upregulated compared to
                 wild-type controls, with 619 genes in common.",
  journal     = "Nat. Methods",
  volume      =  6,
  number      =  5,
  pages       = "377--382",
  month       =  may,
  year        =  2009
}

@ARTICLE{Picelli2013-sb,
  title       = "Smart-seq2 for sensitive full-length transcriptome profiling
                 in single cells",
  author      = "Picelli, Simone and Bj{\"{o}}rklund, \AA{}sa K and Faridani,
                 Omid R and Sagasser, Sven and Winberg, G{\"{o}}sta and
                 Sandberg, Rickard",
  affiliation = "Ludwig Institute for Cancer Research, Stockholm, Sweden.",
  abstract    = "Single-cell gene expression analyses hold promise for
                 characterizing cellular heterogeneity, but current methods
                 compromise on either the coverage, the sensitivity or the
                 throughput. Here, we introduce Smart-seq2 with improved
                 reverse transcription, template switching and preamplification
                 to increase both yield and length of cDNA libraries generated
                 from individual cells. Smart-seq2 transcriptome libraries have
                 improved detection, coverage, bias and accuracy compared to
                 Smart-seq libraries and are generated with off-the-shelf
                 reagents at lower cost.",
  journal     = "Nat. Methods",
  volume      =  10,
  number      =  11,
  pages       = "1096--1098",
  month       =  nov,
  year        =  2013
}


@ARTICLE{Hashimshony2012-kd,
  title       = "{CEL-Seq}: single-cell {RNA-Seq} by multiplexed linear
                 amplification",
  author      = "Hashimshony, Tamar and Wagner, Florian and Sher, Noa and
                 Yanai, Itai",
  affiliation = "Department of Biology, Technion-Israel Institute of
                 Technology, Haifa 32000, Israel.",
  abstract    = "High-throughput sequencing has allowed for unprecedented
                 detail in gene expression analyses, yet its efficient
                 application to single cells is challenged by the small
                 starting amounts of RNA. We have developed CEL-Seq, a method
                 for overcoming this limitation by barcoding and pooling
                 samples before linearly amplifying mRNA with the use of one
                 round of in vitro transcription. We show that CEL-Seq gives
                 more reproducible, linear, and sensitive results than a
                 PCR-based amplification method. We demonstrate the power of
                 this method by studying early C. elegans embryonic development
                 at single-cell resolution. Differential distribution of
                 transcripts between sister cells is seen as early as the
                 two-cell stage embryo, and zygotic expression in the somatic
                 cell lineages is enriched for transcription factors. The
                 robust transcriptome quantifications enabled by CEL-Seq will
                 be useful for transcriptomic analyses of complex tissues
                 containing populations of diverse cell types.",
  journal     = "Cell Rep.",
  volume      =  2,
  number      =  3,
  pages       = "666--673",
  month       =  "27~" # sep,
  year        =  2012
}


% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Macosko2015-ix,
  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",
  abstract = "Summary Cells, the basic units of biological structure and
              function, vary broadly in type and state. Single-cell genomics
              can characterize cell identity and function, but limitations of
              ease and scale have prevented its broad application. Here we
              describe Drop-seq, a strategy for quickly profiling thousands of
              individual cells by separating them into nanoliter-sized aqueous
              droplets, associating a different barcode with each cell’s RNAs,
              and sequencing them all together. Drop-seq analyzes mRNA
              transcripts from thousands of individual cells simultaneously
              while remembering transcripts’ cell of origin. We analyzed
              transcriptomes from 44,808 mouse retinal cells and identified 39
              transcriptionally distinct cell populations, creating a molecular
              atlas of gene expression for known retinal cell classes and novel
              candidate cell subtypes. Drop-seq will accelerate biological
              discovery by enabling routine transcriptional profiling at
              single-cell resolution. Video Abstract",
  journal  = "Cell",
  volume   =  161,
  number   =  5,
  pages    = "1202--1214",
  month    =  "21~" # may,
  year     =  2015
}


@ARTICLE{Saliba2014-dy,
  title       = "Single-cell {RNA-seq}: advances and future challenges",
  author      = "Saliba, Antoine-Emmanuel and Westermann, Alexander J and
                 Gorski, Stanislaw A and Vogel, J{\"{o}}rg",
  affiliation = "Institute for Molecular Infection Biology, University of
                 W{\"{u}}rzburg, Josef-Schneider-Stra\ss{}e 2, D-97080
                 W{\"{u}}rzburg, Germany. Institute for Molecular Infection
                 Biology, University of W{\"{u}}rzburg,
                 Josef-Schneider-Stra\ss{}e 2, D-97080 W{\"{u}}rzburg, Germany.
                 Institute for Molecular Infection Biology, University of
                 W{\"{u}}rzburg, Josef-Schneider-Stra\ss{}e 2, D-97080
                 W{\"{u}}rzburg, Germany. Institute for Molecular Infection
                 Biology, University of W{\"{u}}rzburg,
                 Josef-Schneider-Stra\ss{}e 2, D-97080 W{\"{u}}rzburg, Germany
                 joerg.vogel@uni-wuerzburg.de.",
  abstract    = "Phenotypically identical cells can dramatically vary with
                 respect to behavior during their lifespan and this variation
                 is reflected in their molecular composition such as the
                 transcriptomic landscape. Single-cell transcriptomics using
                 next-generation transcript sequencing (RNA-seq) is now
                 emerging as a powerful tool to profile cell-to-cell
                 variability on a genomic scale. Its application has already
                 greatly impacted our conceptual understanding of diverse
                 biological processes with broad implications for both basic
                 and clinical research. Different single-cell RNA-seq protocols
                 have been introduced and are reviewed here-each one with its
                 own strengths and current limitations. We further provide an
                 overview of the biological questions single-cell RNA-seq has
                 been used to address, the major findings obtained from such
                 studies, and current challenges and expected future
                 developments in this booming field.",
  journal     = "Nucleic Acids Res.",
  volume      =  42,
  number      =  14,
  pages       = "8845--8860",
  month       =  aug,
  year        =  2014
}


@ARTICLE{Handley2015-yi,
  title       = "Designing {Cell-Type-Specific} Genome-wide Experiments",
  author      = "Handley, Ava and Schauer, Tam\'{a}s and Ladurner, Andreas G
                 and Margulies, Carla E",
  affiliation = "Department of Physiological Chemistry, Biomedical Center,
                 Ludwig-Maximilians-University of Munich, Butenandtstrasse 5,
                 81377 Munich, Germany; International Max Planck Research
                 School for Molecular and Cellular Life Sciences, Am
                 Klopferspitz 18, 82152 Martinsried, Germany. Department of
                 Molecular Biology, Biomedical Center,
                 Ludwig-Maximilians-University of Munich, Schillerstrasse 44,
                 80336 Munich, Germany. Department of Physiological Chemistry,
                 Biomedical Center, Ludwig-Maximilians-University of Munich,
                 Butenandtstrasse 5, 81377 Munich, Germany; International Max
                 Planck Research School for Molecular and Cellular Life
                 Sciences, Am Klopferspitz 18, 82152 Martinsried, Germany;
                 Center for Integrated Protein Science Munich (CIPSM), 81377
                 Munich, Germany; Munich Cluster for Systems Neurology
                 (SyNergy), 80336 Munich, Germany. Department of Physiological
                 Chemistry, Biomedical Center, Ludwig-Maximilians-University of
                 Munich, Butenandtstrasse 5, 81377 Munich, Germany. Electronic
                 address: carla.margulies@med.lmu.de.",
  abstract    = "Multicellular organisms depend on cell-type-specific division
                 of labor for survival. Specific cell types have their unique
                 developmental program and respond differently to environmental
                 challenges, yet are orchestrated by the same genetic
                 blueprint. A key challenge in biology is thus to understand
                 how genes are expressed in the right place, at the right time,
                 and to the right level. Further, this exquisite control of
                 gene expression is perturbed in many diseases. As a
                 consequence, coordinated physiological responses to the
                 environment are compromised. Recently, innovative tools have
                 been developed that are able to capture genome-wide gene
                 expression using cell-type-specific approaches. These novel
                 techniques allow us to understand gene regulation in vivo with
                 unprecedented resolution and give us mechanistic insights into
                 how multicellular organisms adapt to changing environments. In
                 this article, we discuss the considerations needed when
                 designing your own cell-type-specific experiment from the
                 isolation of your starting material through selecting the
                 appropriate controls and validating the data.",
  journal     = "Mol. Cell",
  volume      =  58,
  number      =  4,
  pages       = "621--631",
  month       =  "21~" # may,
  year        =  2015
}


@ARTICLE{Kolodziejczyk2015-xy,
  title       = "The Technology and Biology of {Single-Cell} {RNA} Sequencing",
  author      = "Kolodziejczyk, Aleksandra A and Kim, Jong Kyoung and Svensson,
                 Valentine and Marioni, John C and Teichmann, Sarah A",
  affiliation = "European Molecular Biology Laboratory, European Bioinformatics
                 Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton,
                 Cambridge CB10 1SD, UK; Wellcome Trust Sanger Institute,
                 Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.
                 European Molecular Biology Laboratory, European Bioinformatics
                 Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton,
                 Cambridge CB10 1SD, UK. European Molecular Biology Laboratory,
                 European Bioinformatics Institute (EMBL-EBI), Wellcome Trust
                 Genome Campus, Hinxton, Cambridge CB10 1SD, UK. European
                 Molecular Biology Laboratory, European Bioinformatics
                 Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton,
                 Cambridge CB10 1SD, UK; Wellcome Trust Sanger Institute,
                 Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.
                 European Molecular Biology Laboratory, European Bioinformatics
                 Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton,
                 Cambridge CB10 1SD, UK; Wellcome Trust Sanger Institute,
                 Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.
                 Electronic address: saraht@ebi.ac.uk.",
  abstract    = "The differences between individual cells can have profound
                 functional consequences, in both unicellular and multicellular
                 organisms. Recently developed single-cell mRNA-sequencing
                 methods enable unbiased, high-throughput, and high-resolution
                 transcriptomic analysis of individual cells. This provides an
                 additional dimension to transcriptomic information relative to
                 traditional methods that profile bulk populations of cells.
                 Already, single-cell RNA-sequencing methods have revealed new
                 biology in terms of the composition of tissues, the dynamics
                 of transcription, and the regulatory relationships between
                 genes. Rapid technological developments at the level of cell
                 capture, phenotyping, molecular biology, and bioinformatics
                 promise an exciting future with numerous biological and
                 medical applications.",
  journal     = "Mol. Cell",
  volume      =  58,
  number      =  4,
  pages       = "610--620",
  month       =  "21~" # may,
  year        =  2015
}


@ARTICLE{Kharchenko2014-ts,
  title       = "Bayesian approach to single-cell differential expression
                 analysis",
  author      = "Kharchenko, Peter V and Silberstein, Lev and Scadden, David T",
  affiliation = "1] Center for Biomedical Informatics, Harvard Medical School,
                 Boston, Massachusetts, USA. [2] Hematology/Oncology Program,
                 Children's Hospital, Boston, Massachusetts, USA. [3] Harvard
                 Stem Cell Institute, Cambridge, Massachusetts, USA. 1] Harvard
                 Stem Cell Institute, Cambridge, Massachusetts, USA. [2] Center
                 for Regenerative Medicine, Massachusetts General Hospital,
                 Boston, Massachusetts, USA. [3] Department of Stem Cell and
                 Regenerative Biology, Harvard University, Cambridge,
                 Massachusetts, USA. 1] Harvard Stem Cell Institute, Cambridge,
                 Massachusetts, USA. [2] Center for Regenerative Medicine,
                 Massachusetts General Hospital, Boston, Massachusetts, USA.
                 [3] Department of Stem Cell and Regenerative Biology, Harvard
                 University, Cambridge, Massachusetts, USA.",
  abstract    = "Single-cell data provide a means to dissect the composition of
                 complex tissues and specialized cellular environments.
                 However, the analysis of such measurements is complicated by
                 high levels of technical noise and intrinsic biological
                 variability. We describe a probabilistic model of
                 expression-magnitude distortions typical of single-cell
                 RNA-sequencing measurements, which enables detection of
                 differential expression signatures and identification of
                 subpopulations of cells in a way that is more tolerant of
                 noise.",
  journal     = "Nat. Methods",
  volume      =  11,
  number      =  7,
  pages       = "740--742",
  month       =  jul,
  year        =  2014
}


@ARTICLE{Jiang2011-mu,
  title       = "Synthetic spike-in standards for {RNA-seq} experiments",
  author      = "Jiang, Lichun and Schlesinger, Felix and Davis, Carrie A and
                 Zhang, Yu and Li, Renhua and Salit, Marc and Gingeras, Thomas
                 R and Oliver, Brian",
  affiliation = "Section of Developmental Genomics, Laboratory of Cellular and
                 Developmental Biology, National Institute of Diabetes and
                 Digestive and Kidney Diseases, National Institutes of Health,
                 Bethesda, MD 20892, USA.",
  abstract    = "High-throughput sequencing of cDNA (RNA-seq) is a widely
                 deployed transcriptome profiling and annotation technique, but
                 questions about the performance of different protocols and
                 platforms remain. We used a newly developed pool of 96
                 synthetic RNAs with various lengths, and GC content covering a
                 2(20) concentration range as spike-in controls to measure
                 sensitivity, accuracy, and biases in RNA-seq experiments as
                 well as to derive standard curves for quantifying the
                 abundance of transcripts. We observed linearity between read
                 density and RNA input over the entire detection range and
                 excellent agreement between replicates, but we observed
                 significantly larger imprecision than expected under pure
                 Poisson sampling errors. We use the control RNAs to directly
                 measure reproducible protocol-dependent biases due to GC
                 content and transcript length as well as stereotypic
                 heterogeneity in coverage across transcripts correlated with
                 position relative to RNA termini and priming sequence bias.
                 These effects lead to biased quantification for short
                 transcripts and individual exons, which is a serious problem
                 for measurements of isoform abundances, but that can partially
                 be corrected using appropriate models of bias. By using the
                 control RNAs, we derive limits for the discovery and detection
                 of rare transcripts in RNA-seq experiments. By using data
                 collected as part of the model organism and human Encyclopedia
                 of DNA Elements projects (ENCODE and modENCODE), we
                 demonstrate that external RNA controls are a useful resource
                 for evaluating sensitivity and accuracy of RNA-seq experiments
                 for transcriptome discovery and quantification. These quality
                 metrics facilitate comparable analysis across different
                 samples, protocols, and platforms.",
  journal     = "Genome Res.",
  volume      =  21,
  number      =  9,
  pages       = "1543--1551",
  month       =  sep,
  year        =  2011
}


@ARTICLE{Kivioja2012-yt,
  title       = "Counting absolute numbers of molecules using unique molecular
                 identifiers",
  author      = "Kivioja, Teemu and V{\"{a}}h{\"{a}}rautio, Anna and Karlsson,
                 Kasper and Bonke, Martin and Enge, Martin and Linnarsson, Sten
                 and Taipale, Jussi",
  affiliation = "Genome-Scale Biology Program, Institute of Biomedicine,
                 University of Helsinki, Helsinki, Finland.",
  abstract    = "Counting individual RNA or DNA molecules is difficult because
                 they are hard to copy quantitatively for detection. To
                 overcome this limitation, we applied unique molecular
                 identifiers (UMIs), which make each molecule in a population
                 distinct, to genome-scale human karyotyping and mRNA
                 sequencing in Drosophila melanogaster. Use of this method can
                 improve accuracy of almost any next-generation sequencing
                 method, including chromatin immunoprecipitation-sequencing,
                 genome assembly, diagnostics and manufacturing-process control
                 and monitoring.",
  journal     = "Nat. Methods",
  volume      =  9,
  number      =  1,
  pages       = "72--74",
  month       =  jan,
  year        =  2012
}


@ARTICLE{Stegle2015-uv,
  title       = "Computational and analytical challenges in single-cell
                 transcriptomics",
  author      = "Stegle, Oliver and Teichmann, Sarah A and Marioni, John C",
  affiliation = "European Molecular Biology Laboratory European Bioinformatics
                 Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge,
                 CB10 1SD, UK. 1] European Molecular Biology Laboratory
                 European Bioinformatics Institute, Wellcome Trust Genome
                 Campus, Hinxton, Cambridge, CB10 1SD, UK. [2] Wellcome Trust
                 Sanger Institute, Wellcome Trust Genome Campus, Hinxton,
                 Cambridge, CB10 1SA, UK. 1] European Molecular Biology
                 Laboratory European Bioinformatics Institute, Wellcome Trust
                 Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. [2] Wellcome
                 Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton,
                 Cambridge, CB10 1SA, UK.",
  abstract    = "The development of high-throughput RNA sequencing (RNA-seq) at
                 the single-cell level has already led to profound new
                 discoveries in biology, ranging from the identification of
                 novel cell types to the study of global patterns of stochastic
                 gene expression. Alongside the technological breakthroughs
                 that have facilitated the large-scale generation of
                 single-cell transcriptomic data, it is important to consider
                 the specific computational and analytical challenges that
                 still have to be overcome. Although some tools for analysing
                 RNA-seq data from bulk cell populations can be readily applied
                 to single-cell RNA-seq data, many new computational strategies
                 are required to fully exploit this data type and to enable a
                 comprehensive yet detailed study of gene expression at the
                 single-cell level.",
  journal     = "Nat. Rev. Genet.",
  volume      =  16,
  number      =  3,
  pages       = "133--145",
  month       =  mar,
  year        =  2015
}


@ARTICLE{Levine2015-fk,
  title       = "{Data-Driven} Phenotypic Dissection of {AML} Reveals
                 Progenitor-like Cells that Correlate with Prognosis",
  author      = "Levine, Jacob H and Simonds, Erin F and Bendall, Sean C and
                 Davis, Kara L and Amir, El-Ad D and Tadmor, Michelle D and
                 Litvin, Oren and Fienberg, Harris G and Jager, Astraea and
                 Zunder, Eli R and Finck, Rachel and Gedman, Amanda L and
                 Radtke, Ina and Downing, James R and Pe'er, Dana and Nolan,
                 Garry P",
  affiliation = "Departments of Biological Sciences and Systems Biology,
                 Columbia University, New York, NY 10027, USA. Baxter
                 Laboratory in Stem Cell Biology, Department of Microbiology
                 and Immunology, Stanford University, Stanford, CA 94305, USA.
                 Department of Pathology, Stanford University, Stanford, CA
                 94305, USA. Baxter Laboratory in Stem Cell Biology, Department
                 of Microbiology and Immunology, Stanford University, Stanford,
                 CA 94305, USA. Departments of Biological Sciences and Systems
                 Biology, Columbia University, New York, NY 10027, USA.
                 Departments of Biological Sciences and Systems Biology,
                 Columbia University, New York, NY 10027, USA. Departments of
                 Biological Sciences and Systems Biology, Columbia University,
                 New York, NY 10027, USA. Baxter Laboratory in Stem Cell
                 Biology, Department of Microbiology and Immunology, Stanford
                 University, Stanford, CA 94305, USA. Baxter Laboratory in Stem
                 Cell Biology, Department of Microbiology and Immunology,
                 Stanford University, Stanford, CA 94305, USA. Baxter
                 Laboratory in Stem Cell Biology, Department of Microbiology
                 and Immunology, Stanford University, Stanford, CA 94305, USA.
                 Baxter Laboratory in Stem Cell Biology, Department of
                 Microbiology and Immunology, Stanford University, Stanford, CA
                 94305, USA. Department of Pathology, St. Jude Children's
                 Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105,
                 USA. Department of Pathology, St. Jude Children's Research
                 Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA.
                 Department of Pathology, St. Jude Children's Research
                 Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA.
                 Departments of Biological Sciences and Systems Biology,
                 Columbia University, New York, NY 10027, USA. Electronic
                 address: dpeer@biology.columbia.edu. Baxter Laboratory in Stem
                 Cell Biology, Department of Microbiology and Immunology,
                 Stanford University, Stanford, CA 94305, USA. Electronic
                 address: gnolan@stanford.edu.",
  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.",
  journal     = "Cell",
  volume      =  162,
  number      =  1,
  pages       = "184--197",
  month       =  "2~" # jul,
  year        =  2015,
  language    = "en"
}


@ARTICLE{Tung2017-ba,
  title       = "Batch effects and the effective design of single-cell gene
                 expression studies",
  author      = "Tung, Po-Yuan and Blischak, John D and Hsiao, Chiaowen Joyce
                 and Knowles, David A and Burnett, Jonathan E and Pritchard,
                 Jonathan K and Gilad, Yoav",
  affiliation = "Department of Human Genetics, University of Chicago, Chicago,
                 Illinois, USA. Department of Human Genetics, University of
                 Chicago, Chicago, Illinois, USA. Committee on Genetics,
                 Genomics, and Systems Biology, University of Chicago, Chicago,
                 Illinois, USA. Department of Human Genetics, University of
                 Chicago, Chicago, Illinois, USA. Department of Genetics,
                 Stanford University, Stanford, CA, USA. Department of
                 Radiology, Stanford University, Stanford, CA, USA. Department
                 of Human Genetics, University of Chicago, Chicago, Illinois,
                 USA. Department of Genetics, Stanford University, Stanford,
                 CA, USA. Department of Biology, Stanford University, Stanford,
                 CA, USA. Howard Hughes Medical Institute, Stanford University,
                 CA, USA. Department of Human Genetics, University of Chicago,
                 Chicago, Illinois, USA. Department of Medicine, University of
                 Chicago, Chicago, Illinois, USA.",
  abstract    = "Single-cell RNA sequencing (scRNA-seq) can be used to
                 characterize variation in gene expression levels at high
                 resolution. However, the sources of experimental noise in
                 scRNA-seq are not yet well understood. We investigated the
                 technical variation associated with sample processing using
                 the single-cell Fluidigm C1 platform. To do so, we processed
                 three C1 replicates from three human induced pluripotent stem
                 cell (iPSC) lines. We added unique molecular identifiers
                 (UMIs) to all samples, to account for amplification bias. We
                 found that the major source of variation in the gene
                 expression data was driven by genotype, but we also observed
                 substantial variation between the technical replicates. We
                 observed that the conversion of reads to molecules using the
                 UMIs was impacted by both biological and technical variation,
                 indicating that UMI counts are not an unbiased estimator of
                 gene expression levels. Based on our results, we suggest a
                 framework for effective scRNA-seq studies.",
  journal     = "Sci. Rep.",
  volume      =  7,
  pages       = "39921",
  month       =  "3~" # jan,
  year        =  2017,
  language    = "en"
}

@ARTICLE{Archer2016-zq,
  title       = "Modeling Enzyme Processivity Reveals that {RNA-Seq} Libraries
                 Are Biased in Characteristic and Correctable Ways",
  author      = "Archer, Nathan and Walsh, Mark D and Shahrezaei, Vahid and
                 Hebenstreit, Daniel",
  affiliation = "School of Life Sciences, University of Warwick, Coventry CV4
                 7AL, UK. School of Life Sciences, University of Warwick,
                 Coventry CV4 7AL, UK. Department of Mathematics, Imperial
                 College, London SW7 2AZ, UK. Electronic address:
                 v.shahrezaei@imperial.ac.uk. School of Life Sciences,
                 University of Warwick, Coventry CV4 7AL, UK. Electronic
                 address: d.hebenstreit@warwick.ac.uk.",
  abstract    = "Experimental procedures for preparing RNA-seq and single-cell
                 (sc) RNA-seq libraries are based on assumptions regarding
                 their underlying enzymatic reactions. Here, we show that the
                 fairness of these assumptions varies within libraries:
                 coverage by sequencing reads along and between transcripts
                 exhibits characteristic, protocol-dependent biases. To
                 understand the mechanistic basis of this bias, we present an
                 integrated modeling framework that infers the relationship
                 between enzyme reactions during library preparation and the
                 characteristic coverage patterns observed for different
                 protocols. Analysis of new and existing (sc)RNA-seq data from
                 six different library preparation protocols reveals that
                 polymerase processivity is the mechanistic origin of coverage
                 biases. We apply our framework to demonstrate that lowering
                 incubation temperature increases processivity, yield, and
                 (sc)RNA-seq sensitivity in all protocols. We also provide
                 correction factors based on our model for increasing accuracy
                 of transcript quantification in existing samples prepared at
                 standard temperatures. In total, our findings improve our
                 ability to accurately reflect in vivo transcript abundances in
                 (sc)RNA-seq libraries.",
  journal     = "Cell Syst",
  volume      =  3,
  number      =  5,
  pages       = "467--479.e12",
  month       =  "23~" # nov,
  year        =  2016,
  keywords    = "Bayesian framework; Markov Chain Monte Carlo; RNA-seq; bias;
                 coverage; enzyme; mathematical modeling; polymerase;
                 processivity; reverse transcriptase",
  language    = "en"
}


@ARTICLE{Ziegenhain2017-cu,
  title       = "Comparative Analysis of {Single-Cell} {RNA} Sequencing Methods",
  author      = "Ziegenhain, Christoph and Vieth, Beate and Parekh, Swati and
                 Reinius, Bj{\"o}rn and Guillaumet-Adkins, Amy and Smets,
                 Martha and Leonhardt, Heinrich and Heyn, Holger and Hellmann,
                 Ines and Enard, Wolfgang",
  affiliation = "Anthropology \& Human Genomics, Department of Biology II,
                 Ludwig-Maximilians University, Gro{\ss}haderner Stra{\ss}e 2,
                 82152 Martinsried, Germany. Anthropology \& Human Genomics,
                 Department of Biology II, Ludwig-Maximilians University,
                 Gro{\ss}haderner Stra{\ss}e 2, 82152 Martinsried, Germany.
                 Anthropology \& Human Genomics, Department of Biology II,
                 Ludwig-Maximilians University, Gro{\ss}haderner Stra{\ss}e 2,
                 82152 Martinsried, Germany. Ludwig Institute for Cancer
                 Research, Box 240, 171 77 Stockholm, Sweden; Department of
                 Cell and Molecular Biology, Karolinska Institutet, 171 77
                 Stockholm, Sweden. CNAG-CRG, Centre for Genomic Regulation
                 (CRG), Barcelona Institute of Science and Technology (BIST),
                 08028 Barcelona, Spain; Universitat Pompeu Fabra (UPF), 08002
                 Barcelona, Spain. Department of Biology II and Center for
                 Integrated Protein Science Munich (CIPSM), Ludwig-Maximilians
                 University, Gro{\ss}haderner Stra{\ss}e 2, 82152 Martinsried,
                 Germany. Department of Biology II and Center for Integrated
                 Protein Science Munich (CIPSM), Ludwig-Maximilians University,
                 Gro{\ss}haderner Stra{\ss}e 2, 82152 Martinsried, Germany.
                 CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona
                 Institute of Science and Technology (BIST), 08028 Barcelona,
                 Spain; Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain.
                 Anthropology \& Human Genomics, Department of Biology II,
                 Ludwig-Maximilians University, Gro{\ss}haderner Stra{\ss}e 2,
                 82152 Martinsried, Germany. Anthropology \& Human Genomics,
                 Department of Biology II, Ludwig-Maximilians University,
                 Gro{\ss}haderner Stra{\ss}e 2, 82152 Martinsried, Germany.
                 Electronic address: enard@bio.lmu.de.",
  abstract    = "Single-cell RNA sequencing (scRNA-seq) offers new
                 possibilities to address biological and medical questions.
                 However, systematic comparisons of the performance of diverse
                 scRNA-seq protocols are lacking. We generated data from 583
                 mouse embryonic stem cells to evaluate six prominent scRNA-seq
                 methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq,
                 and Smart-seq2. While Smart-seq2 detected the most genes per
                 cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and
                 SCRB-seq quantified mRNA levels with less amplification noise
                 due to the use of unique molecular identifiers (UMIs). Power
                 simulations at different sequencing depths showed that
                 Drop-seq is more cost-efficient for transcriptome
                 quantification of large numbers of cells, while MARS-seq,
                 SCRB-seq, and Smart-seq2 are more efficient when analyzing
                 fewer cells. Our quantitative comparison offers the basis for
                 an informed choice among six prominent scRNA-seq methods, and
                 it provides a framework for benchmarking further improvements
                 of scRNA-seq protocols.",
  journal     = "Mol. Cell",
  volume      =  65,
  number      =  4,
  pages       = "631--643.e4",
  month       =  "16~" # feb,
  year        =  2017,
  keywords    = "cost-effectiveness; method comparison; power analysis;
                 simulation; single-cell RNA-seq; transcriptomics",
  language    = "en"
}

@ARTICLE{Welch2016-jr,
  title       = "{SLICER: inferring branched, nonlinear cellular trajectories
                   from single cell RNA-seq data}",
		     author      = "Welch, Joshua D and Hartemink, Alexander J and Prins, Jan F",
		       affiliation = "Department of Computer Science, University of North Carolina
                 at Chapel Hill, Chapel Hill, NC, 27599, USA. Curriculum in
                 Bioinformatics and Computational Biology, University of North
                 Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
                 Department of Computer Science, Duke University, Durham, NC,
                 27708, USA. Program in Computational Biology and
                 Bioinformatics, Duke University, Durham, NC, 27708, USA.
                 Department of Computer Science, University of North Carolina
                 at Chapel Hill, Chapel Hill, NC, 27599, USA. prins@cs.unc.edu.
                 Curriculum in Bioinformatics and Computational Biology,
                 University of North Carolina at Chapel Hill, Chapel Hill, NC,
                 27599, USA. prins@cs.unc.edu.",
		   abstract    = "Single cell experiments provide an unprecedented opportunity
                 to reconstruct a sequence of changes in a biological process
                 from individual ``snapshots'' of cells. However, nonlinear
                 gene expression changes, genes unrelated to the process, and
                 the possibility of branching trajectories make this a
                 challenging problem. We develop SLICER (Selective Locally
                 Linear Inference of Cellular Expression Relationships) to
		        address these challenges. SLICER can infer highly nonlinear
				trajectories, select genes without prior knowledge of the
				process, and automatically determine the location and number
				of branches and loops. SLICER recovers the ordering of points
				along simulated trajectories more accurately than existing
				methods. We demonstrate the effectiveness of SLICER on
				previously published data from mouse lung cells and neural stem cells.",
  journal     = "Genome biology",
  volume      =  17,
  number      =  1,
  pages       = "106",
  month       =  "23~" # may,
  year        =  2016,
  url         = "http://dx.doi.org/10.1186/s13059-016-0975-3",
  keywords    = "Manifold learning; Single cell RNA-seq; Time series",
  language    = "en",
  issn        = "1465-6906",
  pmid        = "27215581",
  doi         = "10.1186/s13059-016-0975-3",
  pmc         = "PMC4877799"
}


@ARTICLE{Cannoodt2016-uj,
  title       = "{Computational methods for trajectory inference from
                   single-cell transcriptomics}",
		     author      = "Cannoodt, Robrecht and Saelens, Wouter and Saeys, Yvan",
		       affiliation = "Data Mining and Modelling for Biomedicine group, VIB
                 Inflammation Research Center, Ghent, Belgium. Department of
                 Internal Medicine, Ghent University, Ghent, Belgium. Center
                 for Medical Genetics, Ghent University, Ghent, Belgium. Cancer
                 Research Institute Ghent (CRIG), Ghent, Belgium. Data Mining
                 and Modelling for Biomedicine group, VIB Inflammation Research
		        Center, Ghent, Belgium. Department of Internal Medicine, Ghent
				University, Ghent, Belgium. Data Mining and Modelling for
				Biomedicine group, VIB Inflammation Research Center, Ghent,
				Belgium. yvan.saeys@ugent.be. Department of Internal Medicine,
				Ghent University, Ghent, Belgium. yvan.saeys@ugent.be.",
  abstract    = "Recent developments in single-cell transcriptomics have opened
                new opportunities for studying dynamic processes in immunology
		        in a high throughput and unbiased manner. Starting from a
				mixture of cells in different stages of a developmental
				process, unsupervised trajectory inference algorithms aim to
				automatically reconstruct the underlying developmental path
				that cells are following. In this review, we break down the
				strategies used by this novel class of methods, and organize
				their components into a common framework, highlighting several
				practical advantages and disadvantages of the individual
				methods. We also give an overview of new insights these methods 
				have already providedregarding the wiring and gene regulation 
				of cell differentiation. As the trajectory inference field is 
				still in its infancy, we propose several future developments 
				that will ultimately lead to a global and data-driven way of 
				studying immune cell differentiation.",
  journal     = "European journal of immunology",
  volume      =  46,
  number      =  11,
  pages       = "2496--2506",
  month       =  nov,
  year        =  2016,
  url         = "http://dx.doi.org/10.1002/eji.201646347",
  keywords    = "Bioinformatics; Cell differentiation; Single-cell
                   transcriptomics",
  language    = "en",
  issn        = "0014-2980, 1521-4141",
    pmid        = "27682842",
      doi         = "10.1002/eji.201646347"
}


@ARTICLE{Pollen2014-cu,
  title       = "Low-coverage single-cell {mRNA} sequencing reveals cellular
                 heterogeneity and activated signaling pathways in developing
                 cerebral cortex",
  author      = "Pollen, Alex A and Nowakowski, Tomasz J and Shuga, Joe and
                 Wang, Xiaohui and Leyrat, Anne A and Lui, Jan H and Li,
                 Nianzhen and Szpankowski, Lukasz and Fowler, Brian and Chen,
                 Peilin and Ramalingam, Naveen and Sun, Gang and Thu, Myo and
                 Norris, Michael and Lebofsky, Ronald and Toppani, Dominique
                 and Kemp, 2nd, Darnell W and Wong, Michael and Clerkson, Barry
                 and Jones, Brittnee N and Wu, Shiquan and Knutsson, Lawrence
                 and Alvarado, Beatriz and Wang, Jing and Weaver, Lesley S and
                 May, Andrew P and Jones, Robert C and Unger, Marc A and
                 Kriegstein, Arnold R and West, Jay A A",
  affiliation = "1] Eli and Edythe Broad Center of Regeneration Medicine and
                 Stem Cell Research, University of California, San Francisco,
                 San Francisco, California, USA. [2] Department of Neurology,
                 University of California, San Francisco, San Francisco,
                 California, USA. [3]. 1] Eli and Edythe Broad Center of
                 Regeneration Medicine and Stem Cell Research, University of
                 California, San Francisco, San Francisco, California, USA. [2]
                 Department of Neurology, University of California, San
                 Francisco, San Francisco, California, USA. [3]. 1] Fluidigm
                 Corporation, South San Francisco, California, USA. [2]. 1]
                 Fluidigm Corporation, South San Francisco, California, USA.
                 [2]. Fluidigm Corporation, South San Francisco, California,
                 USA. 1] Eli and Edythe Broad Center of Regeneration Medicine
                 and Stem Cell Research, University of California, San
                 Francisco, San Francisco, California, USA. [2] Department of
                 Neurology, University of California, San Francisco, San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. Fluidigm Corporation, South San
                 Francisco, California, USA. 1] Eli and Edythe Broad Center of
                 Regeneration Medicine and Stem Cell Research, University of
                 California, San Francisco, San Francisco, California, USA. [2]
                 Department of Neurology, University of California, San
                 Francisco, San Francisco, California, USA. Fluidigm
                 Corporation, South San Francisco, California, USA.",
  abstract    = "Large-scale surveys of single-cell gene expression have the
                 potential to reveal rare cell populations and lineage
                 relationships but require efficient methods for cell capture
                 and mRNA sequencing. Although cellular barcoding strategies
                 allow parallel sequencing of single cells at ultra-low depths,
                 the limitations of shallow sequencing have not been
                 investigated directly. By capturing 301 single cells from 11
                 populations using microfluidics and analyzing single-cell
                 transcriptomes across downsampled sequencing depths, we
                 demonstrate that shallow single-cell mRNA sequencing (~50,000
                 reads per cell) is sufficient for unbiased cell-type
                 classification and biomarker identification. In the developing
                 cortex, we identify diverse cell types, including multiple
                 progenitor and neuronal subtypes, and we identify EGR1 and FOS
                 as previously unreported candidate targets of Notch signaling
                 in human but not mouse radial glia. Our strategy establishes
                 an efficient method for unbiased analysis and comparison of
                 cell populations from heterogeneous tissue by microfluidic
                 single-cell capture and low-coverage sequencing of many cells.",
  journal     = "Nat. Biotechnol.",
  volume      =  32,
  number      =  10,
  pages       = "1053--1058",
  month       =  oct,
  year        =  2014
}


@ARTICLE{McCarthy2017-kb,
  title       = "Scater: pre-processing, quality control, normalization and
                 visualization of single-cell {RNA-seq} data in {R}",
  author      = "McCarthy, Davis J and Campbell, Kieran R and Lun, Aaron T L
                 and Wills, Quin F",
  affiliation = "European Molecular Biology Laboratory, European Bioinformatics
                 Institute, Wellcome Genome Campus, CB10 1SD Hinxton,
                 Cambridge, UK. Wellcome Trust Centre for Human Genetics,
                 University of Oxford, Oxford OX3 7BN, UK. St Vincent's
                 Institute of Medical Research, Fitzroy, Victoria 3065,
                 Australia. Wellcome Trust Centre for Human Genetics,
                 University of Oxford, Oxford OX3 7BN, UK. Department of
                 Physiology, Anatomy and Genetics, University of Oxford, Oxford
                 OX1 3QX, UK. CRUK Cambridge Institute, University of
                 Cambridge, Cambridge CB2 0RE, UK. Wellcome Trust Centre for
                 Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
                 Weatherall Institute for Molecular Medicine, University of
                 Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK.",
  abstract    = "MOTIVATION: Single-cell RNA sequencing (scRNA-seq) is
                 increasingly used to study gene expression at the level of
                 individual cells. However, preparing raw sequence data for
                 further analysis is not a straightforward process. Biases,
                 artifacts and other sources of unwanted variation are present
                 in the data, requiring substantial time and effort to be spent
                 on pre-processing, quality control (QC) and normalization.
                 RESULTS: We have developed the R/Bioconductor package scater
                 to facilitate rigorous pre-processing, quality control,
                 normalization and visualization of scRNA-seq data. The package
                 provides a convenient, flexible workflow to process raw
                 sequencing reads into a high-quality expression dataset ready
                 for downstream analysis. scater provides a rich suite of
                 plotting tools for single-cell data and a flexible data
                 structure that is compatible with existing tools and can be
                 used as infrastructure for future software development.
                 AVAILABILITY AND IMPLEMENTATION: The open-source code, along
                 with installation instructions, vignettes and case studies, is
                 available through Bioconductor at
                 http://bioconductor.org/packages/scater CONTACT:
                 davis@ebi.ac.ukSupplementary information: Supplementary data
                 are available at Bioinformatics online.",
  journal     = "Bioinformatics",
  month       =  "14~" # jan,
  year        =  2017,
  language    = "en"
}


@ARTICLE{Svensson2017-op,
  title       = "Power analysis of single-cell {RNA-sequencing} experiments",
  author      = "Svensson, Valentine and Natarajan, Kedar Nath and Ly, Lam-Ha
                 and Miragaia, Ricardo J and Labalette, Charlotte and Macaulay,
                 Iain C and Cvejic, Ana and Teichmann, Sarah A",
  affiliation = "European Molecular Biology Laboratory, European Bioinformatics
                 Institute (EMBL-EBI), Hinxton, Cambridge, UK. Wellcome Trust
                 Sanger Institute, Hinxton, Cambridge, UK. European Molecular
                 Biology Laboratory, European Bioinformatics Institute
                 (EMBL-EBI), Hinxton, Cambridge, UK. Wellcome Trust Sanger
                 Institute, Hinxton, Cambridge, UK. Wellcome Trust Sanger
                 Institute, Hinxton, Cambridge, UK. Wellcome Trust Sanger
                 Institute, Hinxton, Cambridge, UK. Centre of Biological
                 Engineering, University of Minho, Braga, Portugal. Wellcome
                 Trust Sanger Institute, Hinxton, Cambridge, UK. Wellcome
                 Trust-Medical Research Council Cambridge Stem Cell Institute,
                 Cambridge, UK. Department of Haematology, University of
                 Cambridge, Cambridge, UK. Wellcome Trust Sanger Institute,
                 Hinxton, Cambridge, UK. Wellcome Trust Sanger Institute,
                 Hinxton, Cambridge, UK. Wellcome Trust-Medical Research
                 Council Cambridge Stem Cell Institute, Cambridge, UK.
                 Department of Haematology, University of Cambridge, Cambridge,
                 UK. European Molecular Biology Laboratory, European
                 Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.
                 Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.",
  abstract    = "Single-cell RNA sequencing (scRNA-seq) has become an
                 established and powerful method to investigate transcriptomic
                 cell-to-cell variation, thereby revealing new cell types and
                 providing insights into developmental processes and
                 transcriptional stochasticity. A key question is how the
                 variety of available protocols compare in terms of their
                 ability to detect and accurately quantify gene expression.
                 Here, we assessed the protocol sensitivity and accuracy of
                 many published data sets, on the basis of spike-in standards
                 and uniform data processing. For our workflow, we developed a
                 flexible tool for counting the number of unique molecular
                 identifiers (https://github.com/vals/umis/). We compared 15
                 protocols computationally and 4 protocols experimentally for
                 batch-matched cell populations, in addition to investigating
                 the effects of spike-in molecular degradation. Our analysis
                 provides an integrated framework for comparing scRNA-seq
                 protocols.",
  journal     = "Nat. Methods",
  month       =  "6~" # mar,
  year        =  2017,
  language    = "en"
}