book.bib

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@article{Korsunsky2018fast,
  title={Fast, sensitive, and accurate integration of single cell data with Harmony},
  author={Korsunsky, Ilya and Fan, Jean and Slowikowski, Kamil and Zhang, Fan and Wei, Kevin and Baglaenko, Yuriy and Brenner, Michael and Loh, Po-Ru and Raychaudhuri, Soumya},
  journal={BioRxiv},
  pages={461954},
  year={2018},
  publisher={Cold Spring Harbor Laboratory}
}

@article{Deng2014-mx,
 author = {Deng, Q. and Ramskold, D. and Reinius, B. and Sandberg, R.},
 doi = {10.1126/science.1245316},
 issn = {0036-8075, 1095-9203},
 journal = {Science},
 month = jan,
 number = {6167},
 pages = {193-196},
 publisher = {American Association for the Advancement of Science (AAAS)},
 source = {Crossref},
 title = {Single-Cell {RNA}-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells},
 url = {https://doi.org/10.1126/science.1245316},
 volume = {343},
 year = {2014},
}

@article{Macosko2015-ix,
 author = {Macosko, Evan Z. and Basu, Anindita and Satija, Rahul and Nemesh, James and Shekhar, Karthik and Goldman, Melissa and Tirosh, Itay and Bialas, Allison R. and Kamitaki, Nolan and Martersteck, Emily M. and Trombetta, John J. and Weitz, David A. and Sanes, Joshua R. and Shalek, Alex K. and Regev, Aviv and McCarroll, Steven A.},
 doi = {10.1016/j.cell.2015.05.002},
 issn = {0092-8674},
 journal = {Cell},
 month = may,
 number = {5},
 pages = {1202-1214},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets},
 url = {https://doi.org/10.1016/j.cell.2015.05.002},
 volume = {161},
 year = {2015},
}

@article{Xu2015-vf,
 author = {Xu, Chen and Su, Zhengchang},
 doi = {10.1093/bioinformatics/btv088},
 issn = {1367-4803, 1460-2059},
 journal = {Bioinformatics},
 month = feb,
 number = {12},
 pages = {1974-1980},
 publisher = {Oxford University Press (OUP)},
 source = {Crossref},
 title = {Identification of cell types from single-cell transcriptomes using a novel clustering method},
 url = {https://doi.org/10.1093/bioinformatics/btv088},
 volume = {31},
 year = {2015},
}

@article{Zurauskiene2016-kg,
 author = {žurauskienė, Justina and Yau, Christopher},
 doi = {10.1186/s12859-016-0984-y},
 issn = {1471-2105},
 journal = {BMC Bioinformatics},
 month = mar,
 number = {1},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {{pcaReduce:} {Hierarchical} clustering of single cell transcriptional profiles},
 url = {https://doi.org/10.1186/s12859-016-0984-y},
 volume = {17},
 year = {2016},
}

@article{Guo2015-ok,
 author = {Guo, Minzhe and Wang, Hui and Potter, S. Steven and Whitsett, Jeffrey A. and Xu, Yan},
 doi = {10.1371/journal.pcbi.1004575},
 issn = {1553-7358},
 journal = {PLoS Comput Biol},
 month = nov,
 number = {11},
 pages = {e1004575},
 publisher = {Public Library of Science (PLoS)},
 source = {Crossref},
 title = {{SINCERA:} {A} Pipeline for Single-Cell {RNA}-Seq Profiling Analysis},
 url = {https://doi.org/10.1371/journal.pcbi.1004575},
 volume = {11},
 year = {2015},
}

@article{Kiselev2016-bq,
 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},
 doi = {10.1038/nmeth.4236},
 issn = {1548-7091, 1548-7105},
 journal = {Nat Meth},
 month = mar,
 number = {5},
 pages = {483-486},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {{SC3:} {Consensus} clustering of single-cell {RNA}-seq data},
 url = {https://doi.org/10.1038/nmeth.4236},
 volume = {14},
 year = {2017},
}

@article{Tang2009-bu,
 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},
 doi = {10.1038/nmeth.1315},
 issn = {1548-7091, 1548-7105},
 journal = {Nat Meth},
 month = apr,
 number = {5},
 pages = {377-382},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {{mRNA}-Seq whole-transcriptome analysis of a single cell},
 url = {https://doi.org/10.1038/nmeth.1315},
 volume = {6},
 year = {2009},
}

@article{Picelli2013-sb,
 author = {Picelli, Simone and Björklund, Åsa K and Faridani, Omid R and Sagasser, Sven and Winberg, Gösta and Sandberg, Rickard},
 doi = {10.1038/nmeth.2639},
 issn = {1548-7091, 1548-7105},
 journal = {Nat Meth},
 month = sep,
 number = {11},
 pages = {1096-1098},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Smart-seq2 for sensitive full-length transcriptome profiling in single cells},
 url = {https://doi.org/10.1038/nmeth.2639},
 volume = {10},
 year = {2013},
}

@article{Hashimshony2012-kd,
 author = {Hashimshony, Tamar and Wagner, Florian and Sher, Noa and Yanai, Itai},
 doi = {10.1016/j.celrep.2012.08.003},
 issn = {2211-1247},
 journal = {Cell Reports},
 month = sep,
 number = {3},
 pages = {666-673},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {{CEL}-Seq: {{Single}-cell} {RNA}-Seq by Multiplexed Linear Amplification},
 url = {https://doi.org/10.1016/j.celrep.2012.08.003},
 volume = {2},
 year = {2012},
}

@article{Saliba2014-dy,
 author = {Saliba, Antoine-Emmanuel and Westermann, Alexander J. and Gorski, Stanislaw A. and Vogel, Jörg},
 doi = {10.1093/nar/gku555},
 issn = {0305-1048, 1362-4962},
 journal = {Nucleic Acids Res},
 month = jul,
 number = {14},
 pages = {8845-8860},
 publisher = {Oxford University Press (OUP)},
 source = {Crossref},
 title = {Single-cell {RNA}-seq: {Advances} and future challenges},
 url = {https://doi.org/10.1093/nar/gku555},
 volume = {42},
 year = {2014},
}

@article{Handley2015-yi,
 author = {Handley, Ava and Schauer, Tamás and Ladurner, Andreas G. and Margulies, Carla E.},
 doi = {10.1016/j.molcel.2015.04.024},
 issn = {1097-2765},
 journal = {Molecular Cell},
 month = may,
 number = {4},
 pages = {621-631},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {Designing Cell-Type-Specific Genome-wide Experiments},
 url = {https://doi.org/10.1016/j.molcel.2015.04.024},
 volume = {58},
 year = {2015},
}

@article{Kolodziejczyk2015-xy,
 author = {Kolodziejczyk, Aleksandra A. and Kim, Jong Kyoung and Svensson, Valentine and Marioni, John C. and Teichmann, Sarah A.},
 doi = {10.1016/j.molcel.2015.04.005},
 issn = {1097-2765},
 journal = {Molecular Cell},
 month = may,
 number = {4},
 pages = {610-620},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {The Technology and Biology of Single-Cell {RNA} Sequencing},
 url = {https://doi.org/10.1016/j.molcel.2015.04.005},
 volume = {58},
 year = {2015},
}

@article{Kharchenko2014-ts,
 author = {Kharchenko, Peter V and Silberstein, Lev and Scadden, David T},
 doi = {10.1038/nmeth.2967},
 issn = {1548-7091, 1548-7105},
 journal = {Nat Meth},
 month = may,
 number = {7},
 pages = {740-742},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Bayesian approach to single-cell differential expression analysis},
 url = {https://doi.org/10.1038/nmeth.2967},
 volume = {11},
 year = {2014},
}

@article{Jiang2011-mu,
 author = {Jiang, L. and Schlesinger, F. and Davis, C. A. and Zhang, Y. and Li, R. and Salit, M. and Gingeras, T. R. and Oliver, B.},
 doi = {10.1101/gr.121095.111},
 issn = {1088-9051},
 journal = {Genome Research},
 month = aug,
 number = {9},
 pages = {1543-1551},
 publisher = {Cold Spring Harbor Laboratory},
 source = {Crossref},
 title = {Synthetic spike-in standards for {RNA}-seq experiments},
 url = {https://doi.org/10.1101/gr.121095.111},
 volume = {21},
 year = {2011},
}

@article{Kivioja2012-yt,
 author = {Kivioja, Teemu and Vähärautio, Anna and Karlsson, Kasper and Bonke, Martin and Enge, Martin and Linnarsson, Sten and Taipale, Jussi},
 doi = {10.1038/nmeth.1778},
 issn = {1548-7091, 1548-7105},
 journal = {Nat Meth},
 month = nov,
 number = {1},
 pages = {72-74},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Counting absolute numbers of molecules using unique molecular identifiers},
 url = {https://doi.org/10.1038/nmeth.1778},
 volume = {9},
 year = {2011},
}

@article{Stegle2015-uv,
 author = {Stegle, Oliver and Teichmann, Sarah A. and Marioni, John C.},
 doi = {10.1038/nrg3833},
 issn = {1471-0056, 1471-0064},
 journal = {Nat Rev Genet},
 month = jan,
 number = {3},
 pages = {133-145},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Computational and analytical challenges in single-cell transcriptomics},
 url = {https://doi.org/10.1038/nrg3833},
 volume = {16},
 year = {2015},
}

@article{Levine2015-fk,
 author = {Levine, Jacob H. and Simonds, Erin F. and Bendall, Sean C. and Davis, Kara L. and Amir, El-ad D. and Tadmor, Michelle D. and Litvin, Oren and Fienberg, Harris G. and Jager, Astraea and Zunder, Eli R. and Finck, Rachel and Gedman, Amanda L. and Radtke, Ina and Downing, James R. and Pe’er, Dana and Nolan, Garry P.},
 doi = {10.1016/j.cell.2015.05.047},
 issn = {0092-8674},
 journal = {Cell},
 month = jul,
 number = {1},
 pages = {184-197},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {Data-Driven Phenotypic Dissection of {AML} Reveals Progenitor-like Cells that Correlate with Prognosis},
 url = {https://doi.org/10.1016/j.cell.2015.05.047},
 volume = {162},
 year = {2015},
}

@article{Tung2017-ba,
 author = {Tung, Po-Yuan and Blischak, John D. and Hsiao, Chiaowen Joyce and Knowles, David A. and Burnett, Jonathan E. and Pritchard, Jonathan K. and Gilad, Yoav},
 doi = {10.1038/srep39921},
 issn = {2045-2322},
 journal = {Sci. Rep.},
 month = jan,
 pages = {39921},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Batch effects and the effective design of single-cell gene expression studies},
 url = {https://doi.org/10.1038/srep39921},
 volume = {7},
 year = {2017},
}

@article{Archer2016-zq,
 author = {Archer, Nathan and Walsh, Mark D. and Shahrezaei, Vahid and Hebenstreit, Daniel},
 doi = {10.1016/j.cels.2016.10.012},
 issn = {2405-4712},
 journal = {Cell Systems},
 month = nov,
 number = {5},
 pages = {467-479.e12},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {Modeling Enzyme Processivity Reveals that {RNA}-Seq Libraries Are Biased in Characteristic and Correctable Ways},
 url = {https://doi.org/10.1016/j.cels.2016.10.012},
 volume = {3},
 year = {2016},
}

@article{Ziegenhain2017-cu,
 author = {Ziegenhain, Christoph and Vieth, Beate and Parekh, Swati and Reinius, Björn and Guillaumet-Adkins, Amy and Smets, Martha and Leonhardt, Heinrich and Heyn, Holger and Hellmann, Ines and Enard, Wolfgang},
 doi = {10.1016/j.molcel.2017.01.023},
 issn = {1097-2765},
 journal = {Molecular Cell},
 month = feb,
 number = {4},
 pages = {631-643.e4},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {Comparative Analysis of Single-Cell {RNA} Sequencing Methods},
 url = {https://doi.org/10.1016/j.molcel.2017.01.023},
 volume = {65},
 year = {2017},
}

@article{Welch2016-jr,
 author = {Welch, Joshua D. and Hartemink, Alexander J. and Prins, Jan F.},
 doi = {10.1186/s13059-016-0975-3},
 issn = {1474-760X},
 journal = {Genome Biol},
 month = may,
 number = {1},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {{SLICER:} {Inferring} branched, nonlinear cellular trajectories from single cell {RNA}-seq data},
 url = {https://doi.org/10.1186/s13059-016-0975-3},
 volume = {17},
 year = {2016},
}

@article{Cannoodt2016-uj,
 author = {Cannoodt, Robrecht and Saelens, Wouter and Saeys, Yvan},
 doi = {10.1002/eji.201646347},
 issn = {0014-2980},
 journal = {Eur. J. Immunol.},
 month = oct,
 number = {11},
 pages = {2496-2506},
 publisher = {Wiley-Blackwell},
 source = {Crossref},
 title = {Computational methods for trajectory inference from single-cell transcriptomics},
 url = {https://doi.org/10.1002/eji.201646347},
 volume = {46},
 year = {2016},
}

@article{Pollen2014-cu,
 author = {Pollen, Alex A and Nowakowski, Tomasz J and Shuga, Joe and Wang, Xiaohui and Leyrat, Anne A and Lui, Jan H and Li, Nianzhen and Szpankowski, Lukasz and Fowler, Brian and Chen, Peilin and Ramalingam, Naveen and Sun, Gang and Thu, Myo and Norris, Michael and Lebofsky, Ronald and Toppani, Dominique and Kemp, Darnell W and Wong, Michael and Clerkson, Barry and Jones, Brittnee N and Wu, Shiquan and Knutsson, Lawrence and Alvarado, Beatriz and Wang, Jing and Weaver, Lesley S and May, Andrew P and Jones, Robert C and Unger, Marc A and Kriegstein, Arnold R and West, Jay A A},
 doi = {10.1038/nbt.2967},
 issn = {1087-0156, 1546-1696},
 journal = {Nat Biotechnol},
 month = aug,
 number = {10},
 pages = {1053-1058},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Low-coverage single-cell {mRNA} sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex},
 url = {https://doi.org/10.1038/nbt.2967},
 volume = {32},
 year = {2014},
}

@article{McCarthy2017-kb,
 author = {McCarthy, Davis J. and Campbell, Kieran R. and Lun, Aaron T. L. and Wills, Quin F.},
 doi = {10.1093/bioinformatics/btw777},
 issn = {1367-4803, 1460-2059},
 journal = {Bioinformatics},
 month = jan,
 pages = {btw777},
 publisher = {Oxford University Press (OUP)},
 source = {Crossref},
 title = {Scater: {{Pre}-processing,} quality control, normalization and visualization of single-cell {RNA}-seq data in R},
 url = {https://doi.org/10.1093/bioinformatics/btw777},
 year = {2017},
}

@article{Svensson2017-op,
 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},
 doi = {10.1038/nmeth.4220},
 issn = {1548-7091, 1548-7105},
 journal = {Nat Meth},
 month = mar,
 number = {4},
 pages = {381-387},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Power analysis of single-cell {RNA}-sequencing experiments},
 url = {https://doi.org/10.1038/nmeth.4220},
 volume = {14},
 year = {2017},
}

@article{Hashimshony2016-lx,
 author = {Hashimshony, Tamar and Senderovich, Naftalie and Avital, Gal and Klochendler, Agnes and de Leeuw, Yaron and Anavy, Leon and Gennert, Dave and Li, Shuqiang and Livak, Kenneth J. and Rozenblatt-Rosen, Orit and Dor, Yuval and Regev, Aviv and Yanai, Itai},
 doi = {10.1186/s13059-016-0938-8},
 issn = {1474-760X},
 journal = {Genome Biol},
 month = apr,
 number = {1},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {{CEL}-Seq2: {Sensitive} highly-multiplexed single-cell {RNA}-Seq},
 url = {https://doi.org/10.1186/s13059-016-0938-8},
 volume = {17},
 year = {2016},
}

@article{Klein2015-kz,
 author = {Klein, Allon M. and Mazutis, Linas and Akartuna, Ilke and Tallapragada, Naren and Veres, Adrian and Li, Victor and Peshkin, Leonid and Weitz, David A. and Kirschner, Marc W.},
 doi = {10.1016/j.cell.2015.04.044},
 issn = {0092-8674},
 journal = {Cell},
 month = may,
 number = {5},
 pages = {1187-1201},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells},
 url = {https://doi.org/10.1016/j.cell.2015.04.044},
 volume = {161},
 year = {2015},
}

@article{Jaitin2014-ko,
 author = {Jaitin, Diego Adhemar and Kenigsberg, Ephraim and Keren-Shaul, Hadas and Elefant, Naama and Paul, Franziska and Zaretsky, Irina and Mildner, Alexander and Cohen, Nadav and Jung, Steffen and Tanay, Amos and Amit, Ido},
 abstract = {In multicellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless, dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing (RNA-seq) approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell-type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed preprogrammed cell-type hierarchy. These data demonstrate single-cell RNA-seq as an effective tool for comprehensive cellular decomposition of complex tissues.},
 affiliation = {Department of Immunology, Weizmann Institute, Rehovot 76100, Israel.},
 journal = {Science},
 month = feb,
 number = {6172},
 pages = {776--779},
 title = {Massively parallel single-cell {{RNA}-seq} for marker-free decomposition of tissues into cell types},
 volume = {343},
 year = {2014},
}

@article{Soumillon2014-eu,
 author = {Soumillon, Magali and Cacchiarelli, Davide and Semrau, Stefan and van Oudenaarden, Alexander and Mikkelsen, Tarjei S},
 abstract = {Directed differentiation of cells in vitro is a powerful approach for dissection of developmental pathways, disease modeling and regenerative medicine, but analysis of such systems is complicated by heterogeneous and asynchronous cellular responses to differentiation-inducing stimuli. To enable deep characterization of heterogeneous cell populations, we developed an efficient digital gene expression profiling protocol that enables surveying of mRNA in thousands of single cells at a time. We then applied this protocol to profile 12,832 cells collected at multiple time points during directed adipogenic differentiation of human adipose-derived stem/stromal cells in vitro. The resulting data reveal the major axes of cell-to-cell variation within and between time points, and an inverse relationship between inflammatory gene expression and lipid accumulation across cells from a single donor.},
 journal = {bioRxiv},
 language = {en},
 month = mar,
 pages = {003236},
 title = {Characterization of directed differentiation by high-throughput single-cell {{RNA}-Seq}},
 year = {2014},
}

@article{Gierahn2017-es,
 author = {Gierahn, Todd M and Wadsworth, 2nd, Marc H and Hughes, Travis K and Bryson, Bryan D and Butler, Andrew and Satija, Rahul and Fortune, Sarah and Love, J Christopher and Shalek, Alex K},
 abstract = {Single-cell RNA-seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA-seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We use Seq-Well to profile thousands of primary human macrophages exposed to Mycobacterium tuberculosis.},
 affiliation = {Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts, USA. Institute for Medical Engineering \&Science (IMES) and Department of Chemistry, MIT, Cambridge, Massachusetts, USA. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA. Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA. Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA. New York Genome Center, New York, New York, USA.},
 journal = {Nat. Methods},
 language = {en},
 month = apr,
 number = {4},
 pages = {395--398},
 title = {{{Seq}-Well}: {Portable,} low-cost {RNA} sequencing of single cells at high throughput},
 volume = {14},
 year = {2017},
}

@article{Picelli2014-ic,
 author = {Picelli, Simone and Faridani, Omid R and Bj{\"o}rklund, Asa K and Winberg, G{\"o}sta and Sagasser, Sven and Sandberg, Rickard},
 abstract = {Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.},
 affiliation = {Ludwig Institute for Cancer Research, Stockholm, Sweden. 1] Ludwig Institute for Cancer Research, Stockholm, Sweden. [2] Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.},
 journal = {Nat. Protoc.},
 language = {en},
 month = jan,
 number = {1},
 pages = {171--181},
 title = {Full-length {{RNA}-seq} from single cells using Smart-seq2},
 volume = {9},
 year = {2014},
}

@article{Islam2014-cn,
 author = {Islam, Saiful and Zeisel, Amit and Joost, Simon and La Manno, Gioele and Zajac, Pawel and Kasper, Maria and Lönnerberg, Peter and Linnarsson, Sten},
 doi = {10.1038/nmeth.2772},
 issn = {1548-7091, 1548-7105},
 journal = {Nat Meth},
 month = dec,
 number = {2},
 pages = {163-166},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Quantitative single-cell {RNA}-seq with unique molecular identifiers},
 url = {https://doi.org/10.1038/nmeth.2772},
 volume = {11},
 year = {2013},
}

@article{Buttner2017-ds,
 author = {Buttner, Maren and Miao, Zhichao and Wolf, Alexander and Teichmann, Sarah A and Theis, Fabian J},
 abstract = {Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations. As with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch effect correction is often evaluated by visual inspection of dimension reduced representations such as principal component analysis. This is inherently imprecise due to the high number of genes and non-normal distribution of gene expression. Here, we present a k-nearest neighbour batch effect test (kBET, https://github.com/theislab/kBET) to quantitatively measure batch effects. kBET is easier to interpret, more sensitive and more robust than visual evaluation and other measures of batch effects. We use kBET to assess commonly used batch regression and normalisation approaches, and quantify the extent to which they remove batch effects while preserving biological variability. Our results illustrate that batch correction based on log-transformation or scran pooling followed by ComBat reduced the batch effect while preserving structure across data sets. Finally we show that kBET can pinpoint successful data integration methods across multiple data sets, in this case from different publications all charting mouse embryonic development. This has important implications for future data integration efforts, which will be central to projects such as the Human Cell Atlas where data for the same tissue may be generated in multiple locations around the world.},
 journal = {bioRxiv},
 language = {en},
 month = oct,
 pages = {200345},
 title = {Assessment of batch-correction methods for {{scRNA}-seq} data with a new test metric},
 year = {2017},
}

@article{Van_Dijk2017-bh,
 author = {van Dijk, David and Nainys, Juozas and Sharma, Roshan and Kathail, Pooja and Carr, Ambrose J and Moon, Kevin R and Mazutis, Linas and Wolf, Guy and Krishnaswamy, Smita and Pe'er, Dana},
 abstract = {Single-cell RNA-sequencing is fast becoming a major technology that is revolutionizing biological discovery in fields such as development, immunology and cancer. The ability to simultaneously measure thousands of genes at single cell resolution allows, among other prospects, for the possibility of learning gene regulatory networks at large scales. However, scRNA-seq technologies suffer from many sources of significant technical noise, the most prominent of which is dropout due to inefficient mRNA capture. This results in data that has a high degree of sparsity, with typically only 10\% non-zero values. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method for imputing missing values, and restoring the structure of the data. After MAGIC, we find that two- and three-dimensional gene interactions are restored and that MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and our newly generated epithelial-to-mesenchymal transition dataset.},
 journal = {bioRxiv},
 language = {en},
 month = feb,
 pages = {111591},
 title = {{{MAGIC}:} {A} diffusion-based imputation method reveals gene-gene interactions in single-cell {{RNA}-sequencing} data},
 year = {2017},
}

@article{Li2017-tz,
 author = {Li, Wei Vivian and Li, Jingyi Jessica},
 abstract = {The analysis of single-cell RNA-seq (scRNA-seq) data is complicated and biased by excess zero or near zero counts, the so-called dropouts due to the low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute is shown as an effective tool to enhance the clustering of cell populations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics in time series scRNA-seq experiments.},
 journal = {bioRxiv},
 language = {en},
 month = jun,
 pages = {141598},
 title = {{scImpute:} {Accurate} And Robust Imputation For Single Cell {{RNA}-Seq} Data},
 year = {2017},
}

@article{Blondel2008-px,
 author = {Blondel, Vincent D and Guillaume, Jean-Loup and Lambiotte, Renaud and Lefebvre, Etienne},
 doi = {10.1088/1742-5468/2008/10/p10008},
 issn = {1742-5468},
 journal = {J. Stat. Mech.},
 month = oct,
 number = {10},
 pages = {P10008},
 publisher = {IOP Publishing},
 source = {Crossref},
 title = {Fast unfolding of communities in large networks},
 url = {https://doi.org/10.1088/1742-5468/2008/10/p10008},
 volume = {2008},
 year = {2008},
}

@article{Kiselev2017-nb,
 author = {Kiselev, Vladimir Yu and Hemberg, Martin},
 abstract = {Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. Here, we present scmap (http://bioconductor.org/packages/scmap), a method for projecting cells from a scRNA-seq experiment onto the cell-types identified in other experiments (the application can be run for free, without restrictions, from http://www.hemberg-lab.cloud/scmap).},
 journal = {bioRxiv},
 language = {en},
 month = jul,
 pages = {150292},
 title = {scmap - A tool for unsupervised projection of single cell {{RNA}-seq} data},
 year = {2017},
}

@article{Muraro2016-yk,
 author = {Muraro, Mauro J. and Dharmadhikari, Gitanjali and Grün, Dominic and Groen, Nathalie and Dielen, Tim and Jansen, Erik and van Gurp, Leon and Engelse, Marten A. and Carlotti, Francoise and de Koning, Eelco J.P. and van Oudenaarden, Alexander},
 doi = {10.1016/j.cels.2016.09.002},
 issn = {2405-4712},
 journal = {Cell Systems},
 month = oct,
 number = {4},
 pages = {385-394.e3},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {A Single-Cell Transcriptome Atlas of the Human Pancreas},
 url = {https://doi.org/10.1016/j.cels.2016.09.002},
 volume = {3},
 year = {2016},
}

@article{Segerstolpe2016-wc,
 author = {Segerstolpe, Åsa and Palasantza, Athanasia and Eliasson, Pernilla and Andersson, Eva-Marie and Andréasson, Anne-Christine and Sun, Xiaoyan and Picelli, Simone and Sabirsh, Alan and Clausen, Maryam and Bjursell, Magnus K. and Smith, David M. and Kasper, Maria and Ämmälä, Carina and Sandberg, Rickard},
 doi = {10.1016/j.cmet.2016.08.020},
 issn = {1550-4131},
 journal = {Cell Metabolism},
 month = oct,
 number = {4},
 pages = {593-607},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes},
 url = {https://doi.org/10.1016/j.cmet.2016.08.020},
 volume = {24},
 year = {2016},
}

@article{Regev2017-mw,
 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},
 language = {en},
 month = may,
 pages = {121202},
 title = {The Human Cell Atlas},
 year = {2017},
}

@article{Altschul1990-ts,
 author = {Altschul, Stephen F. and Gish, Warren and Miller, Webb and Myers, Eugene W. and Lipman, David J.},
 doi = {10.1016/s0022-2836(05)80360-2},
 issn = {0022-2836},
 journal = {Journal of Molecular Biology},
 month = oct,
 number = {3},
 pages = {403-410},
 publisher = {Elsevier BV},
 source = {Crossref},
 title = {Basic local alignment search tool},
 url = {https://doi.org/10.1016/s0022-2836(05)80360-2},
 volume = {215},
 year = {1990},
}

@article{Anders2010-jr,
 author = {Anders, Simon and Huber, Wolfgang},
 doi = {10.1186/gb-2010-11-10-r106},
 issn = {1465-6906},
 journal = {Genome Biol},
 number = {10},
 pages = {R106},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Differential expression analysis for sequence count data},
 url = {https://doi.org/10.1186/gb-2010-11-10-r106},
 volume = {11},
 year = {2010},
}

@article{Bullard2010-eb,
 author = {Bullard, James H and Purdom, Elizabeth and Hansen, Kasper D and Dudoit, Sandrine},
 doi = {10.1186/1471-2105-11-94},
 issn = {1471-2105},
 journal = {BMC Bioinformatics},
 number = {1},
 pages = {94},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Evaluation of statistical methods for normalization and differential expression in {mRNA}-Seq experiments},
 url = {https://doi.org/10.1186/1471-2105-11-94},
 volume = {11},
 year = {2010},
}

@article{Robinson2010-hz,
 author = {Robinson, Mark D and Oshlack, Alicia},
 doi = {10.1186/gb-2010-11-3-r25},
 issn = {1465-6906},
 journal = {Genome Biol},
 number = {3},
 pages = {R25},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {A scaling normalization method for differential expression analysis of {RNA}-seq data},
 url = {https://doi.org/10.1186/gb-2010-11-3-r25},
 volume = {11},
 year = {2010},
}

@article{L_Lun2016-pq,
 author = {L. Lun, Aaron T. and Bach, Karsten and Marioni, John C.},
 doi = {10.1186/s13059-016-0947-7},
 issn = {1474-760X},
 journal = {Genome Biol},
 month = apr,
 number = {1},
 publisher = {Springer Nature},
 source = {Crossref},
 title = {Pooling across cells to normalize single-cell {RNA} sequencing data with many zero counts},
 url = {https://doi.org/10.1186/s13059-016-0947-7},
 volume = {17},
 year = {2016},
}

@article{Haghverdi2017-vh,
 author = {Haghverdi, Laleh and Lun, Aaron T L and Morgan, Michael D and Marioni, John C},
 abstract = {The presence of batch effects is a well-known problem in experimental data analysis, and single-cell RNA sequencing (scRNA-seq) is no exception. Large-scale scRNA-seq projects that generate data from different laboratories and at different times are rife with batch effects that can fatally compromise integration and interpretation of the data. In such cases, computational batch correction is critical for eliminating uninteresting technical factors and obtaining valid biological conclusions. However, existing methods assume that the composition of cell populations are either known or the same across batches. Here, we present a new strategy for batch correction based on the detection of mutual nearest neighbours in the high-dimensional expression space. Our approach does not rely on pre-defined or equal population compositions across batches, only requiring that a subset of the population be shared between batches. We demonstrate the superiority of our approach over existing methods on a range of simulated and real scRNA-seq data sets. We also show how our method can be applied to integrate scRNA-seq data from two separate studies of early embryonic development.},
 journal = {bioRxiv},
 language = {en},
 month = jul,
 pages = {165118},
 title = {Correcting batch effects in single-cell {RNA} sequencing data by matching mutual nearest neighbours},
 year = {2017},
}

@article{kolodziejczyk_2015,
title = {Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation.},
author = {Kolodziejczyk, Aleksandra A and Kim, Jong Kyoung and Tsang, Jason C H and Ilicic, Tomislav and Henriksson, Johan and Natarajan, Kedar N and Tuck, Alex C and Gao, Xuefei and Bühler, Marc and Liu, Pentao and Marioni, John C and Teichmann, Sarah A},
pages = {471-485},
url = {http://dx.doi.org/10.1016/j.stem.2015.09.011},
year = {2015},
month = {oct},
day = {1},
urldate = {2017-08-21},
journal = {Cell Stem Cell},
volume = {17},
number = {4},
doi = {10.1016/j.stem.2015.09.011},
pmid = {26431182},
pmcid = {PMC4595712},
keywords = {supplementary},
abstract = {Embryonic stem cell (ESC) culture conditions are important for maintaining long-term self-renewal, and they influence cellular pluripotency state. Here, we report single cell RNA-sequencing of mESCs cultured in three different conditions: serum, 2i, and the alternative ground state a2i. We find that the cellular transcriptomes of cells grown in these conditions are distinct, with 2i being the most similar to blastocyst cells and including a subpopulation resembling the two-cell embryo state. Overall levels of intercellular gene expression heterogeneity are comparable across the three conditions. However, this masks variable expression of pluripotency genes in serum cells and homogeneous expression in 2i and a2i cells. Additionally, genes related to the cell cycle are more variably expressed in the 2i and a2i conditions. Mining of our dataset for correlations in gene expression allowed us to identify additional components of the pluripotency network, including Ptma and Zfp640, illustrating its value as a resource for future discovery. Copyright \copyright 2015 The Authors. Published by Elsevier Inc. All rights reserved.}
}
@article{dobin_2013,
title = {STAR: ultrafast universal RNA-seq aligner.},
author = {Dobin, Alexander and Davis, Carrie A and Schlesinger, Felix and Drenkow, Jorg and Zaleski, Chris and Jha, Sonali and Batut, Philippe and Chaisson, Mark and Gingeras, Thomas R},
pages = {15-21},
url = {http://dx.doi.org/10.1093/bioinformatics/bts635},
year = {2013},
month = {jan},
day = {1},
urldate = {2016-04-25},
journal = {Bioinformatics},
volume = {29},
number = {1},
doi = {10.1093/bioinformatics/bts635},
pmid = {23104886},
pmcid = {PMC3530905},
abstract = {MOTIVATION: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. RESULTS: To align our large (\textgreater80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of \textgreater50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90\% success rate, corroborating the high precision of the STAR mapping strategy. AVAILABILITY AND IMPLEMENTATION: STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.}
}
@article{bray_2016,
title = {Near-optimal probabilistic RNA-seq quantification.},
author = {Bray, Nicolas L and Pimentel, Harold and Melsted, Páll and Pachter, Lior},
pages = {525-527},
url = {http://www.nature.com/doifinder/10.1038/nbt.3519},
year = {2016},
month = {apr},
day = {4},
urldate = {2017-08-21},
journal = {Nat Biotechnol},
volume = {34},
number = {5},
issn = {1087-0156},
doi = {10.1038/nbt.3519},
pmid = {27043002},
keywords = {supplementary},
abstract = {We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in \textless 10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.}
}
@article{wickham_2014,
title = {Tidy Data},
author = {Wickham, Hadley},
url = {http://www.jstatsoft.org/v59/i10/},
year = {2014},
urldate = {2018-01-19},
journal = {J Stat Softw},
volume = {59},
number = {10},
issn = {1548-7660},
doi = {10.18637/jss.v059.i10}
}