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@ARTICLE{Parekh2018-ug,
  title    = "{zUMIs - A fast and flexible pipeline to process RNA sequencing
              data with UMIs}",
  author   = "Parekh, Swati and Ziegenhain, Christoph and Vieth, Beate and
              Enard, Wolfgang and Hellmann, Ines",
  abstract = "Background: Single cell RNA-seq (scRNA-seq) experiments typically
              analyze hundreds or thousands of cells after amplification of the
              cDNA. The high throughput is made possible by the early
              introduction of sample-specific barcodes (BCs) and the
              amplification bias is alleviated by unique molecular identifiers
              (UMIs). Thus the ideal analysis pipeline for scRNA-seq data needs
              to efficiently tabulate reads according to both BC and UMI.
              Findings: zUMIs is a pipeline that can handle both known and
              random BCs and also efficiently collapses UMIs, either just for
              Exon mapping reads or for both Exon and Intron mapping reads. If
              BC annotation is missing, zUMIs can accurately detect intact
              cells from the distribution of sequencing reads. Another unique
              feature of zUMIs is the adaptive downsampling function, that
              facilitates dealing with hugely varying library sizes, but also
              allows to evaluate whether the library has been sequenced to
              saturation. To illustrate the utility of zUMIs, we analysed a
              single-nucleus RNA-seq dataset and show that more than 35\% of
              all reads map to Introns. We furthermore show that these intronic
              reads are informative about expression levels, significantly
              increasing the number of detected genes and improving the cluster
              resolution. Conclusions: zUMIs flexibility allows to accommodate
              data generated with any of the major scRNA-seq protocols that use
              BCs and UMIs and is the most feature-rich, fast and user-friendly
              pipeline to process such scRNA-seq data. Availability:
              https://github.com/sdparekh/zUMIs.",
  journal  = "Gigascience",
  month    =  may,
  year     =  2018,
  url      = "http://dx.doi.org/10.1093/gigascience/giy059",
  keywords = "
              single-cell;rna-seq;methods;UMI;software;[zUMIs];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "2047-217X",
  pmid     = "29846586",
  doi      = "10.1093/gigascience/giy059"
}

@ARTICLE{Butler2018-js,
  title    = "{Integrating single-cell transcriptomic data across different
              conditions, technologies, and species}",
  author   = "Butler, Andrew and Hoffman, Paul and Smibert, Peter and Papalexi,
              Efthymia and Satija, Rahul",
  abstract = "Computational single-cell RNA-seq (scRNA-seq) methods have been
              successfully applied to experiments representing a single
              condition, technology, or species to discover and define cellular
              phenotypes. However, identifying subpopulations of cells that are
              present across multiple data sets remains challenging. Here, we
              introduce an analytical strategy for integrating scRNA-seq data
              sets based on common sources of variation, enabling the
              identification of shared populations across data sets and
              downstream comparative analysis. We apply this approach,
              implemented in our R toolkit Seurat
              (http://satijalab.org/seurat/), to align scRNA-seq data sets of
              peripheral blood mononuclear cells under resting and stimulated
              conditions, hematopoietic progenitors sequenced using two
              profiling technologies, and pancreatic cell 'atlases' generated
              from human and mouse islets. In each case, we learn distinct or
              transitional cell states jointly across data sets, while boosting
              statistical power through integrated analysis. Our approach
              facilitates general comparisons of scRNA-seq data sets,
              potentially deepening our understanding of how distinct cell
              states respond to perturbation, disease, and evolution.",
  journal  = "Nat. Biotechnol.",
  month    =  apr,
  year     =  2018,
  url      = "http://dx.doi.org/10.1038/nbt.4096",
  keywords = "single-cell;rna-seq;methods;software;batch
              effects;[Seurat];UNREAD;CITED;scRNA-tools paper",
  language = "en",
  issn     = "1087-0156, 1546-1696",
  pmid     = "29608179",
  doi      = "10.1038/nbt.4096"
}

@ARTICLE{Bourne2017-fc,
  title    = "{Ten simple rules to consider regarding preprint submission}",
  author   = "Bourne, Philip E and Polka, Jessica K and Vale, Ronald D and
              Kiley, Robert",
  journal  = "PLoS Comput. Biol.",
  volume   =  13,
  number   =  5,
  pages    = "e1005473",
  month    =  may,
  year     =  2017,
  url      = "http://dx.doi.org/10.1371/journal.pcbi.1005473",
  keywords = "preprints;10SR;CITED;scRNA-tools paper",
  language = "en",
  issn     = "1553-734X, 1553-7358",
  pmid     = "28472041",
  doi      = "10.1371/journal.pcbi.1005473",
  pmc      = "PMC5417409"
}

@MISC{Sievert2017-am,
  title    = "{plotly: Create Interactive Web Graphics via 'plotly.js'}",
  author   = "Sievert, Carson and Parmer, Chris and Hocking, Toby and
              Chamberlain, Scott and Ram, Karthik and Corvellec, Marianne and
              Despouy, Pedro",
  year     =  2017,
  url      = "https://CRAN.R-project.org/package=plotly",
  keywords = "software;[plotly];CITED;scRNA-tools paper"
}

@MISC{Ram2017-mn,
  title    = "{aRxiv: Interface to the arXiv API}",
  author   = "Ram, Karthik and Broman, Karl",
  year     =  2017,
  url      = "https://CRAN.R-project.org/package=aRxiv",
  keywords = "software;[aRxiv];CITED;scRNA-tools paper"
}

@ARTICLE{Vieth2017-vc,
  title    = "{powsimR: power analysis for bulk and single cell RNA-seq
              experiments}",
  author   = "Vieth, Beate and Ziegenhain, Christoph and Parekh, Swati and
              Enard, Wolfgang and Hellmann, Ines",
  abstract = "Summary: Power analysis is essential to optimize the design of
              RNA-seq experiments and to assess and compare the power to detect
              differentially expressed genes in RNA-seq data. PowsimR is a
              flexible tool to simulate and evaluate differential expression
              from bulk and especially single-cell RNA-seq data making it
              suitable for a priori and posterior power analyses. Availability
              and implementation: The R package and associated tutorial are
              freely available at https://github.com/bvieth/powsimR. Contact:
              vieth@bio.lmu.de or hellmann@bio.lmu.de. Supplementary
              information: Supplementary data are available at Bioinformatics
              online.",
  journal  = "Bioinformatics",
  volume   =  33,
  number   =  21,
  pages    = "3486--3488",
  month    =  nov,
  year     =  2017,
  url      = "http://dx.doi.org/10.1093/bioinformatics/btx435",
  keywords = "single-cell;rna-seq;methods;software;power
              analysis;[Powsim];simulation;UNREAD;CITED;scRNA-tools paper",
  language = "en",
  issn     = "1367-4803, 1367-4811",
  pmid     = "29036287",
  doi      = "10.1093/bioinformatics/btx435"
}

@ARTICLE{Svensson2018-dx,
  title     = "{Exponential scaling of single-cell RNA-seq in the past decade}",
  author    = "Svensson, Valentine and Vento-Tormo, Roser and Teichmann, Sarah
               A",
  abstract  = "Measurement of the transcriptomes of single cells has been
               feasible for only a few years, but it has become an extremely
               popular assay. While many types of analysis can be carried out
               and various questions can be answered by single-cell RNA-seq, a
               central focus is the ability to survey the diversity of cell
               types in a sample. Unbiased and reproducible cataloging of gene
               expression patterns in distinct cell types requires large
               numbers of cells. Technological developments and protocol
               improvements have fueled consistent and exponential increases in
               the number of cells that can be studied in single-cell RNA-seq
               analyses. In this Perspective, we highlight the key
               technological developments that have enabled this growth in the
               data obtained from single-cell RNA-seq experiments.",
  journal   = "Nat. Protoc.",
  publisher = "Nature Publishing Group",
  volume    =  13,
  pages     = "599",
  month     =  mar,
  year      =  2018,
  url       = "http://dx.doi.org/10.1038/nprot.2017.149",
  keywords  = "single-cell;review;rna-seq;UNREAD;CITED;scRNA-tools paper",
  issn      = "1754-2189",
  doi       = "10.1038/nprot.2017.149"
}

@ARTICLE{Soneson2018-fb,
  title    = "{Bias, robustness and scalability in single-cell differential
              expression analysis}",
  author   = "Soneson, Charlotte and Robinson, Mark D",
  abstract = "Many methods have been used to determine differential gene
              expression from single-cell RNA (scRNA)-seq data. We evaluated 36
              approaches using experimental and synthetic data and found
              considerable differences in the number and characteristics of the
              genes that are called differentially expressed. Prefiltering of
              lowly expressed genes has important effects, particularly for
              some of the methods developed for bulk RNA-seq data analysis.
              However, we found that bulk RNA-seq analysis methods do not
              generally perform worse than those developed specifically for
              scRNA-seq. We also present conquer, a repository of consistently
              processed, analysis-ready public scRNA-seq data sets that is
              aimed at simplifying method evaluation and reanalysis of
              published results. Each data set provides abundance estimates for
              both genes and transcripts, as well as quality control and
              exploratory analysis reports.",
  journal  = "Nat. Methods",
  month    =  feb,
  year     =  2018,
  url      = "http://dx.doi.org/10.1038/nmeth.4612",
  keywords = "single-cell;rna-seq;differential
              expression;comparison;[conquer];filtering;UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "29481549",
  doi      = "10.1038/nmeth.4612"
}

@ARTICLE{Wolf2018-na,
  title    = "{SCANPY: large-scale single-cell gene expression data analysis}",
  author   = "Wolf, F Alexander and Angerer, Philipp and Theis, Fabian J",
  abstract = "SCANPY is a scalable toolkit for analyzing single-cell gene
              expression data. It includes methods for preprocessing,
              visualization, clustering, pseudotime and trajectory inference,
              differential expression testing, and simulation of gene
              regulatory networks. Its Python-based implementation efficiently
              deals with data sets of more than one million cells (
              https://github.com/theislab/Scanpy ). Along with SCANPY, we
              present ANNDATA, a generic class for handling annotated data
              matrices ( https://github.com/theislab/anndata ).",
  journal  = "Genome Biol.",
  volume   =  19,
  number   =  1,
  pages    = "15",
  month    =  feb,
  year     =  2018,
  url      = "http://dx.doi.org/10.1186/s13059-017-1382-0",
  keywords = "Bioinformatics; Clustering; Differential expression testing;
              Graph analysis; Machine learning; Pseudotemporal ordering;
              Scalability; Single-cell transcriptomics; Trajectory inference;
              Visualization;[Scanpy];single-cell;rna-seq;methods;quality
              control;lineage;clustering;simulation;software;differential
              expression;UNREAD;CITED;scRNA-tools paper",
  language = "en",
  issn     = "1465-6906",
  pmid     = "29409532",
  doi      = "10.1186/s13059-017-1382-0"
}

@MISC{Wilke2017-zt,
  title    = "{cowplot: Streamlined Plot Theme and Plot Annotations for
              'ggplot2'}",
  author   = "Wilke, Claus O",
  year     =  2017,
  url      = "https://CRAN.R-project.org/package=cowplot",
  keywords = "software;[cowplot];CITED;scRNA-tools paper"
}

@MISC{Wickham2017-ip,
  title    = "{dplyr: A Grammar of Data Manipulation}",
  author   = "Wickham, Hadley and Francois, Romain and Henry, Lionel and
              M{\"u}ller, Kirill",
  year     =  2017,
  url      = "https://CRAN.R-project.org/package=dplyr",
  keywords = "software;[dplyr];CITED;scRNA-tools paper"
}

@MANUAL{Chamberlain2017-ov,
  title    = "{rcrossref: Client for Various 'CrossRef' 'APIs'}",
  author   = "Chamberlain, Scott and Boettiger, Carl and Hart, Ted and Ram,
              Karthik",
  year     =  2017,
  url      = "https://CRAN.R-project.org/package=rcrossref",
  keywords = "software;[rcrossref];CITED;scRNA-tools paper"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Canzar2017-jb,
  title    = "{BASIC: BCR assembly from single cells}",
  author   = "Canzar, Stefan and Neu, Karlynn E and Tang, Qingming and Wilson,
              Patrick C and Khan, Aly A",
  abstract = "Motivation: The B-cell receptor enables individual B cells to
              identify diverse antigens, including bacterial and viral
              proteins. While advances in RNA-sequencing (RNA-seq) have enabled
              high throughput profiling of transcript expression in single
              cells, the unique task of assembling the full-length heavy and
              light chain sequences from single cell RNA-seq (scRNA-seq) in B
              cells has been largely unstudied. Results: We developed a new
              software tool, BASIC, which allows investigators to use scRNA-seq
              for assembling BCR sequences at single-cell resolution. To
              demonstrate the utility of our software, we subjected nearly 200
              single human B cells to scRNA-seq, assembled the full-length
              heavy and the light chains, and experimentally confirmed these
              results by using single-cell primer-based nested PCRs and Sanger
              sequencing. Availability and Implementation:
              http://ttic.uchicago.edu/∼aakhan/BASIC Contact: aakhan@ttic.edu
              Supplementary Information: Supplementary data are available at
              Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  33,
  number   =  3,
  pages    = "425--427",
  month    =  feb,
  year     =  2017,
  url      = "http://dx.doi.org/10.1093/bioinformatics/btw631",
  keywords = "
              single-cell;rna-seq;immune;methods;software;[BASIC];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1367-4803, 1367-4811",
  pmid     = "28172415",
  doi      = "10.1093/bioinformatics/btw631",
  pmc      = "PMC5408917"
}

@MISC{Lun2017-jj,
  title    = "{SingleCellExperiment: S4 Classes for Single Cell Data}",
  author   = "Lun, Aaron and Risso, Davide",
  year     =  2017,
  keywords = "single-cell;software;[SingleCellExperiment];CITED;scRNA-tools
              paper"
}

@ARTICLE{Zhao2017-ju,
  title    = "{Dr.seq2: A quality control and analysis pipeline for parallel
              single cell transcriptome and epigenome data}",
  author   = "Zhao, Chengchen and Hu, Sheng'en and Huo, Xiao and Zhang, Yong",
  abstract = "An increasing number of single cell transcriptome and epigenome
              technologies, including single cell ATAC-seq (scATAC-seq), have
              been recently developed as powerful tools to analyze the features
              of many individual cells simultaneously. However, the methods and
              software were designed for one certain data type and only for
              single cell transcriptome data. A systematic approach for
              epigenome data and multiple types of transcriptome data is needed
              to control data quality and to perform cell-to-cell heterogeneity
              analysis on these ultra-high-dimensional transcriptome and
              epigenome datasets. Here we developed Dr.seq2, a Quality Control
              (QC) and analysis pipeline for multiple types of single cell
              transcriptome and epigenome data, including scATAC-seq and
              Drop-ChIP data. Application of this pipeline provides four groups
              of QC measurements and different analyses, including cell
              heterogeneity analysis. Dr.seq2 produced reliable results on
              published single cell transcriptome and epigenome datasets.
              Overall, Dr.seq2 is a systematic and comprehensive QC and
              analysis pipeline designed for parallel single cell transcriptome
              and epigenome data. Dr.seq2 is freely available at:
              http://www.tongji.edu.cn/~zhanglab/drseq2/ and
              https://github.com/ChengchenZhao/DrSeq2.",
  journal  = "PLoS One",
  volume   =  12,
  number   =  7,
  pages    = "e0180583",
  month    =  jul,
  year     =  2017,
  url      = "http://dx.doi.org/10.1371/journal.pone.0180583",
  keywords = "
              single-cell;rna-seq;atac-seq;chip-seq;methods;software;quantification;quality
              control;[DrSeq2];UNREAD;CITED;scRNA-tools paper",
  language = "en",
  issn     = "1932-6203",
  pmid     = "28671995",
  doi      = "10.1371/journal.pone.0180583",
  pmc      = "PMC5495495"
}

@ARTICLE{Zappia2017-sg,
  title    = "{Splatter: simulation of single-cell RNA sequencing data}",
  author   = "Zappia, Luke and Phipson, Belinda and Oshlack, Alicia",
  abstract = "As single-cell RNA sequencing (scRNA-seq) technologies have
              rapidly developed, so have analysis methods. Many methods have
              been tested, developed, and validated using simulated datasets.
              Unfortunately, current simulations are often poorly documented,
              their similarity to real data is not demonstrated, or
              reproducible code is not available. Here, we present the Splatter
              Bioconductor package for simple, reproducible, and
              well-documented simulation of scRNA-seq data. Splatter provides
              an interface to multiple simulation methods including Splat, our
              own simulation, based on a gamma-Poisson distribution. Splat can
              simulate single populations of cells, populations with multiple
              cell types, or differentiation paths.",
  journal  = "Genome Biol.",
  volume   =  18,
  number   =  1,
  pages    = "174",
  month    =  sep,
  year     =  2017,
  url      = "https://doi.org/10.1186/s13059-017-1305-0",
  keywords = "
              single-cell;rna-seq;methods;software;simulation;[Splatter];ME;CITED;scRNA-tools
              paper;Second year review",
  issn     = "1465-6906, 1474-760X",
  doi      = "10.1186/s13059-017-1305-0"
}

@ARTICLE{Ritchie2015-te,
  title    = "{limma powers differential expression analyses for RNA-sequencing
              and microarray studies}",
  author   = "Ritchie, Matthew E and Phipson, Belinda and Wu, Di and Hu, Yifang
              and Law, Charity W and Shi, Wei and Smyth, Gordon K",
  abstract = "limma is an R/Bioconductor software package that provides an
              integrated solution for analysing data from gene expression
              experiments. It contains rich features for handling complex
              experimental designs and for information borrowing to overcome
              the problem of small sample sizes. Over the past decade, limma
              has been a popular choice for gene discovery through differential
              expression analyses of microarray and high-throughput PCR data.
              The package contains particularly strong facilities for reading,
              normalizing and exploring such data. Recently, the capabilities
              of limma have been significantly expanded in two important
              directions. First, the package can now perform both differential
              expression and differential splicing analyses of RNA sequencing
              (RNA-seq) data. All the downstream analysis tools previously
              restricted to microarray data are now available for RNA-seq as
              well. These capabilities allow users to analyse both RNA-seq and
              microarray data with very similar pipelines. Second, the package
              is now able to go past the traditional gene-wise expression
              analyses in a variety of ways, analysing expression profiles in
              terms of co-regulated sets of genes or in terms of higher-order
              expression signatures. This provides enhanced possibilities for
              biological interpretation of gene expression differences. This
              article reviews the philosophy and design of the limma package,
              summarizing both new and historical features, with an emphasis on
              recent enhancements and features that have not been previously
              described.",
  journal  = "Nucleic Acids Res.",
  volume   =  43,
  number   =  7,
  pages    = "e47",
  month    =  apr,
  year     =  2015,
  url      = "http://dx.doi.org/10.1093/nar/gkv007",
  keywords = "rna-seq;differential
              expression;methods;microarray;software;[limma];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "0305-1048, 1362-4962",
  pmid     = "25605792",
  doi      = "10.1093/nar/gkv007",
  pmc      = "PMC4402510"
}

@ARTICLE{Afik2017-qw,
  title    = "{Targeted reconstruction of T cell receptor sequence from single
              cell RNA-seq links CDR3 length to T cell differentiation state}",
  author   = "Afik, Shaked and Yates, Kathleen B and Bi, Kevin and Darko,
              Samuel and Godec, Jernej and Gerdemann, Ulrike and Swadling, Leo
              and Douek, Daniel C and Klenerman, Paul and Barnes, Eleanor J and
              Sharpe, Arlene H and Haining, W Nicholas and Yosef, Nir",
  abstract = "The T cell compartment must contain diversity in both T cell
              receptor (TCR) repertoire and cell state to provide effective
              immunity against pathogens. However, it remains unclear how
              differences in the TCR contribute to heterogeneity in T cell
              state. Single cell RNA-sequencing (scRNA-seq) can allow
              simultaneous measurement of TCR sequence and global
              transcriptional profile from single cells. However, current
              methods for TCR inference from scRNA-seq are limited in their
              sensitivity and require long sequencing reads, thus increasing
              the cost and decreasing the number of cells that can be feasibly
              analyzed. Here we present TRAPeS, a publicly available tool that
              can efficiently extract TCR sequence information from short-read
              scRNA-seq libraries. We apply it to investigate heterogeneity in
              the CD8+ T cell response in humans and mice, and show that it is
              accurate and more sensitive than existing approaches. Coupling
              TRAPeS with transcriptome analysis of CD8+ T cells specific for a
              single epitope from Yellow Fever Virus (YFV), we show that the
              recently described `naive-like' memory population have
              significantly longer CDR3 regions and greater divergence from
              germline sequence than do effector-memory phenotype cells. This
              suggests that TCR usage is associated with the differentiation
              state of the CD8+ T cell response to YFV.",
  journal  = "Nucleic Acids Res.",
  month    =  jul,
  year     =  2017,
  url      = "https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx615",
  keywords = "libraries; t-cell receptor; reconstructive surgical procedures;
              rna, small cytoplasmic; sequence analysis, rna; t-lymphocyte;
              phenotype; transcription, genetic; heterogeneity; epitopes; gene
              expression profiling; rna; inference; t cell differentiation;
              donors; memory; mice; yellow fever virus; pathogenic organism;
              immunity; genes;
              cytomegalovirus;single-cell;rna-seq;methods;immune;software;[TRAPeS];UNREAD;CITED;scRNA-tools
              paper",
  issn     = "0305-1048",
  doi      = "10.1093/nar/gkx615"
}

@ARTICLE{Stubbington2016-xw,
  title    = "{T cell fate and clonality inference from single-cell
              transcriptomes}",
  author   = "Stubbington, Michael J T and L{\"o}nnberg, Tapio and Proserpio,
              Valentina and Clare, Simon and Speak, Anneliese O and Dougan,
              Gordon and Teichmann, Sarah A",
  abstract = "We developed TraCeR, a computational method to reconstruct
              full-length, paired T cell receptor (TCR) sequences from T
              lymphocyte single-cell RNA sequence data. TraCeR links T cell
              specificity with functional response by revealing clonal
              relationships between cells alongside their transcriptional
              profiles. We found that T cell clonotypes in a mouse Salmonella
              infection model span early activated CD4(+) T cells as well as
              mature effector and memory cells.",
  journal  = "Nat. Methods",
  volume   =  13,
  number   =  4,
  pages    = "329--332",
  month    =  apr,
  year     =  2016,
  url      = "http://dx.doi.org/10.1038/nmeth.3800",
  keywords = "
              single-cell;rna-seq;methods;software;immune;[TraCeR];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "26950746",
  doi      = "10.1038/nmeth.3800",
  pmc      = "PMC4835021"
}

@ARTICLE{Qiu2017-mq,
  title    = "{Reversed graph embedding resolves complex single-cell
              trajectories}",
  author   = "Qiu, Xiaojie and Mao, Qi and Tang, Ying and Wang, Li and Chawla,
              Raghav and Pliner, Hannah A and Trapnell, Cole",
  abstract = "Single-cell trajectories can unveil how gene regulation governs
              cell fate decisions. However, learning the structure of complex
              trajectories with multiple branches remains a challenging
              computational problem. We present Monocle 2, an algorithm that
              uses reversed graph embedding to describe multiple fate decisions
              in a fully unsupervised manner. We applied Monocle 2 to two
              studies of blood development and found that mutations in the
              genes encoding key lineage transcription factors divert cells to
              alternative fates.",
  journal  = "Nat. Methods",
  month    =  aug,
  year     =  2017,
  url      = "http://dx.doi.org/10.1038/nmeth.4402",
  keywords = "
              single-cell;rna-seq;methods;lineage;software;[monocle];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "28825705",
  doi      = "10.1038/nmeth.4402"
}

@ARTICLE{Miragaia2017-tq,
  title    = "{Single-cell insights into transcriptomic diversity in immunity}",
  author   = "Miragaia, Ricardo J and Teichmann, Sarah A and Hagai, Tzachi",
  abstract = "Our body is composed of a diverse set of cells that together
              perform numerous functions in a highly coordinated manner,
              allowing a timely response to external cues and to inter-cellular
              signals. Emerging single-cell technologies enable investigation
              of these intricate processes at an unprecedented level. Here, we
              discuss recent work on cellular differentiation and
              heterogeneity, and describe novel experimental and computational
              tools that enable this research. As an example of cellular
              differentiation, we focus on T cell development in response to
              diverse infections and in immune pathologies. We then describe
              how single-cell studies have contributed to our understanding of
              transcriptional variability in innate immune response, and how
              this variability might be important in achieving a balanced
              immune response. Future single-cell studies will likely include
              spatial analyses and lineage tracing strategies, holding great
              potential to further our understanding of cell behaviour in
              steady-state and pathological conditions.",
  journal  = "Current Opinion in Systems Biology",
  volume   =  5,
  pages    = "63--71",
  month    =  oct,
  year     =  2017,
  url      = "http://www.sciencedirect.com/science/article/pii/S2452310017301282",
  keywords = "Single-cell; Pseudotime; Cell heterogeneity; Cell
              differentiation;
              Immunity;single-cell;rna-seq;immune;review;UNREAD;CITED;scRNA-tools
              paper;Reviews",
  issn     = "2452-3100",
  doi      = "10.1016/j.coisb.2017.08.003"
}

@UNPUBLISHED{Petukhov2017-lm,
  title    = "{Accurate estimation of molecular counts in droplet-based
              single-cell RNA-seq experiments}",
  author   = "Petukhov, Viktor and Guo, Jimin and Baryawno, Ninib and Severe,
              Nicolas and Scadden, David and Kharchenko, Peter V",
  abstract = "Single-cell RNA-seq protocols provide powerful means for
              examining the gamut of cell types and transcriptional states that
              comprise complex biological tissues. Recently-developed
              approaches based on droplet microfluidics, such as inDrop or
              Drop-seq, use massively multiplexed barcoding to enable
              simultaneous measurements of transcriptomes for thousands of
              individual cells. The increasing complexity of such data also
              creates challenges for subsequent computational processing and
              troubleshooting of these experiments, with few software options
              currently available. Here we describe a flexible pipeline for
              processing droplet-based transcriptome data that implements
              barcode corrections, classification of cell quality, and
              diagnostic information about the droplet libraries. We introduce
              advanced methods for correcting composition bias and sequencing
              errors affecting cellular and molecular barcodes to provide more
              accurate estimates of molecular counts in individual cells.",
  journal  = "bioRxiv",
  pages    = "171496",
  month    =  aug,
  year     =  2017,
  url      = "http://www.biorxiv.org/content/early/2017/08/02/171496?ct",
  keywords = "
              single-cell;rna-seq;quantification;UMI;methods;software;[dropEst];quality
              control;UNREAD;PREPRINT;CITED;scRNA-tools paper",
  language = "en",
  doi      = "10.1101/171496"
}

@UNPUBLISHED{Tian2017-zl,
  title    = "{scPipe: a flexible data preprocessing pipeline for single-cell
              RNA-sequencing data}",
  author   = "Tian, Luyi and Su, Shian and Amann-Zalcenstein, Daniela and
              Biben, Christine and Naik, Shalin H and Ritchie, Matthew E",
  abstract = "Single-cell RNA sequencing (scRNA-seq) technology allows
              researchers to profile the transcriptomes of thousands of cells
              simultaneously. Protocols that incorporate both designed and
              random barcodes to label individual cells and molecules have
              greatly increased the throughput of scRNA-seq, but give rise to a
              more complex data structure. There is a need for new tools that
              can handle the various barcodes used by different protocols and
              exploit this information for quality assessment at the
              sample-level and provide effective visualization of these results
              in preparation for higher-level analyses. We developed scPipe, an
              R package that allows barcode demultiplexing, read alignment and
              gene-level quantification and quality control of raw sequencing
              data generated by multiple 3 prime end sequencing protocols that
              include CEL-seq, MARS-seq and Drop-seq. scPipe produces a count
              matrix that is essential for downstream analysis along with an
              HTML report that summarises data quality. These results can be
              used as input for downstream analyses including normalization,
              visualization and statistical testing. scPipe performs this
              processing in a few simple R commands, promoting reproducible
              analysis of single-cell data that is compatible with the emerging
              suite of scRNA-seq analysis tools available in R/Bioconductor.
              The scPipe R package is available from
              https://github.com/LuyiTian/scPipe.",
  journal  = "bioRxiv",
  pages    = "175927",
  month    =  aug,
  year     =  2017,
  url      = "http://www.biorxiv.org/content/early/2017/08/14/175927",
  keywords = "single-cell;rna-seq;pipeline;UMI;quality
              control;software;[scPipe];UNREAD;PREPRINT;CITED;scRNA-tools paper",
  language = "en",
  doi      = "10.1101/175927"
}

@ARTICLE{Wagner2016-zm,
  title    = "{Revealing the vectors of cellular identity with single-cell
              genomics}",
  author   = "Wagner, Allon and Regev, Aviv and Yosef, Nir",
  abstract = "Single-cell genomics has now made it possible to create a
              comprehensive atlas of human cells. At the same time, it has
              reopened definitions of a cell's identity and of the ways in
              which identity is regulated by the cell's molecular circuitry.
              Emerging computational analysis methods, especially in
              single-cell RNA sequencing (scRNA-seq), have already begun to
              reveal, in a data-driven way, the diverse simultaneous facets of
              a cell's identity, from discrete cell types to continuous dynamic
              transitions and spatial locations. These developments will
              eventually allow a cell to be represented as a superposition of
              'basis vectors', each determining a different (but possibly
              dependent) aspect of cellular organization and function. However,
              computational methods must also overcome considerable
              challenges-from handling technical noise and data scale to
              forming new abstractions of biology. As the scale of single-cell
              experiments continues to increase, new computational approaches
              will be essential for constructing and characterizing a reference
              map of cell identities.",
  journal  = "Nat. Biotechnol.",
  volume   =  34,
  number   =  11,
  pages    = "1145--1160",
  month    =  nov,
  year     =  2016,
  url      = "http://dx.doi.org/10.1038/nbt.3711",
  keywords = "single-cell;rna-seq;methylation;review;UNREAD;CITED;scRNA-tools
              paper;Reviews",
  language = "en",
  issn     = "1087-0156, 1546-1696",
  pmid     = "27824854",
  doi      = "10.1038/nbt.3711",
  pmc      = "PMC5465644"
}

@UNPUBLISHED{Butler2017-lb,
  title    = "{Integrated analysis of single cell transcriptomic data across
              conditions, technologies, and species}",
  author   = "Butler, Andrew and Satija, Rahul",
  abstract = "Single cell RNA-seq (scRNA-seq) has emerged as a transformative
              tool to discover and define cellular phenotypes. While
              computational scRNA-seq methods are currently well suited for
              experiments representing a single condition, technology, or
              species, analyzing multiple datasets simultaneously raises new
              challenges. In particular, traditional analytical workflows
              struggle to align subpopulations that are present across
              datasets, limiting the possibility for integrated or comparative
              analysis. Here, we introduce a new computational strategy for
              scRNA-seq alignment, utilizing common sources of variation to
              identify shared subpopulations between datasets as part of our R
              toolkit Seurat. We demonstrate our approach by aligning scRNA-seq
              datasets of PBMCs under resting and stimulated conditions,
              hematopoietic progenitors sequenced across two profiling
              technologies, and pancreatic cell 9atlases9 generated from human
              and mouse islets. In each case, we learn distinct or transitional
              cell states jointly across datasets, and can identify
              subpopulations that could not be detected by analyzing datasets
              independently. We anticipate that these methods will serve not
              only to correct for batch or technology-dependent effects, but
              also to facilitate general comparisons of scRNA-seq datasets,
              potentially deepening our understanding of how distinct cell
              states respond to perturbation, disease, and evolution.
              Availability: Installation instructions, documentation, and
              tutorials are available at http://www.satijalab.org/seurat",
  journal  = "bioRxiv",
  pages    = "164889",
  month    =  jul,
  year     =  2017,
  url      = "http://www.biorxiv.org/content/early/2017/07/18/164889",
  keywords = "single-cell;rna-seq;methods;software;batch
              effects;[Seurat];PREPRINT;CITED;scRNA-tools paper",
  language = "en",
  doi      = "10.1101/164889"
}

@ARTICLE{Smith2017-nz,
  title    = "{UMI-tools: modeling sequencing errors in Unique Molecular
              Identifiers to improve quantification accuracy}",
  author   = "Smith, Tom and Heger, Andreas and Sudbery, Ian",
  abstract = "Unique Molecular Identifiers (UMIs) are random oligonucleotide
              barcodes that are increasingly used in high-throughput sequencing
              experiments. Through a UMI, identical copies arising from
              distinct molecules can be distinguished from those arising
              through PCR amplification of the same molecule. However,
              bioinformatic methods to leverage the information from UMIs have
              yet to be formalized. In particular, sequencing errors in the UMI
              sequence are often ignored or else resolved in an ad hoc manner.
              We show that errors in the UMI sequence are common and introduce
              network-based methods to account for these errors when
              identifying PCR duplicates. Using these methods, we demonstrate
              improved quantification accuracy both under simulated conditions
              and real iCLIP and single-cell RNA-seq data sets. Reproducibility
              between iCLIP replicates and single-cell RNA-seq clustering are
              both improved using our proposed network-based method,
              demonstrating the value of properly accounting for errors in
              UMIs. These methods are implemented in the open source UMI-tools
              software package.",
  journal  = "Genome Res.",
  volume   =  27,
  number   =  3,
  pages    = "491--499",
  month    =  mar,
  year     =  2017,
  url      = "http://dx.doi.org/10.1101/gr.209601.116",
  keywords = "
              single-cell;rna-seq;methods;software;UMI;[UMI-tools];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1088-9051, 1549-5469",
  pmid     = "28100584",
  doi      = "10.1101/gr.209601.116",
  pmc      = "PMC5340976"
}

@ARTICLE{Huber2015-lz,
  title    = "{Orchestrating high-throughput genomic analysis with Bioconductor}",
  author   = "Huber, Wolfgang and Carey, Vincent J and Gentleman, Robert and
              Anders, Simon and Carlson, Marc and Carvalho, Benilton S and
              Bravo, Hector Corrada and Davis, Sean and Gatto, Laurent and
              Girke, Thomas and Gottardo, Raphael and Hahne, Florian and
              Hansen, Kasper D and Irizarry, Rafael A and Lawrence, Michael and
              Love, Michael I and MacDonald, James and Obenchain, Valerie and
              Ole{\'s}, Andrzej K and Pag{\`e}s, Herv{\'e} and Reyes, Alejandro
              and Shannon, Paul and Smyth, Gordon K and Tenenbaum, Dan and
              Waldron, Levi and Morgan, Martin",
  abstract = "Bioconductor is an open-source, open-development software project
              for the analysis and comprehension of high-throughput data in
              genomics and molecular biology. The project aims to enable
              interdisciplinary research, collaboration and rapid development
              of scientific software. Based on the statistical programming
              language R, Bioconductor comprises 934 interoperable packages
              contributed by a large, diverse community of scientists. Packages
              cover a range of bioinformatic and statistical applications. They
              undergo formal initial review and continuous automated testing.
              We present an overview for prospective users and contributors.",
  journal  = "Nat. Methods",
  volume   =  12,
  number   =  2,
  pages    = "115--121",
  month    =  feb,
  year     =  2015,
  url      = "http://dx.doi.org/10.1038/nmeth.3252",
  keywords = "software;[Bioconductor];UNREAD;CITED;Splatter Paper;scRNA-tools
              paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "25633503",
  doi      = "10.1038/nmeth.3252",
  pmc      = "PMC4509590"
}

@ARTICLE{Song2017-ym,
  title    = "{Single-cell alternative splicing analysis with Expedition reveals
              splicing dynamics during neuron differentiation}",
  author   = "Song, Yan and Botvinnik, Olga B and Lovci, Michael T and
              Kakaradov, Boyko and Liu, Patrick and Xu, Jia L and Yeo, Gene W",
  abstract = "Alternative splicing (AS) generates isoform diversity for
              cellular identity and homeostasis in multicellular life. Although
              AS variation has been observed among single cells, little is
              known about the biological or evolutionary significance of such
              variation. We developed Expedition, a computational framework
              consisting of outrigger, a de novo splice graph transversal
              algorithm to detect AS; anchor, a Bayesian approach to assign
              modalities; and bonvoyage, a visualization tool using
              non-negative matrix factorization to display modality changes.
              Applying Expedition to single pluripotent stem cells undergoing
              neuronal differentiation, we discover that up to 20\% of AS exons
              exhibit bimodality. Bimodal exons are flanked by more conserved
              intronic sequences harboring distinct cis-regulatory motifs,
              constitute much of cell-type-specific splicing, are highly
              dynamic during cellular transitions, preserve reading frame, and
              reveal intricacy of cell states invisible to conventional gene
              expression analysis. Systematic AS characterization in single
              cells redefines our understanding of AS complexity in cell
              biology.",
  journal  = "Mol. Cell",
  month    =  jun,
  year     =  2017,
  url      = "http://dx.doi.org/10.1016/j.molcel.2017.06.003",
  keywords = "RNA processing; alternative splicing; bimodality;
              differentiation; modality; neuron; post-transcription; single
              cell; stem
              cells;single-cell;rna-seq;brain;methods;software;modality;splicing;UNREAD;[anchor];[outrigger];[bonvoyage];CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1097-2765, 1097-4164",
  pmid     = "28673540",
  doi      = "10.1016/j.molcel.2017.06.003"
}

@ARTICLE{Huang2017-lp,
  title    = "{BRIE: transcriptome-wide splicing quantification in single cells}",
  author   = "Huang, Yuanhua and Sanguinetti, Guido",
  abstract = "Single-cell RNA-seq (scRNA-seq) provides a comprehensive
              measurement of stochasticity in transcription, but the
              limitations of the technology have prevented its application to
              dissect variability in RNA processing events such as splicing.
              Here, we present BRIE (Bayesian regression for isoform
              estimation), a Bayesian hierarchical model that resolves these
              problems by learning an informative prior distribution from
              sequence features. We show that BRIE yields reproducible
              estimates of exon inclusion ratios in single cells and provides
              an effective tool for differential isoform quantification between
              scRNA-seq data sets. BRIE, therefore, expands the scope of
              scRNA-seq experiments to probe the stochasticity of RNA
              processing.",
  journal  = "Genome Biol.",
  volume   =  18,
  number   =  1,
  pages    = "123",
  month    =  jun,
  year     =  2017,
  url      = "http://dx.doi.org/10.1186/s13059-017-1248-5",
  keywords = "Differential splicing; Isoform estimate; Single-cell
              RNA-seq;[BRIE];rna-seq;single-cell;methods;software;splicing;UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1465-6906",
  pmid     = "28655331",
  doi      = "10.1186/s13059-017-1248-5"
}

@UNPUBLISHED{Parekh2017-hq,
  title    = "{zUMIs: A fast and flexible pipeline to process RNA sequencing
              data with UMIs}",
  author   = "Parekh, Swati and Ziegenhain, Christoph and Vieth, Beate and
              Enard, Wolfgang and Hellmann, Ines",
  abstract = "RNA sequencing is increasingly performed with less starting
              material and at a higher sample throughput, e.g. to analyse
              single-cell transcriptomes. In this context, unique molecular
              identifiers (UMIs) are used to reduce amplification noise and
              sample-specific barcodes are used to track libraries. Here, we
              present a fast and flexible pipeline to process data from such
              RNA-seq protocols. Availability:
              https://github.com/sdparekh/zUMIs",
  journal  = "bioRxiv",
  pages    = "153940",
  month    =  jun,
  year     =  2017,
  url      = "http://www.biorxiv.org/content/early/2017/06/22/153940",
  keywords = "
              single-cell;rna-seq;UMI;methods;software;[zUMIs];PREPRINT;CITED;scRNA-tools
              paper",
  language = "en",
  doi      = "10.1101/153940"
}

@ARTICLE{Dal_Molin2017-pl,
  title    = "{Single-cell RNA-sequencing: assessment of differential expression
              analysis methods}",
  author   = "Dal Molin, Alessandra and Baruzzo, Giacomo and Di Camillo,
              Barbara",
  abstract = "The sequencing of the transcriptomes of single-cells or
              single-cell RNA-sequencing has now become the dominant technology
              for the identification of novel cell types or the study of
              stochastic gene expression. In recent years, various tools for
              analyzing single-cell RNA-sequencing data have been proposed,
              many of them with the purpose of performing differentially
              expression analysis. In this work, we compare four different
              tools for single-cell RNA-sequencing differential expression,
              together with two popular methods originally developed for the
              analysis of bulk RNA-sequencing data, but largely applied to
              single-cell data. We discuss results obtained in two real
              datasets and one synthetic dataset, along with considerations
              about the perspectives of single-cell differential expression
              analysis. In particular, we explore the methods performance in
              four different scenarios, mimicking different unimodal or bimodal
              distribution of the data, as characteristic of single-cell
              transcriptomics. We observe marked differences between the
              selected methods in terms of precision and recall, the number of
              detected differentially expressed genes and the overall
              performance. Globally, the results obtained in our study suggest
              that is difficult to identify a best performing tool and that
              efforts are needed to improve the methodologies for single-cell
              RNA-sequencing data analysis and gain better accuracy of results.",
  journal  = "Front. Genet.",
  volume   =  8,
  pages    = "62",
  year     =  2017,
  url      = "http://journal.frontiersin.org/article/10.3389/fgene.2017.00062",
  keywords = "single-cell;rna-seq;review;differential
              expression;UNREAD;CITED;scRNA-tools paper",
  issn     = "1664-8021",
  doi      = "10.3389/fgene.2017.00062"
}

@ARTICLE{Rostom2017-dc,
  title    = "{Computational approaches for interpreting scRNA-seq data}",
  author   = "Rostom, R and Svensson, V and Teichmann, S A and Kar, G",
  abstract = "The recent developments in high throughput single-cell RNA
              sequencing technology (scRNA-seq) have enabled the generation of
              vast amounts of transcriptomic data at cellular resolution. With
              these advances come new modes of data analysis, building on
              high-dimensional data mining techniques. Here, we consider
              biological questions for which scRNA-seq data is used, both at a
              cell and gene level, and describe tools available for these types
              of analyses. This is an exciting and rapidly evolving field,
              where clustering, pseudotime inference, branching inference and
              gene-level analyses are particularly informative areas of
              computational analysis. This article is protected by copyright.
              All rights reserved.",
  journal  = "FEBS Lett.",
  month    =  may,
  year     =  2017,
  url      = "http://dx.doi.org/10.1002/1873-3468.12684",
  keywords = "single-cell;rna-seq;review;CITED;scRNA-tools paper;Reviews",
  language = "en",
  issn     = "0014-5793, 1873-3468",
  pmid     = "28524227",
  doi      = "10.1002/1873-3468.12684"
}

@UNPUBLISHED{Regev2017-wg,
  title    = "{The Human Cell Atlas}",
  author   = "Regev, Aviv and Teichmann, Sarah and Lander, Eric S and Amit, Ido
              and Benoist, Christophe and Birney, Ewan and Bodenmiller, Bernd
              and Campbell, Peter and Carninci, Piero and Clatworthy, Menna and
              Clevers, Hans and Deplancke, Bart and Dunham, Ian and Eberwine,
              James and Eils, Roland and Enard, Wolfgang and Farmer, Andrew and
              Fugger, Lars and Gottgens, Berthold and Hacohen, Nir and Haniffa,
              Muzlifah and Hemberg, Martin and Kim, Seung K and Klenerman, Paul
              and Kriegstein, Arnold and Lein, Ed and Linnarsson, Sten and
              Lundeberg, Joakim and Majumder, Partha and Marioni, John and
              Merad, Miriam and Mhlanga, Musa and Nawijn, Martijn and Netea,
              Mihai and Nolan, Garry and Pe'er, Dana and Philipakis, Anthony
              and Ponting, Chris P and Quake, Stephen R and Reik, Wolf and
              Rozenblatt-Rosen, Orit and Sanes, Joshua R and Satija, Rahul and
              Shumacher, Ton and Shalek, Alex K and Shapiro, Ehud and Sharma,
              Padmanee and Shin, Jay and Stegle, Oliver and Stratton, Michael
              and Stubbington, Michael J T and van Oudenaarden, Alexander and
              Wagner, Allon and Watt, Fiona M and Weissman, Jonathan S and
              Wold, Barbara and Xavier, Ramnik J and Yosef, Nir and {Human Cell
              Atlas}",
  abstract = "The recent advent of methods for high-throughput single-cell
              molecular profiling has catalyzed a growing sense in the
              scientific community that the time is ripe to complete the
              150-year-old effort to identify all cell types in the human body,
              by undertaking a Human Cell Atlas Project as an international
              collaborative effort. The aim would be to define all human cell
              types in terms of distinctive molecular profiles (e.g., gene
              expression) and connect this information with classical cellular
              descriptions (e.g., location and morphology). A comprehensive
              reference map of the molecular state of cells in healthy human
              tissues would propel the systematic study of physiological
              states, developmental trajectories, regulatory circuitry and
              interactions of cells, as well as provide a framework for
              understanding cellular dysregulation in human disease. Here we
              describe the idea, its potential utility, early
              proofs-of-concept, and some design considerations for the Human
              Cell Atlas.",
  journal  = "bioRxiv",
  pages    = "121202",
  month    =  may,
  year     =  2017,
  url      = "http://biorxiv.org/content/early/2017/05/08/121202",
  keywords = "single-cell;rna-seq;[Human Cell Atlas];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  doi      = "10.1101/121202"
}

@ARTICLE{Jiang2017-sb,
  title    = "{SCALE: modeling allele-specific gene expression by single-cell
              RNA sequencing}",
  author   = "Jiang, Yuchao and Zhang, Nancy R and Li, Mingyao",
  abstract = "Allele-specific expression is traditionally studied by bulk RNA
              sequencing, which measures average expression across cells.
              Single-cell RNA sequencing allows the comparison of expression
              distribution between the two alleles of a diploid organism and
              the characterization of allele-specific bursting. Here, we
              propose SCALE to analyze genome-wide allele-specific bursting,
              with adjustment of technical variability. SCALE detects genes
              exhibiting allelic differences in bursting parameters and genes
              whose alleles burst non-independently. We apply SCALE to mouse
              blastocyst and human fibroblast cells and find that cis control
              in gene expression overwhelmingly manifests as differences in
              burst frequency.",
  journal  = "Genome Biol.",
  volume   =  18,
  number   =  1,
  pages    = "74",
  month    =  apr,
  year     =  2017,
  url      = "http://dx.doi.org/10.1186/s13059-017-1200-8",
  keywords = "Allele-specific expression; Expression stochasticity; Single-cell
              RNA sequencing; Technical variability; Transcriptional bursting;
              cis and trans transcriptional
              control;single-cell;rna-seq;methods;software;allele-specific;[SCALE];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1465-6906",
  pmid     = "28446220",
  doi      = "10.1186/s13059-017-1200-8"
}

@BOOK{Wickham2010-zq,
  title     = "{ggplot2: Elegant Graphics for Data Analysis}",
  author    = "Wickham, Hadley",
  abstract  = "1. 1 Welcome to ggplot2 ggplot2 is an R package for producing
               statistical, or data, graphics, but it is unlike most other
               graphics packages because it has a deep underlying grammar. This
               grammar, based on the Grammar of Graphics (Wilkinson, 2005), is
               composed of a set of independent components that can be composed
               in many di?erent ways. This makesggplot2 very powerful, because
               you are not limited to a set of pre-speci?ed graphics, but you
               can create new graphics that are precisely tailored for your
               problem. This may sound overwhelming, but because there is a
               simple set of core principles and very few special cases,
               ggplot2 is also easy to learn (although it may take a little
               time to forget your preconceptions from other graphics tools).
               Practically,ggplot2 provides beautiful, hassle-free plots, that
               take care of ?ddly details like drawing legends. The plots can
               be built up iteratively and edited later. A carefully chosen set
               of defaults means that most of the time you can produce a
               publication-quality graphic in seconds, but if you do have
               special formatting requirements, a comprehensive theming system
               makes it easy to do what you want. Instead of spending time
               making your graph look pretty, you can focus on creating a graph
               that best reveals the messages in your data.",
  publisher = "Springer New York",
  month     =  feb,
  year      =  2010,
  keywords  = "software;graphics;visualisation;CITED;UNREAD;[ggplot2];Splatter
               Paper;scRNA-tools paper;clustree paper",
  language  = "en",
  isbn      = "9780387981406"
}

@ARTICLE{Kiselev2017-an,
  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 Yiu, Andrew and Chandra, Tamir and
              Natarajan, Kedar N and Reik, Wolf and Barahona, Mauricio and
              Green, Anthony R and Hemberg, Martin",
  abstract = "Single-cell RNA-seq enables the quantitative characterization of
              cell types based on global transcriptome profiles. We present
              single-cell consensus clustering (SC3), a user-friendly tool for
              unsupervised clustering, which achieves high accuracy and
              robustness by combining multiple clustering solutions through a
              consensus approach (http://bioconductor.org/packages/SC3). We
              demonstrate that SC3 is capable of identifying subclones from the
              transcriptomes of neoplastic cells collected from patients.",
  journal  = "Nat. Methods",
  volume   =  14,
  number   =  5,
  pages    = "483--486",
  month    =  mar,
  year     =  2017,
  url      = "http://dx.doi.org/10.1038/nmeth.4236",
  keywords = "[SC3];single-cell;rna-seq;software;methods;consensus
              clustering;k-means;CITED;Splatter Paper;scRNA-tools
              paper;clustree paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "28346451",
  doi      = "10.1038/nmeth.4236"
}

@ARTICLE{McCarthy2012-gc,
  title    = "{Differential expression analysis of multifactor RNA-Seq
              experiments with respect to biological variation}",
  author   = "McCarthy, Davis J and Chen, Yunshun and Smyth, Gordon K",
  abstract = "A flexible statistical framework is developed for the analysis of
              read counts from RNA-Seq gene expression studies. It provides the
              ability to analyse complex experiments involving multiple
              treatment conditions and blocking variables while still taking
              full account of biological variation. Biological variation
              between RNA samples is estimated separately from the technical
              variation associated with sequencing technologies. Novel
              empirical Bayes methods allow each gene to have its own specific
              variability, even when there are relatively few biological
              replicates from which to estimate such variability. The pipeline
              is implemented in the edgeR package of the Bioconductor project.
              A case study analysis of carcinoma data demonstrates the ability
              of generalized linear model methods (GLMs) to detect differential
              expression in a paired design, and even to detect tumour-specific
              expression changes. The case study demonstrates the need to allow
              for gene-specific variability, rather than assuming a common
              dispersion across genes or a fixed relationship between abundance
              and variability. Genewise dispersions de-prioritize genes with
              inconsistent results and allow the main analysis to focus on
              changes that are consistent between biological replicates.
              Parallel computational approaches are developed to make
              non-linear model fitting faster and more reliable, making the
              application of GLMs to genomic data more convenient and
              practical. Simulations demonstrate the ability of adjusted
              profile likelihood estimators to return accurate estimators of
              biological variability in complex situations. When variation is
              gene-specific, empirical Bayes estimators provide an advantageous
              compromise between the extremes of assuming common dispersion or
              separate genewise dispersion. The methods developed here can also
              be applied to count data arising from DNA-Seq applications,
              including ChIP-Seq for epigenetic marks and DNA methylation
              analyses.",
  journal  = "Nucleic Acids Res.",
  volume   =  40,
  number   =  10,
  pages    = "4288--4297",
  month    =  may,
  year     =  2012,
  url      = "http://dx.doi.org/10.1093/nar/gks042",
  keywords = "rna-seq;differential
              expression;variation;UNREAD;[edgeR];CITED;Splatter
              Paper;scRNA-tools paper",
  language = "en",
  issn     = "0305-1048, 1362-4962",
  pmid     = "22287627",
  doi      = "10.1093/nar/gks042",
  pmc      = "PMC3378882"
}

@ARTICLE{Svensson2017-zo,
  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",
  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    =  mar,
  year     =  2017,
  url      = "http://dx.doi.org/10.1038/nmeth.4220",
  keywords = "rna-seq;power;software;[umis];UNREAD;CITED;scRNA-tools paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "28263961",
  doi      = "10.1038/nmeth.4220"
}

@ARTICLE{McCarthy2017-ql,
  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",
  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",
  volume   =  33,
  number   =  8,
  pages    = "1179--1186",
  month    =  jan,
  year     =  2017,
  url      = "http://dx.doi.org/10.1093/bioinformatics/btw777",
  keywords = "single-cell;rna-seq;[scater];UNREAD;quality
              control;normalisation;visualisation;software;CITED;Splatter
              Paper;scRNA-tools paper;Second year review",
  language = "en",
  issn     = "1367-4803, 1367-4811",
  pmid     = "28088763",
  doi      = "10.1093/bioinformatics/btw777"
}

@ARTICLE{Welch2016-dv,
  title    = "{Robust detection of alternative splicing in a population of
              single cells}",
  author   = "Welch, Joshua D and Hu, Yin and Prins, Jan F",
  abstract = "Single cell RNA-seq experiments provide valuable insight into
              cellular heterogeneity but suffer from low coverage, 3' bias and
              technical noise. These unique properties of single cell RNA-seq
              data make study of alternative splicing difficult, and thus most
              single cell studies have restricted analysis of transcriptome
              variation to the gene level. To address these limitations, we
              developed SingleSplice, which uses a statistical model to detect
              genes whose isoform usage shows biological variation
              significantly exceeding technical noise in a population of single
              cells. Importantly, SingleSplice is tailored to the unique
              demands of single cell analysis, detecting isoform usage
              differences without attempting to infer expression levels for
              full-length transcripts. Using data from spike-in transcripts, we
              found that our approach detects variation in isoform usage among
              single cells with high sensitivity and specificity. We also
              applied SingleSplice to data from mouse embryonic stem cells and
              discovered a set of genes that show significant biological
              variation in isoform usage across the set of cells. A subset of
              these isoform differences are linked to cell cycle stage,
              suggesting a novel connection between alternative splicing and
              the cell cycle.",
  journal  = "Nucleic Acids Res.",
  volume   =  44,
  number   =  8,
  pages    = "e73",
  month    =  may,
  year     =  2016,
  url      = "http://dx.doi.org/10.1093/nar/gkv1525",
  keywords = "
              single-cell;rna-seq;methods;software;splicing;[SingleSplice];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "0305-1048, 1362-4962",
  pmid     = "26740580",
  doi      = "10.1093/nar/gkv1525",
  pmc      = "PMC4856971"
}

@ARTICLE{Qiu2017-bi,
  title     = "{Single-cell mRNA quantification and differential analysis with
               Census}",
  author    = "Qiu, Xiaojie and Hill, Andrew and Packer, Jonathan and Lin,
               Dejun and Ma, Yi-An and Trapnell, Cole",
  abstract  = "The Census tool converts single-cell RNA-seq relative read
               counts to relative transcript counts for more accurate
               differential gene expression and analysis in the absence of
               spike-ins or molecular barcodes.",
  journal   = "Nat. Methods",
  publisher = "Nature Publishing Group",
  month     =  jan,
  year      =  2017,
  url       = "http://dx.doi.org/10.1038/nmeth.4150",
  keywords  = "
               single-cell;rna-seq;methods;software;quantification;[Census];[monocle];CITED;scRNA-tools
               paper",
  language  = "en",
  issn      = "1548-7091, 1548-7105",
  doi       = "10.1038/nmeth.4150"
}

@ARTICLE{Miao2016-eo,
  title     = "{Differential expression analyses for single-cell RNA-Seq: old
               questions on new data}",
  author    = "Miao, Zhun and Zhang, Xuegong",
  abstract  = "BackgroundSingle-cell RNA sequencing (scRNA-seq) is an emerging
               technology that enables high resolution detection of
               heterogeneities between cells. One important application of
               scRNA-seq data is to detect differential expression (DE) of
               genes. Currently, some researchers still use DE analysis methods
               developed for bulk RNA-Seq data on single-cell data, and some
               new methods for scRNA-seq data have also been developed. Bulk
               and single-cell RNA-seq data have different characteristics. A
               systematic evaluation of the two types of methods on scRNA-seq
               data is needed.ResultsIn this study, we conducted a series of
               experiments on scRNA-seq data to quantitatively evaluate 14
               popular DE analysis methods, including both of traditional
               methods developed for bulk RNA-seq data and new methods
               specifically designed for scRNA-seq data. We obtained
               observations and recommendations for the methods under different
               situations.ConclusionsDE analysis methods should be chosen for
               scRNA-seq data with great caution with regard to different
               situations of data. Different strategies should be taken for
               data with different sample sizes and/or different strengths of
               the expected signals. Several methods for scRNA-seq data show
               advantages in some aspects, and DEGSeq tends to outperform other
               methods with respect to consistency, reproducibility and
               accuracy of predictions on scRNA-seq data.",
  journal   = "Quant Biol",
  publisher = "Higher Education Press",
  volume    =  4,
  number    =  4,
  pages     = "243--260",
  month     =  dec,
  year      =  2016,
  url       = "http://link.springer.com/article/10.1007/s40484-016-0089-7",
  keywords  = "single-cell;rna-seq;differential
               expression;comparison;UNREAD;CITED;scRNA-tools paper",
  language  = "en",
  issn      = "2095-4689, 2095-4697",
  doi       = "10.1007/s40484-016-0089-7"
}

@UNPUBLISHED{Poirion2016-ld,
  title    = "{Using single nucleotide variations in cancer single-cell RNA-seq
              data for subpopulation identification and genotype-phenotype
              linkage analysis}",
  author   = "Poirion, Olivier B and Zhu, Xun and Ching, Travers and Garmire,
              Lana X",
  abstract = "Characterization of subpopulations is a key challenge in the
              emerging field of single-cell RNA-seq (scRNA-seq). In scRNA-seq
              data, gene expression has been used as feature to detect
              subpopulations, however, this data type is subject to significant
              amount of noise. Unconventionally, we propose to use filtered,
              effective and expressed nucleotide variations (eeSNVs)
              information from scRNA-seq data as improved predictive features
              for subpopulation identification. We developed a linear modeling
              framework called SSrGE (Sparse SNV inference to reflect Gene
              Expression) to detect eeSNVs that are associated with gene
              expression profiles. In all the datasets tested, these eeSNVs
              show better accuracy than gene expression for retrieving cell
              subpopulations. Moreover, bipartite graphs of cells in
              combination with eeSNVs have better visual representation of the
              different cell subpopulations than the other methods that use
              gene expression data. We ranked genes, according to their eeSNVs
              in the cancer scRNA-seq data, and found that genes in antigen
              processing and presentation pathway have top ranked eeSNVs. They
              include HLA-A, HLA-B, HLA-C and HLA-DRA in Human Leukocyte
              Antigen (HLA) complex, and B2M in histocompatibility complex MHC.
              Moreover, previously experimentally validated cancer relevant
              genes, such as KRAS and SPARC, are highly ranked for eeSNVs too.
              In summary, we emphasize that SNV features hidden in scRNA-seq
              data have merits for both subpopulation identification and
              linkage of genotype-phenotype relationship. The computational
              method is freely available at
              https://github.com/lanagarmire/SSrGE.",
  journal  = "bioRxiv",
  pages    = "095810",
  month    =  jan,
  year     =  2016,
  url      = "http://biorxiv.org/content/early/2016/12/21/095810?ct",
  keywords = "
              single-cell;rna-seq;methods;software;clustering;variants;cancer;[SSrGE];UNREAD;PREPRINT;CITED;scRNA-tools
              paper",
  language = "en",
  doi      = "10.1101/095810"
}

@ARTICLE{Maaten2008-ne,
  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,
  url      = "http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf",
  keywords = "dimensionality
              reduction;methods;[t-SNE];UNREAD;CITED;Confimation;scRNA-tools
              paper",
  issn     = "1532-4435"
}

@ARTICLE{Patel2014-bl,
  title    = "{Single-cell RNA-seq highlights intratumoral heterogeneity in
              primary glioblastoma}",
  author   = "Patel, Anoop P and Tirosh, Itay and Trombetta, John J and Shalek,
              Alex K and Gillespie, Shawn M and Wakimoto, Hiroaki and Cahill,
              Daniel P and Nahed, Brian V and Curry, William T and Martuza,
              Robert L and Louis, David N and Rozenblatt-Rosen, Orit and
              Suv{\`a}, Mario L and Regev, Aviv and Bernstein, Bradley E",
  abstract = "Human cancers are complex ecosystems composed of cells with
              distinct phenotypes, genotypes, and epigenetic states, but
              current models do not adequately reflect tumor composition in
              patients. We used single-cell RNA sequencing (RNA-seq) to profile
              430 cells from five primary glioblastomas, which we found to be
              inherently variable in their expression of diverse
              transcriptional programs related to oncogenic signaling,
              proliferation, complement/immune response, and hypoxia. We also
              observed a continuum of stemness-related expression states that
              enabled us to identify putative regulators of stemness in vivo.
              Finally, we show that established glioblastoma subtype
              classifiers are variably expressed across individual cells within
              a tumor and demonstrate the potential prognostic implications of
              such intratumoral heterogeneity. Thus, we reveal previously
              unappreciated heterogeneity in diverse regulatory programs
              central to glioblastoma biology, prognosis, and therapy.",
  journal  = "Science",
  volume   =  344,
  number   =  6190,
  pages    = "1396--1401",
  month    =  jun,
  year     =  2014,
  url      = "http://dx.doi.org/10.1126/science.1254257",
  keywords = "
              single-cell;rna-seq;brain;UNREAD;CITED;[inferCNV];Confimation;scRNA-tools
              paper",
  language = "en",
  issn     = "0036-8075, 1095-9203",
  pmid     = "24925914",
  doi      = "10.1126/science.1254257",
  pmc      = "PMC4123637"
}

@ARTICLE{Love2014-tw,
  title    = "{Moderated estimation of fold change and dispersion for RNA-seq
              data with DESeq2}",
  author   = "Love, Michael I and Huber, Wolfgang and Anders, Simon",
  abstract = "In comparative high-throughput sequencing assays, a fundamental
              task is the analysis of count data, such as read counts per gene
              in RNA-seq, for evidence of systematic changes across
              experimental conditions. Small replicate numbers, discreteness,
              large dynamic range and the presence of outliers require a
              suitable statistical approach. We present DESeq2, a method for
              differential analysis of count data, using shrinkage estimation
              for dispersions and fold changes to improve stability and
              interpretability of estimates. This enables a more quantitative
              analysis focused on the strength rather than the mere presence of
              differential expression. The DESeq2 package is available at
              http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
              webcite.",
  journal  = "Genome Biol.",
  volume   =  15,
  number   =  12,
  pages    = "550",
  year     =  2014,
  url      = "http://dx.doi.org/10.1186/s13059-014-0550-8",
  keywords = "rna-seq;differential
              expression;methods;software;[DESeq2];UNREAD;CITED;Confimation;scRNA-tools
              paper",
  language = "en",
  issn     = "1465-6906",
  pmid     = "25516281",
  doi      = "10.1186/s13059-014-0550-8",
  pmc      = "PMC4302049"
}

@ARTICLE{Robinson2010-pt,
  title    = "{edgeR: a Bioconductor package for differential expression
              analysis of digital gene expression data}",
  author   = "Robinson, Mark D and McCarthy, Davis J and Smyth, Gordon K",
  abstract = "SUMMARY: It is expected that emerging digital gene expression
              (DGE) technologies will overtake microarray technologies in the
              near future for many functional genomics applications. One of the
              fundamental data analysis tasks, especially for gene expression
              studies, involves determining whether there is evidence that
              counts for a transcript or exon are significantly different
              across experimental conditions. edgeR is a Bioconductor software
              package for examining differential expression of replicated count
              data. An overdispersed Poisson model is used to account for both
              biological and technical variability. Empirical Bayes methods are
              used to moderate the degree of overdispersion across transcripts,
              improving the reliability of inference. The methodology can be
              used even with the most minimal levels of replication, provided
              at least one phenotype or experimental condition is replicated.
              The software may have other applications beyond sequencing data,
              such as proteome peptide count data. AVAILABILITY: The package is
              freely available under the LGPL licence from the Bioconductor web
              site (http://bioconductor.org).",
  journal  = "Bioinformatics",
  volume   =  26,
  number   =  1,
  pages    = "139--140",
  month    =  jan,
  year     =  2010,
  url      = "http://dx.doi.org/10.1093/bioinformatics/btp616",
  keywords = "rna-seq;methods;software;[edgeR];UNREAD;CITED;Splatter
              Paper;scRNA-tools paper",
  language = "en",
  issn     = "1367-4803, 1367-4811",
  pmid     = "19910308",
  doi      = "10.1093/bioinformatics/btp616",
  pmc      = "PMC2796818"
}

@ARTICLE{Korthauer2016-yq,
  title    = "{A statistical approach for identifying differential distributions
              in single-cell RNA-seq experiments}",
  author   = "Korthauer, Keegan D and Chu, Li-Fang and Newton, Michael A and
              Li, Yuan and Thomson, James and Stewart, Ron and Kendziorski,
              Christina",
  abstract = "The ability to quantify cellular heterogeneity is a major
              advantage of single-cell technologies. However, statistical
              methods often treat cellular heterogeneity as a nuisance. We
              present a novel method to characterize differences in expression
              in the presence of distinct expression states within and among
              biological conditions. We demonstrate that this framework can
              detect differential expression patterns under a wide range of
              settings. Compared to existing approaches, this method has higher
              power to detect subtle differences in gene expression
              distributions that are more complex than a mean shift, and can
              characterize those differences. The freely available R package
              scDD implements the approach.",
  journal  = "Genome Biol.",
  volume   =  17,
  number   =  1,
  pages    = "222",
  month    =  oct,
  year     =  2016,
  url      = "http://dx.doi.org/10.1186/s13059-016-1077-y",
  keywords = "Cellular heterogeneity; Differential expression; Mixture
              modeling; Single-cell
              RNA-seq;single-cell;rna-seq;methods;software;differential
              expression;[scDD];UNREAD;CITED;Splatter
              Paper;Confimation;scRNA-tools paper",
  language = "en",
  issn     = "1465-6906",
  pmid     = "27782827",
  doi      = "10.1186/s13059-016-1077-y",
  pmc      = "PMC5080738"
}

@ARTICLE{Poirion2016-da,
  title    = "{Single-cell transcriptomics bioinformatics and computational
              challenges}",
  author   = "Poirion, Olivier B and Zhu, Xun and Ching, Travers and Garmire,
              Lana",
  abstract = "The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the
              promise to revolutionize our understanding of diseases and
              associated biological processes at an unprecedented resolution.
              It opens the door to reveal the intercellular heterogeneity and
              has been employed to a variety of applications, ranging from
              characterizing cancer cells subpopulations to elucidating tumor
              resistance mechanisms. Parallel to improving experimental
              protocols to deal with technological issues, deriving new
              analytical methods to reveal the complexity in scRNA-Seq data is
              just as challenging. Here we review the current state-of-the-art
              bioinformatics tools and methods for scRNA-Seq analysis, as well
              as addressing some critical analytical challenges that the field
              faces.",
  journal  = "Front. Genet.",
  volume   =  7,
  month    =  sep,
  year     =  2016,
  url      = "http://journal.frontiersin.org/article/10.3389/fgene.2016.00163/full",
  keywords = "single-cell genomics; Single-Cell Analysis; bioinformatics;
              heterogeneity;
              microevolution;single-cell;rna-seq;review;CITED;scRNA-tools
              paper;Reviews",
  language = "en",
  issn     = "1664-8021",
  doi      = "10.3389/fgene.2016.00163"
}

@ARTICLE{Haghverdi2016-zt,
  title    = "{Diffusion pseudotime robustly reconstructs lineage branching}",
  author   = "Haghverdi, Laleh and B{\"u}ttner, Maren and Wolf, F Alexander and
              Buettner, Florian and Theis, Fabian J",
  abstract = "The temporal order of differentiating cells is intrinsically
              encoded in their single-cell expression profiles. We describe an
              efficient way to robustly estimate this order according to
              diffusion pseudotime (DPT), which measures transitions between
              cells using diffusion-like random walks. Our DPT software
              implementations make it possible to reconstruct the developmental
              progression of cells and identify transient or metastable states,
              branching decisions and differentiation endpoints.",
  journal  = "Nat. Methods",
  month    =  aug,
  year     =  2016,
  url      = "http://dx.doi.org/10.1038/nmeth.3971",
  keywords = "
              single-cell;rna-seq;lineage;bifurcation;methods;software;[DPT];simulation;UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "27571553",
  doi      = "10.1038/nmeth.3971"
}

@ARTICLE{Setty2016-yh,
  title    = "{Wishbone identifies bifurcating developmental trajectories from
              single-cell data}",
  author   = "Setty, Manu and Tadmor, Michelle D and Reich-Zeliger, Shlomit and
              Angel, Omer and Salame, Tomer Meir and Kathail, Pooja and Choi,
              Kristy and Bendall, Sean and Friedman, Nir and Pe'er, Dana",
  abstract = "Recent single-cell analysis technologies offer an unprecedented
              opportunity to elucidate developmental pathways. Here we present
              Wishbone, an algorithm for positioning single cells along
              bifurcating developmental trajectories with high resolution.
              Wishbone uses multi-dimensional single-cell data, such as mass
              cytometry or RNA-Seq data, as input and orders cells according to
              their developmental progression, and it pinpoints bifurcation
              points by labeling each cell as pre-bifurcation or as one of two
              post-bifurcation cell fates. Using 30-channel mass cytometry
              data, we show that Wishbone accurately recovers the known stages
              of T-cell development in the mouse thymus, including the
              bifurcation point. We also apply the algorithm to mouse myeloid
              differentiation and demonstrate its generalization to additional
              lineages. A comparison of Wishbone to diffusion maps, SCUBA and
              Monocle shows that it outperforms these methods both in the
              accuracy of ordering cells and in the correct identification of
              branch points.",
  journal  = "Nat. Biotechnol.",
  volume   =  34,
  number   =  6,
  pages    = "637--645",
  month    =  may,
  year     =  2016,
  url      = "http://dx.doi.org/10.1038/nbt.3569",
  keywords = "
              single-cell;rna-seq;methods;software;lineage;[Wishbone];UNREAD;CITED;scRNA-tools
              paper",
  language = "en",
  issn     = "1087-0156, 1546-1696",
  pmid     = "27136076",
  doi      = "10.1038/nbt.3569",
  pmc      = "PMC4900897"
}

@ARTICLE{Jaakkola2016-ko,
  title    = "{Comparison of methods to detect differentially expressed genes
              between single-cell populations}",
  author   = "Jaakkola, Maria K and Seyednasrollah, Fatemeh and Mehmood, Arfa
              and Elo, Laura L",
  abstract = "We compared five statistical methods to detect differentially
              expressed genes between two distinct single-cell populations.
              Currently, it remains unclear whether differential expression
              methods developed originally for conventional bulk RNA-seq data
              can also be applied to single-cell RNA-seq data analysis. Our
              results in three diverse comparison settings showed marked
              differences between the different methods in terms of the number
              of detections as well as their sensitivity and specificity. They,
              however, did not reveal systematic benefits of the currently
              available single-cell-specific methods. Instead, our previously
              introduced reproducibility-optimization method showed good
              performance in all comparison settings without any
              single-cell-specific modifications.",
  journal  = "Brief. Bioinform.",
  month    =  jul,
  year     =  2016,
  url      = "http://bib.oxfordjournals.org/content/early/2016/07/02/bib.bbw057.abstract",
  keywords = "single-cell;rna-seq;differential
              expression;comparison;UNREAD;CITED;scRNA-tools paper",
  issn     = "1467-5463",
  doi      = "10.1093/bib/bbw057"
}

@ARTICLE{Kivioja2012-bq,
  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",
  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,
  url      = "http://dx.doi.org/10.1038/nmeth.1778",
  keywords = "rna-seq;UMI;protocol;UNREAD;CITED;MCRI Top
              Up;Proposal;Confimation;Splatter Paper;scRNA-tools paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "22101854",
  doi      = "10.1038/nmeth.1778"
}

@ARTICLE{Tang2009-jd,
  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",
  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,
  url      = "http://dx.doi.org/10.1038/nmeth.1315",
  keywords = "rna-seq;single-cell;protocol;UNREAD;CITED;MCRI Top
              Up;Proposal;Splatter Paper;Confimation;scRNA-tools paper;clustree
              paper",
  language = "en",
  issn     = "1548-7091, 1548-7105",
  pmid     = "19349980",
  doi      = "10.1038/nmeth.1315"
}

@ARTICLE{Satija2015-or,
  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",
  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.",
  publisher = "Nature Publishing Group",
  volume    =  33,
  number    =  5,
  pages     = "495--502",
  month     =  apr,
  year      =  2015,
  url       = "http://dx.doi.org/10.1038/nbt.3192",
  keywords  = "
               single-cell;rna-seq;localisation;software;[Seurat];UNREAD;methods;CITED;Splatter
               Paper;Confimation;scRNA-tools paper;Second year review;clustree
               paper",
  issn      = "1087-0156, 1546-1696",
  pmid      = "25867923",
  doi       = "10.1038/nbt.3192",
  pmc       = "PMC4430369"
}

@ARTICLE{Diaz2016-fz,
  title    = "{SCell: integrated analysis of single-cell RNA-seq data}",
  author   = "Diaz, Aaron and Liu, Siyuan J and Sandoval, Carmen and Pollen,
              Alex and Nowakowski, Tom J and Lim, Daniel A and Kriegstein,
              Arnold",
  abstract = "Summary: Analysis of the composition of heterogeneous tissue has
              been greatly enabled by recent developments in single-cell
              transcriptomics. We present SCell, an integrated software tool
              for quality filtering, normalization, feature selection,
              iterative dimensionality reduction, clustering and the estimation
              of gene-expression gradients from large ensembles of single-cell
              RNA-seq datasets. SCell is open source, and implemented with an
              intuitive graphical interface. Scripts and protocols for the
              high-throughput pre-processing of large ensembles of single-cell,
              RNA-seq datasets are provided as an additional
              resource.Availability and Implementation: Binary executables for
              Windows, MacOS and Linux are available at
              http://sourceforge.net/projects/scell, source code and
              pre-processing scripts are available from
              https://github.com/diazlab/SCell.Contact:
              aaron.diaz@ucsf.eduSupplementary information: Supplementary data
              are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  month    =  apr,
  year     =  2016,
  url      = "http://bioinformatics.oxfordjournals.org/content/early/2016/04/18/bioinformatics.btw201.abstract",
  keywords = "single-cell;rna-seq;quality control;normalisation;dimensionality
              reduction;clustering;differential
              expression;methods;software;[SCell];UNREAD;CITED;scRNA-tools
              paper;scRNA-seq Normalisation",
  issn     = "1367-4803",
  doi      = "10.1093/bioinformatics/btw201"
}

@ARTICLE{Bacher2016-en,
  title    = "{Design and computational analysis of single-cell RNA-sequencing
              experiments}",
  author   = "Bacher, Rhonda and Kendziorski, Christina",
  abstract = "Single-cell RNA-sequencing (scRNA-seq) has emerged as a
              revolutionary tool that allows us to address scientific questions
              that eluded examination just a few years ago. With the advantages
              of scRNA-seq come computational challenges that are just
              beginning to be addressed. In this article, we highlight the
              computational methods available for the design and analysis of
              scRNA-seq experiments, their advantages and disadvantages in
              various settings, the open questions for which novel methods are
              needed, and expected future developments in this exciting area.",
  journal  = "Genome Biol.",
  volume   =  17,
  number   =  1,
  pages    = "63",
  month    =  apr,
  year     =  2016,
  url      = "http://dx.doi.org/10.1186/s13059-016-0927-y",
  keywords = "single-cell;rna-seq;design;review;UNREAD;CITED;scRNA-tools
              paper;Reviews",
  issn     = "1465-6906",
  pmid     = "27052890",
  doi      = "10.1186/s13059-016-0927-y"
}

@ARTICLE{Katayama2013-pq,
  title    = "{SAMstrt: statistical test for differential expression in
              single-cell transcriptome with spike-in normalization}",
  author   = "Katayama, Shintaro and T{\"o}h{\"o}nen, Virpi and Linnarsson,
              Sten and Kere, Juha",
  abstract = "MOTIVATION: Recent transcriptome studies have revealed that total
              transcript numbers vary by cell type and condition; therefore,
              the statistical assumptions for single-cell transcriptome studies
              must be revisited. SAMstrt is an extension code for SAMseq, which
              is a statistical method for differential expression, to enable
              spike-in normalization and statistical testing based on the
              estimated absolute number of transcripts per cell for single-cell
              RNA-seq methods. AVAILABILITY AND IMPLEMENTATION: SAMstrt is
              implemented on R and available in github
              (https://github.com/shka/R-SAMstrt). CONTACT:
              shintaro.katayama@ki.se",
  journal  = "Bioinformatics",
  volume   =  29,
  number   =  22,
  pages    = "2943--2945",
  month    =  nov,
  year     =  2013,
  url      = "http://dx.doi.org/10.1093/bioinformatics/btt511",
  keywords = "normalisation;methods;single-cell;software;rna-seq;differential
              expression;[SAMstrt];spike-in;CITED;scRNA-tools paper;scRNA-seq
              Normalisation",
  issn     = "1367-4803, 1367-4811",
  pmid     = "23995393",
  doi      = "10.1093/bioinformatics/btt511",
  pmc      = "PMC3810855"
}

@ARTICLE{Kharchenko2014-tb,
  title    = "{Bayesian approach to single-cell differential expression analysis}",
  author   = "Kharchenko, Peter V and Silberstein, Lev and Scadden, David T",
  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,
  url      = "http://dx.doi.org/10.1038/nmeth.2967",
  keywords = "methods;single-cell;rna-seq;differential
              expression;bayesian;zeros;[SCDE];software;CITED;scRNA-tools paper",
  issn     = "1548-7091, 1548-7105",
  pmid     = "24836921",
  doi      = "10.1038/nmeth.2967",
  pmc      = "PMC4112276"
}

@ARTICLE{Zeisel2015-rd,
  title    = "{Brain structure. Cell types in the mouse cortex and hippocampus
              revealed by single-cell RNA-seq}",
  author   = "Zeisel, Amit and Mu{\~n}oz-Manchado, Ana B and Codeluppi, Simone
              and L{\"o}nnerberg, Peter and La Manno, Gioele and Jur{\'e}us,
              Anna and Marques, Sueli and Munguba, Hermany and He, Liqun and
              Betsholtz, Christer and Rolny, Charlotte and Castelo-Branco,
              Gon{\c c}alo and Hjerling-Leffler, Jens and Linnarsson, Sten",
  abstract = "The mammalian cerebral cortex supports cognitive functions such
              as sensorimotor integration, memory, and social behaviors. Normal
              brain function relies on a diverse set of differentiated cell
              types, including neurons, glia, and vasculature. Here, we have
              used large-scale single-cell RNA sequencing (RNA-seq) to classify
              cells in the mouse somatosensory cortex and hippocampal CA1
              region. We found 47 molecularly distinct subclasses, comprising
              all known major cell types in the cortex. We identified numerous
              marker genes, which allowed alignment with known cell types,
              morphology, and location. We found a layer I interneuron
              expressing Pax6 and a distinct postmitotic oligodendrocyte
              subclass marked by Itpr2. Across the diversity of cortical cell
              types, transcription factors formed a complex, layered regulatory
              code, suggesting a mechanism for the maintenance of adult cell
              type identity.",
  journal  = "Science",
  volume   =  347,
  number   =  6226,
  pages    = "1138--1142",
  month    =  mar,
  year     =  2015,
  url      = "http://dx.doi.org/10.1126/science.aaa1934",
  keywords = "
              brain;software;single-cell;differentiation;rna-seq;methods;clustering;[BackSPIN];CITED;Proposal;Confimation;Splatter
              Paper;scRNA-tools paper",
  issn     = "0036-8075, 1095-9203",
  pmid     = "25700174",
  doi      = "10.1126/science.aaa1934"
}

@ARTICLE{Stegle2015-mc,
  title     = "{Computational and analytical challenges in single-cell
               transcriptomics}",
  author    = "Stegle, Oliver and Teichmann, Sarah A and Marioni, John C",
  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.",
  publisher = "Nature Publishing Group",
  volume    =  16,
  number    =  3,
  pages     = "133--145",
  month     =  mar,
  year      =  2015,
  url       = "http://dx.doi.org/10.1038/nrg3833",
  keywords  = "single-cell;rna-seq;review;CITED;MCRI Top
               Up;Proposal;Confimation;scRNA-tools paper;clustree paper;Reviews",
  issn      = "1471-0056, 1471-0064",
  pmid      = "25628217",
  doi       = "10.1038/nrg3833"
}

@ARTICLE{Trapnell2014-he,
  title    = "{The dynamics and regulators of cell fate decisions are revealed
              by pseudotemporal ordering of single cells}",
  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",
  abstract = "Defining the transcriptional dynamics of a temporal process such
              as cell differentiation is challenging owing to the high
              variability in gene expression between individual cells.
              Time-series gene expression analyses of bulk cells have
              difficulty distinguishing early and late phases of a
              transcriptional cascade or identifying rare subpopulations of
              cells, and single-cell proteomic methods rely on a priori
              knowledge of key distinguishing markers. Here we describe
              Monocle, an unsupervised algorithm that increases the temporal
              resolution of transcriptome dynamics using single-cell RNA-Seq
              data collected at multiple time points. Applied to the
              differentiation of primary human myoblasts, Monocle revealed
              switch-like changes in expression of key regulatory factors,
              sequential waves of gene regulation, and expression of regulators
              that were not known to act in differentiation. We validated some
              of these predicted regulators in a loss-of function screen.
              Monocle can in principle be used to recover single-cell gene
              expression kinetics from a wide array of cellular processes,
              including differentiation, proliferation and oncogenic
              transformation.",
  journal  = "Nat. Biotechnol.",
  volume   =  32,
  number   =  4,
  pages    = "381--386",
  month    =  apr,
  year     =  2014,
  url      = "http://dx.doi.org/10.1038/nbt.2859",
  keywords = "
              software;rna-seq;single-cell;[monocle];muscle;differentiation;methods;lineage;CITED;Proposal;Splatter
              Paper;Confimation;scRNA-tools paper",
  issn     = "1087-0156, 1546-1696",
  pmid     = "24658644",
  doi      = "10.1038/nbt.2859",
  pmc      = "PMC4122333"
}

repository: novapyth/scRNAtools-paper

repo_name: novapyth/scRNAtools-paper