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@article{McCarthy2017,
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 conveni-ent, 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 flex-ible 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, vi-gnettes and case studies, is available through Bioconductor at},
author = {McCarthy, Davis J. and Campbell, Kieran R. and Lun, Aaron T.L. and Wills, Quin F.},
doi = {10.1093/bioinformatics/btw777},
file = {:Users/nils/Documents/Mendeley Desktop/btw777.pdf:pdf},
isbn = {1367-4811 (Electronic)
1367-4803 (Linking)},
issn = {14602059},
journal = {Bioinformatics},
number = {8},
pages = {1179--1186},
pmid = {28088763},
title = {{Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R}},
volume = {33},
year = {2017}
}
@article{Katayama2013,
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).$\backslash$n$\backslash$nCONTACT: shintaro.katayama@ki.se},
author = {Katayama, Shintaro and T{\"{o}}h{\"{o}}nen, Virpi and Linnarsson, Sten and Kere, Juha},
doi = {10.1093/bioinformatics/btt511},
file = {:Users/nils/Documents/Mendeley Desktop/Katayama et al. - 2013 - SAMstrt Statistical test for differential expression in single-cell transcriptome with spike-in normalization.pdf:pdf},
isbn = {1367-4811 (Electronic)
1367-4803 (Linking)},
issn = {13674803},
journal = {Bioinformatics},
number = {22},
pages = {2943--2945},
pmid = {23995393},
title = {{SAMstrt: Statistical test for differential expression in single-cell transcriptome with spike-in normalization}},
volume = {29},
year = {2013}
}

@article{Robinson2010,
  title = {{{edgeR}}: A {{Bioconductor}} Package for Differential Expression Analysis of Digital Gene Expression Data},
  shorttitle = {{{edgeR}}},
  author = {Robinson, M. D. and McCarthy, D. J. and Smyth, G. K.},
  year = {2010},
  month = jan,
  journal = {Bioinformatics},
  volume = {26},
  number = {1},
  pages = {139--140},
  issn = {1367-4803, 1460-2059},
  doi = {10.1093/bioinformatics/btp616},
  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.},
  langid = {english},
  file = {/home/alan/Documents/Papers/Robinson et al. - 2010 - edgeR a Bioconductor package for differential exp.pdf}
}
@article{Carpenter2017,
 title={Stan: A Probabilistic Programming Language},
 volume={76},
 url={https://www.jstatsoft.org/index.php/jss/article/view/v076i01},
 doi={10.18637/jss.v076.i01},
 abstract={Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.},
 number={1},
 journal={Journal of Statistical Software},
 author={Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
 year={2017},
 pages={1--32}
}

@article{Bacher2016,
  title = {Design and Computational Analysis of Single-Cell {{RNA-sequencing}} Experiments},
  author = {Bacher, Rhonda and Kendziorski, Christina},
  year = {2016},
  month = dec,
  journal = {Genome Biology},
  volume = {17},
  number = {1},
  pages = {63},
  issn = {1474-760X},
  doi = {10.1186/s13059-016-0927-y},
  urldate = {2023-08-05},
  langid = {english},
  file = {/home/alan/Zotero/storage/M4EQEK5E/Bacher and Kendziorski - 2016 - Design and computational analysis of single-cell R.pdf}
}

@article{Delmans2016,
abstract = {BACKGROUND The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions. RESULTS We present a discrete, distributional method for differential gene expression (D(3)E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D(3)E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D(3)E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D(3)E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression. CONCLUSION Evaluation using synthetic data shows that D(3)E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D(3)E makes it possible to gain additional biological insights.},
author = {Delmans, Mihails and Hemberg, Martin},
doi = {10.1186/s12859-016-0944-6},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {110,1186,17,2016,bioinformatics,doi 10,mans and hemberg bmc,s12859-016-0944-6},
number = {1},
pages = {110},
pmid = {26927822},
publisher = {BMC Bioinformatics},
title = {{Discrete distributional differential expression (D(3)E) - a tool for gene expression analysis of single-cell RNA-seq data.}},
url = {http://dx.doi.org/10.1186/s12859-016-0944-6{\%}5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/26927822{\%}5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4772470},
volume = {17},
year = {2016}
}
@article{Lun2016,
author = {Lun, Aaron T. L. and McCarthy, Davis J. and Marioni, John C.},
doi = {10.12688/f1000research.9501.1},
file = {:Users/nils/Documents/Mendeley Desktop/Lun, McCarthy, Marioni - 2016 - A step-by-step workflow for basic analyses of single-cell RNA-seq data.pdf:pdf},
issn = {2046-1402},
journal = {F1000Research},
number = {2122},
title = {{A step-by-step workflow for basic analyses of single-cell RNA-seq data}},
volume = {5},
year = {2016}
}
@article{Vallejos2015,
author = {Vallejos, Catalina A. and Marioni, John C. and Richardson, Sylvia},
doi = {10.1371/journal.pcbi.1004333},
file = {:Users/nils/Documents/Mendeley Desktop/Vallejos, Marioni, Richardson - 2015 - BASiCS Bayesian Analysis of Single-Cell Sequencing Data.pdf:pdf},
issn = {1553-7358},
journal = {PLOS Computational Biology},
pages = {e1004333},
title = {{BASiCS: Bayesian analysis of single-cell sequencing data}},
url = {http://dx.plos.org/10.1371/journal.pcbi.1004333},
volume = {11},
year = {2015}
}
@misc{Brennecke2013,
abstract = {Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.},
author = {Brennecke, Philip and Anders, Simon and Kim, Jong Kyoung and Ko{\l}odziejczyk, Aleksandra A and Zhang, Xiuwei and Proserpio, Valentina and Baying, Bianka and Benes, Vladimir and Teichmann, Sarah a and Marioni, John C and Heisler, Marcus G},
booktitle = {Nature methods},
doi = {10.1038/nmeth.2645},
file = {:Users/nils/Documents/Mendeley Desktop/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-seq experiments(4).pdf:pdf},
isbn = {0000000000},
issn = {1548-7105},
keywords = {RNA,RNA: methods,Sequence Analysis,Single-Cell Analysis},
number = {11},
pages = {1093--5},
pmid = {24056876},
title = {{Accounting for technical noise in single-cell RNA-seq experiments.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24056876},
volume = {10},
year = {2013}
}
@article{Lonnberg2017,
abstract = {Differentiation of na{\"{i}}ve CD4(+) T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to extensive heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell transcriptomics and computational analysis using a temporal mixtures of Gaussian processes model, termed GPfates, we reconstructed the developmental trajectories of Th1 and Tfh cells during blood-stage Plasmodium infection in mice. By tracking clonality using endogenous TCR sequences, we first demonstrated that Th1/Tfh bifurcation had occurred at both population and single-clone levels. Next, we identified genes whose expression was associated with Th1 or Tfh fates, and demonstrated a T-cell intrinsic role for Galectin-1 in supporting a Th1 differentiation. We also revealed the close molecular relationship between Th1 and IL-10-producing Tr1 cells in this infection. Th1 and Tfh fates emerged from a highly proliferative precursor that upregulated aerobic glycolysis and accelerated cell cycling as cytokine expression began. Dynamic gene expression of chemokine receptors around bifurcation predicted roles for cell-cell in driving Th1/Tfh fates. In particular, we found that precursor Th cells were coached towards a Th1 but not a Tfh fate by inflammatory monocytes. Thus, by integrating genomic and computational approaches, our study has provided two unique resources, a database www.PlasmoTH.org, which facilitates discovery of novel factors controlling Th1/Tfh fate commitment, and more generally, GPfates, a modelling framework for characterizing cell differentiation towards multiple fates.},
author = {L{\"{o}}nnberg, Tapio and Svensson, Valentine and James, Kylie R. and Fernandez-Ruiz, Daniel and Sebina, Ismail and Montandon, Ruddy and Soon, Megan S. F. and Fogg, Lily G. and Nair, Arya Sheela and Liligeto, Urijah N. and Stubbington, Michael J. T. and Ly, Lam-Ha and Bagger, Frederik Otzen and Zwiessele, Max and Lawrence, Neil D. and Souza-Fonseca-Guimaraes, Fernando and Bunn, Patrick T. and Engwerda, Christian R. and Heath, William R. and Billker, Oliver and Stegle, Oliver and Haque, Ashraful and Teichmann, Sarah A.},
doi = {10.1126/sciimmunol.aal2192},
file = {:Users/nils/Documents/Mendeley Desktop/lnnberg2017.pdf:pdf},
issn = {2470-9468},
journal = {Science Immunology},
number = {9},
pages = {eaal2192},
pmid = {28345074},
title = {{Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves Th1/Tfh fate bifurcation in malaria}},
url = {http://immunology.sciencemag.org/lookup/doi/10.1126/sciimmunol.aal2192},
volume = {2},
year = {2017}
}
@article{Rna2005,
abstract = {The External RNA Control Consortium (ERCC) is an ad-hoc group with approximately 70 members from private, public, and academic organizations. The group is developing a set of external RNA control transcripts that can be used to assess technical performance in gene expression assays. The ERCC is now initiating the Testing Phase of the project, during which candidate external RNA controls will be evaluated in both microarray and QRT-PCR gene expression platforms. This document describes the proposed experiments and informatics process that will be followed to test and qualify individual controls. The ERCC is distributing this description of the proposed testing process in an effort to gain consensus and to encourage feedback from the scientific community. On October 4-5, 2005, the ERCC met to further review the document, clarify ambiguities, and plan next steps. A summary of this meeting and changes to the test plan are provided as an appendix to this manuscript.},
author = {{External RNA Controls Consortium}},
doi = {10.1186/1471-2164-6-150},
file = {:Users/nils/Documents/Mendeley Desktop/External RNA Controls Consortium - 2005 - Proposed methods for testing and selecting the ERCC external RNA controls.pdf:pdf},
isbn = {1471-2164 (Electronic) 1471-2164 (Linking)},
issn = {1471-2164},
journal = {BMC Genomics},
number = {June 2004},
pages = {150},
pmid = {16266432},
title = {{Proposed methods for testing and selecting the ERCC external RNA controls.}},
volume = {6},
year = {2005}
}
@article{Tung2016,
abstract = {Single cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to account for amplification bias. We found that the major source of variation in the gene expression data was driven by genotype, but we also observed substantial variation between the technical replicates. We observed that the conversion of reads to molecules using the UMIs was impacted by both biological and technical variation, indicating that UMI counts are not an unbiased estimator of gene expression levels. Based on our results, we suggest a framework for effective scRNA-seq studies.},
author = {Tung, Po-Yuan and Blischak, John D and Hsiao, Chiaowen and Knowles, David A and Burnett, Jonathan E and Pritchard, Jonathan K and Gilad, Yoav},
doi = {10.1101/062919},
file = {:Users/nils/Documents/Mendeley Desktop/062919.full-2.pdf:pdf},
journal = {bioRxiv},
pages = {062919},
title = {{Batch effects and the effective design of single-cell gene expression studies}},
url = {http://biorxiv.org/lookup/doi/10.1101/062919},
year = {2016}
}
@article{Grun2014,
abstract = {Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition.},
author = {Gr{\"{u}}n, Dominic and Kester, Lennart and van Oudenaarden, Alexander},
doi = {10.1038/nmeth.2930},
file = {:Users/nils/Documents/Mendeley Desktop/Gr{\"{u}}n, Kester, van Oudenaarden - 2014 - Validation of noise models for single-cell transcriptomics.pdf:pdf},
issn = {1548-7105},
journal = {Nature methods},
keywords = {Animals,Embryonic Stem Cells,Embryonic Stem Cells: metabolism,Gene Expression Profiling,Gene Expression Profiling: methods,Gene Expression Profiling: standards,Gene Expression Regulation,Mice,Models, Biological,Observer Variation,Selection Bias,Sequence Analysis, RNA,Signal-To-Noise Ratio,Transcription Factors,Transcription Factors: metabolism},
month = {jun},
number = {6},
pages = {637--40},
pmid = {24747814},
title = {{Validation of noise models for single-cell transcriptomics.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24747814},
volume = {11},
year = {2014}
}
@article{Proserpio2016,
author = {Proserpio, Valentina and Piccolo, Andrea and Haim-Vilmovsky, Liora and Kar, Gozde and L{\"{o}}nnberg, Tapio and Svensson, Valentine and Pramanik, Jhuma and Natarajan, Kedar Nath and Zhai, Weichao and Zhang, Xiuwei and Donati, Giacomo and Kayikci, Melis and Kotar, Jurij and McKenzie, Andrew N. J. and Montandon, Ruddy and Billker, Oliver and Woodhouse, Steven and Cicuta, Pietro and Nicodemi, Mario and Teichmann, Sarah A.},
doi = {10.1186/s13059-016-0957-5},
file = {:Users/nils/Documents/Mendeley Desktop/Proserpio et al. - 2016 - Single-cell analysis of CD4 T-cell differentiation reveals three major cell states and progressive acceleratio.pdf:pdf},
isbn = {10.1186/s13059-016-0957-5},
issn = {1474-760X},
journal = {Genome Biology},
keywords = {CD4+ T cells,Adaptive immunity,Single-cell RNA-seq,adaptive immunity,cd4,cell cycle,differentiation,live imaging,single-cell rna-seq,t cells},
number = {1},
pages = {103},
pmid = {27176874},
publisher = {Genome Biology},
title = {{Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation}},
url = {http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0957-5},
volume = {17},
year = {2016}
}
@article{Kharchenko2014,
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.},
author = {Kharchenko, Peter V and Silberstein, Lev and Scadden, David T},
doi = {10.1038/nmeth.2967},
file = {:Users/nils/Documents/Mendeley Desktop/Kharchenko, Silberstein, Scadden - 2014 - Bayesian approach to single-cell differential expression analysis.pdf:pdf},
issn = {1548-7105},
journal = {Nature methods},
month = {jul},
number = {7},
pages = {740--2},
pmid = {24836921},
title = {{Bayesian approach to single-cell differential expression analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24836921},
volume = {11},
year = {2014}
}
@article{Eling2017,
abstract = {Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays a crucial role in tissue function and development. Single-cell RNA sequencing can characterise this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinders meaningful comparison of expression variability between cell populations. To address this problem, we introduce a novel analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. Moreover, we introduce a new and robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronisation of the translational machinery in immune cells upon activation. Additionally, our approach identifies new patterns of variability across CD4+ T cell differentiation.},
author = {Eling, Nils and Richard, Arianne C. and Richardson, Sylvia and Marioni, John C. and Vallejos, Catalina A.},
doi = {10.1101/237214},
file = {:Users/nils/Documents/Mendeley Desktop/237214.full.pdf:pdf},
journal = {bioRxiv},
pages = {237214},
title = {{Robust expression variability testing reveals heterogeneous T cell responses}},
url = {https://www.biorxiv.org/content/early/2017/12/21/237214.full.pdf+html},
year = {2017}
}
@article{Goolam2016,
abstract = {The major and essential objective of pre-implantation development is to establish embryonic and extra-embryonic cell fates. To address when and how this fundamental process is initiated in mammals, we characterize transcriptomes of all individual cells throughout mouse pre-implantation development. This identifies targets of master pluripotency regulators Oct4 and Sox2 as being highly heterogeneously expressed between blastomeres of the 4-cell embryo, with Sox21 showing one of the most heterogeneous expression profiles. Live-cell tracking demonstrates that cells with decreased Sox21 yield more extra-embryonic than pluripotent progeny. Consistently, decreasing Sox21 results in premature upregulation of the differentiation regulator Cdx2, suggesting that Sox21 helps safeguard pluripotency. Furthermore, Sox21 is elevated following increased expression of the histone H3R26-methylase CARM1 and is lowered following CARM1 inhibition, indicating the importance of epigenetic regulation. Therefore, our results indicate that heterogeneous gene expression, as early as the 4-cell stage, initiates cell-fate decisions by modulating the balance of pluripotency and differentiation.},
archivePrefix = {arXiv},
arxivId = {cs/9605103},
author = {Goolam, Mubeen and Scialdone, Antonio and Graham, Sarah J L and MacAulay, Iain C. and Jedrusik, Agnieszka and Hupalowska, Anna and Voet, Thierry and Marioni, John C. and Zernicka-Goetz, Magdalena},
doi = {10.1016/j.cell.2016.01.047},
eprint = {9605103},
file = {:Users/nils/Documents/Mendeley Desktop/main.pdf:pdf},
isbn = {0-7803-3213-X},
issn = {10974172},
journal = {Cell},
number = {1},
pages = {61--74},
pmid = {27015307},
primaryClass = {cs},
publisher = {The Authors},
title = {{Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos}},
url = {http://dx.doi.org/10.1016/j.cell.2016.01.047},
volume = {165},
year = {2016}
}
@article{Ibarra-Soria2018a,
abstract = {During gastrulation, cell types from all three germ layers are specified and the basic body plan is established
                1
              . However, molecular analysis of this key developmental stage has been hampered by limited cell numbers and a paucity of markers. Single-cell RNA sequencing circumvents these problems, but has so far been limited to specific organ systems
                2
              . Here, we report single-cell transcriptomic characterization of {\textgreater}20,000 cells immediately following gastrulation at E8.25 of mouse development. We identify 20 major cell types, which frequently contain substructure, including three distinct signatures in early foregut cells. Pseudo-space ordering of somitic progenitor cells identifies dynamic waves of transcription and candidate regulators, which are validated by molecular characterization of spatially resolved regions of the embryo. Within the endothelial population, cells that transition from haemogenic endothelial to erythro-myeloid progenitors specifically express Alox5 and its co-factor Alox5ap, which control leukotriene production. Functional assays using mouse embryonic stem cells demonstrate that leukotrienes promote haematopoietic progenitor cell generation. Thus, this comprehensive single-cell map can be exploited to reveal previously unrecognized pathways that contribute to tissue development.},
author = {Ibarra-Soria, Ximena and Jawaid, Wajid and Pijuan-Sala, Blanca and Ladopoulos, Vasileios and Scialdone, Antonio and J{\"{o}}rg, David J. and Tyser, Richard C.V. and Calero-Nieto, Fernando J. and Mulas, Carla and Nichols, Jennifer and Vallier, Ludovic and Srinivas, Shankar and Simons, Benjamin D. and G{\"{o}}ttgens, Berthold and Marioni, John C.},
doi = {10.1038/s41556-017-0013-z},
file = {:Users/nils/Documents/Mendeley Desktop/ibarrasoria2018.pdf:pdf},
issn = {14764679},
journal = {Nature Cell Biology},
number = {2},
pages = {127--134},
pmid = {29311656},
title = {{Defining murine organogenesis at single-cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation}},
volume = {20},
year = {2018}
}
@article{Kolodziejczyk2015a,
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 B{\"{u}}hler, Marc and Liu, Pentao and Marioni, John C. and Teichmann, Sarah A.},
doi = {10.1016/j.stem.2015.09.011},
file = {:Users/nils/Documents/Mendeley Desktop/Kolodziejczyk et al. - 2015 - Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation.pdf:pdf},
issn = {19345909},
journal = {Cell Stem Cell},
pages = {471--485},
title = {{Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S193459091500418X},
volume = {17},
year = {2015}
}
@article{Stegle2015,
author = {Stegle, Oliver and Teichmann, Sarah a. and Marioni, John C.},
doi = {10.1038/nrg3833},
file = {:Users/nils/Documents/Mendeley Desktop/Stegle, Teichmann, Marioni - 2015 - Computational and analytical challenges in single-cell transcriptomics.pdf:pdf},
issn = {1471-0056},
journal = {Nature Reviews Genetics},
month = {jan},
number = {3},
pages = {133--145},
publisher = {Nature Publishing Group},
title = {{Computational and analytical challenges in single-cell transcriptomics}},
url = {http://www.nature.com/doifinder/10.1038/nrg3833},
volume = {16},
year = {2015}
}
@article{Anders2010,
author = {Anders, Simon and Huber, Wolfgang},
doi = {10.1186/gb-2010-11-10-r106},
file = {:Users/nils/Documents/Mendeley Desktop/Anders, Huber - 2010 - Differential expression analysis for sequence count data.pdf:pdf},
issn = {1465-6906},
journal = {Genome Biology},
number = {10},
pages = {R106},
publisher = {BioMed Central Ltd},
title = {{Differential expression analysis for sequence count data}},
url = {http://genomebiology.com/2010/11/10/R106},
volume = {11},
year = {2010}
}
@article{Eberwine2015,
abstract = {The advent of single cell transcriptome analysis has permitted the discovery of cell-to-cell variation in transcriptome expression of even presumptively identical cells. We hypothesize that this variability reflects a many-to-one relation between transcriptome states and the phenotype of a cell. In this relation, the molecular ratios of the subsets of RNA are determined by the stoichiometric constraints of the cell systems, which underdetermine the transcriptome state. Furthermore, the variability is, in part, induced by the tissue context and is important for system-level function. This theory is analogous to theories of literary deconstruction, where multiple 'signifiers' work in opposition to one another to create meaning. By analogy, transcriptome phenotypes should be defined as subsets of RNAs comprising selected RNA systems where the system-associated RNAs are balanced with each other to produce the associated cellular function. This idea provides a framework for understanding cellular heterogeneity in phenotypic responses to variant conditions, such as disease challenge. Comparisons of transcriptomes between seemingly identical cells show that the transcriptomes have significant biological variation.Cells exhibit 'many-to-one' sets of equally functional transcriptional states that are defined, in part, by pathway constraints that are underspecified in comparison to the number of genes.Cellular contextualization occurs from the influence of the microenvironment and local interactions, suggesting that variation itself is important for system-level function.},
author = {Eberwine, James and Kim, Junhyong},
doi = {10.1016/j.tcb.2015.07.004},
file = {:Users/nils/Documents/Mendeley Desktop/Eberwine, Kim - 2015 - Cellular Deconstruction Finding Meaning in Individual Cell Variation.pdf:pdf},
issn = {18793088},
journal = {Trends in Cell Biology},
keywords = {Biological constraints,Phenotype,Transcriptome},
number = {10},
pages = {569--578},
pmid = {26410403},
publisher = {Elsevier Ltd},
title = {{Cellular Deconstruction: Finding Meaning in Individual Cell Variation}},
url = {http://dx.doi.org/10.1016/j.tcb.2015.07.004},
volume = {25},
year = {2015}
}
@book{Gelman2014,
author = {Gelman, Andrew and Carlin, John and Stern, Hal and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
isbn = {978-1439840955},
pages = {286--288},
publisher = {CRC Press},
title = {{Bayesian Data Analysis}},
year = {2014}
}
@article{CowlesCarlin1996,
author = { Mary   Kathryn   Cowles  and  Bradley P.   Carlin },
title = {Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review},
journal = {Journal of the American Statistical Association},
volume = {91},
number = {434},
pages = {883-904},
year  = {1996},
publisher = {Taylor & Francis},
doi = {10.1080/01621459.1996.10476956},
URL = { https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1996.10476956},
eprint = { https://amstat.tandfonline.com/doi/pdf/10.1080/01621459.1996.10476956}
}
@article{BrooksGelman1998,
author = { Stephen P.   Brooks  and  Andrew   Gelman },
title = {General Methods for Monitoring Convergence of Iterative Simulations},
journal = {Journal of Computational and Graphical Statistics},
volume = {7},
number = {4},
pages = {434-455},
year  = {1998},
publisher = {Taylor & Francis},
doi = {10.1080/10618600.1998.10474787},
URL = { https://amstat.tandfonline.com/doi/abs/10.1080/10618600.1998.10474787},
eprint = { https://amstat.tandfonline.com/doi/pdf/10.1080/10618600.1998.10474787}
}

@article{CODA2006,
  volume = {6},
  number = {1},
   month = {March},
  author = {Martyn Plummer and Nicky Best and Kate Cowles and Karen Vines},
   title = {CODA: convergence diagnosis and output analysis for MCMC},
 journal = {R News},
   pages = {7--11},
    year = {2006},
     url = {http://oro.open.ac.uk/22547/},
}
@article{Prakadan2017,
abstract = {Recent advances in cellular profiling have demonstrated substantial heterogeneity in the behaviour of cells once deemed 'identical', challenging fundamental notions of cell 'type' and 'state'. Not surprisingly, these findings have elicited substantial interest in deeply characterizing the diversity, interrelationships and plasticity among cellular phenotypes. To explore these questions, experimental platforms are needed that can extensively and controllably profile many individual cells. Here, microfluidic structures - whether valve-, droplet- or nanowell-based - have an important role because they can facilitate easy capture and processing of single cells and their components, reducing labour and costs relative to conventional plate-based methods while also improving consistency. In this article, we review the current state-of-the-art methodologies with respect to microfluidics for mammalian single-cell 'omics' and discuss challenges and future opportunities.},
archivePrefix = {arXiv},
arxivId = {10.1038/nrg.2017.15},
author = {Prakadan, Sanjay M. and Shalek, Alex K. and Weitz, David A.},
doi = {10.1038/nrg.2017.15},
eprint = {nrg.2017.15},
file = {:Users/nils/Documents/Mendeley Desktop/Prakadan, Shalek, Weitz - 2017 - Scaling by shrinking empowering single-cell 'omics' with microfluidic devices.pdf:pdf},
isbn = {1471-0056},
issn = {1471-0056},
journal = {Nature Reviews Genetics},
number = {6},
pages = {345--361},
pmid = {28392571},
primaryClass = {10.1038},
title = {{Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices}},
url = {http://www.nature.com/doifinder/10.1038/nrg.2017.15},
volume = {18},
year = {2017}
}
@article{Patange2018,
abstract = {Gene expression varies across cells in a population or a tissue. This heterogeneity has come into sharp focus in recent years through developments in new imaging and sequencing technologies. However, our ability to measure variation has outpaced our ability to interpret it. Much of the variability may arise from random effects occurring in the processes of gene expression (transcription, RNA processing and decay, translation). The molecular basis of these effects is largely unknown. Likewise, a functional role of this variability in growth, differentiation and disease has only been elucidated in a few cases. In this review, we highlight recent experimental and theoretical advances for measuring and analyzing stochastic variation.},
author = {Patange, Simona and Girvan, Michelle and Larson, Daniel R.},
doi = {10.1016/j.coisb.2017.11.011},
file = {:Users/nils/Documents/Mendeley Desktop/Patange, Girvan, Larson - 2018 - Single-cell systems biology Probing the basic unit of information flow.pdf:pdf},
issn = {24523100},
journal = {Current Opinion in Systems Biology},
keywords = {Live cell imaging,Single-cell gene expression,Single-molecule RNA FISH,scRNA-seq},
pages = {7--15},
publisher = {Elsevier Ltd},
title = {{Single-cell systems biology: Probing the basic unit of information flow}},
url = {https://doi.org/10.1016/j.coisb.2017.11.011},
volume = {8},
year = {2018}
}
@article{Villani2017,
abstract = {Dendritic cells (DCs) and monocytes play a central role in pathogen sensing, phagocytosis, and antigen presentation and consist of multiple specialized subtypes. However, their identities and interrelationships are not fully understood. Using unbiased single-cell RNA sequencing (RNA-seq) of {\~{}}2400 cells, we identified six human DCs and four monocyte subtypes in human blood. Our study reveals a new DC subset that shares properties with plasmacytoid DCs (pDCs) but potently activates T cells, thus redefining pDCs; a new subdivision within the CD1C(+) subset of DCs; the relationship between blastic plasmacytoid DC neoplasia cells and healthy DCs; and circulating progenitor of conventional DCs (cDCs). Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease.},
archivePrefix = {arXiv},
arxivId = {15334406},
author = {Villani, Alexandra Chlo{\'{e}} and Satija, Rahul and Reynolds, Gary and Sarkizova, Siranush and Shekhar, Karthik and Fletcher, James and Griesbeck, Morgane and Butler, Andrew and Zheng, Shiwei and Lazo, Suzan and Jardine, Laura and Dixon, David and Stephenson, Emily and Nilsson, Emil and Grundberg, Ida and McDonald, David and Filby, Andrew and Li, Weibo and {De Jager}, Philip L. and Rozenblatt-Rosen, Orit and Lane, Andrew A. and Haniffa, Muzlifah and Regev, Aviv and Hacohen, Nir},
doi = {10.1126/science.aah4573},
eprint = {15334406},
file = {:Users/nils/Documents/Mendeley Desktop/Villani et al. - 2017 - Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors.pdf:pdf},
isbn = {1095-9203 (Electronic) 0036-8075 (Linking)},
issn = {10959203},
journal = {Science},
number = {6335},
pmid = {28428369},
title = {{Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors}},
volume = {356},
year = {2017}
}
@article{Zheng2017,
abstract = {Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of up to tens of thousands of single cells per sample. Cell encapsulation in droplets takes place in {\~{}}6 minutes, with {\~{}}50{\%} cell capture efficiency, up to 8 samples at a time. The speed and efficiency allow the processing of precious samples while minimizing stress to cells. To demonstrate the system′s technical performance and its applications, we collected transcriptome data from {\~{}}¼ million single cells across 29 samples. First, we validate the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. Then, we profile 68k peripheral blood mononuclear cells (PBMCs) to demonstrate the system′s ability to characterize large immune populations. Finally, we use sequence variation in the transcriptome data to determine host and donor chimerism at single cell resolution in bone marrow mononuclear cells (BMMCs) of transplant patients. This analysis enables characterization of the complex interplay between donor and host cells and monitoring of treatment response. This high-throughput system is robust and enables characterization of diverse biological systems with single cell mRNA analysis.},
archivePrefix = {arXiv},
arxivId = {065912},
author = {Zheng, Grace X.Y. and Terry, Jessica M. and Belgrader, Phillip and Ryvkin, Paul and Bent, Zachary W. and Wilson, Ryan and Ziraldo, Solongo B. and Wheeler, Tobias D. and McDermott, Geoff P. and Zhu, Junjie and Gregory, Mark T. and Shuga, Joe and Montesclaros, Luz and Underwood, Jason G. and Masquelier, Donald A. and Nishimura, Stefanie Y. and Schnall-Levin, Michael and Wyatt, Paul W. and Hindson, Christopher M. and Bharadwaj, Rajiv and Wong, Alexander and Ness, Kevin D. and Beppu, Lan W. and Deeg, H. Joachim and McFarland, Christopher and Loeb, Keith R. and Valente, William J. and Ericson, Nolan G. and Stevens, Emily A. and Radich, Jerald P. and Mikkelsen, Tarjei S. and Hindson, Benjamin J. and Bielas, Jason H.},
doi = {10.1038/ncomms14049},
eprint = {065912},
file = {:Users/nils/Documents/Mendeley Desktop/ncomms14049.pdf:pdf},
isbn = {9780979806483},
issn = {20411723},
journal = {Nature Communications},
pages = {1--12},
pmid = {28091601},
publisher = {Nature Publishing Group},
title = {{Massively parallel digital transcriptional profiling of single cells}},
url = {http://dx.doi.org/10.1038/ncomms14049},
volume = {8},
year = {2017}
}
@article{Ibarra-Soria2018,
abstract = {During gastrulation, cell types from all three germ layers are specified and the basic body plan is established 1 . However, molecular analysis of this key developmental stage has been hampered by limited cell numbers and a paucity of markers. Single-cell RNA sequencing circumvents these problems, but has so far been limited to specific organ systems 2 . Here, we report single-cell transcriptomic characterization of {\textgreater}20,000 cells immediately following gastrulation at E8.25 of mouse development. We identify 20 major cell types, which frequently contain substructure, including three distinct signatures in early foregut cells. Pseudo-space ordering of somitic progenitor cells identifies dynamic waves of transcription and candidate regulators, which are validated by molecular characterization of spatially resolved regions of the embryo. Within the endothelial population, cells that transition from haemogenic endothelial to erythro-myeloid progenitors specifically express Alox5 and its co-factor Alox5ap, which control leukotriene production. Functional assays using mouse embryonic stem cells demonstrate that leukotrienes promote haematopoietic progenitor cell generation. Thus, this comprehensive single-cell map can be exploited to reveal previously unrecognized pathways that contribute to tissue development.},
author = {Ibarra-Soria, Ximena and Jawaid, Wajid and Pijuan-Sala, Blanca and Ladopoulos, Vasileios and Scialdone, Antonio and J{\"{o}}rg, David J. and Tyser, Richard C.V. and Calero-Nieto, Fernando J. and Mulas, Carla and Nichols, Jennifer and Vallier, Ludovic and Srinivas, Shankar and Simons, Benjamin D. and G{\"{o}}ttgens, Berthold and Marioni, John C.},
doi = {10.1038/s41556-017-0013-z},
file = {:Users/nils/Documents/Mendeley Desktop/ibarrasoria2018.pdf:pdf},
issn = {14764679},
journal = {Nature Cell Biology},
number = {2},
pages = {127--134},
pmid = {29311656},
title = {{Defining murine organogenesis at single-cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation}},
volume = {20},
year = {2018}
}
@article{Wagner2018,
abstract = {As embryos develop, numerous cell types with distinct functions and morphologies arise from pluripotent cells. Three research groups have used single-cell RNA sequencing to analyze the transcriptional changes accompanying development of vertebrate embryos (see the Perspective by Harland). Wagner et al. sequenced the transcriptomes of more than 90,000 cells throughout zebrafish development to reveal how cells differentiate during axis patterning, germ layer formation, and early organogenesis. Farrell et al. profiled the transcriptomes of tens of thousands of embryonic cells and applied a computational approach to construct a branching tree describing the transcriptional trajectories that lead to 25 distinct zebrafish cell types. The branching tree revealed how cells change their gene expression as they become more and more specialized. Briggs et al. examined whole frog embryos, spanning zygotic genome activation through early organogenesis, to map cell states and differentiation across all cell lineages over time. These data and approaches pave the way for the comprehensive reconstruction of transcriptional trajectories during development. Science , this issue p. [981][1], p. [eaar3131][2], p. [eaar5780][3]; see also p. [967][4] [1]: /lookup/doi/10.1126/science.aar4362 [2]: /lookup/doi/10.1126/science.aar3131 [3]: /lookup/doi/10.1126/science.aar5780 [4]: /lookup/doi/10.1126/science.aat8413},
archivePrefix = {arXiv},
arxivId = {1101.5435},
author = {Wagner, Daniel E. and Weinreb, Caleb and Collins, Zach M. and Briggs, James A. and Megason, Sean G. and Klein, Allon M.},
doi = {10.1126/science.aar4362},
eprint = {1101.5435},
file = {:Users/nils/Documents/Mendeley Desktop/science.aar4362.full.pdf:pdf},
isbn = {9781605589077},
issn = {10959203},
journal = {Science},
pages = {1--12},
pmid = {29700229},
title = {{Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo}},
volume = {4362},
year = {2018}
}
@article{Pijuan-Sala2019,
abstract = {Across the animal kingdom, gastrulation represents a key developmental event during which embryonic pluripotent cells diversify into lineage-specific precursors that will generate the adult organism. Here we report the transcriptional profiles of 116,312 single cells from mouse embryos collected at nine sequential time points ranging from 6.5 to 8.5 days post-fertilization. We construct a molecular map of cellular differentiation from pluripotency towards all major embryonic lineages, and explore the complex events involved in the convergence of visceral and primitive streak-derived endoderm. Furthermore, we use single-cell profiling to show that Tal1−/− chimeric embryos display defects in early mesoderm diversification, and we thus demonstrate how combining temporal and transcriptional information can illuminate gene function. Together, this comprehensive delineation of mammalian cell differentiation trajectories in vivo represents a baseline for understanding the effects of gene mutations during development, as well as a roadmap for the optimization of in vitro differentiation protocols for regenerative medicine.},
author = {Pijuan-Sala, Blanca and Griffiths, Jonathan A. and Guibentif, Carolina and Hiscock, Tom W. and Jawaid, Wajid and Calero-Nieto, Fernando J. and Mulas, Carla and Ibarra-Soria, Ximena and Tyser, Richard C.V. and Ho, Debbie Lee Lian and Reik, Wolf and Srinivas, Shankar and Simons, Benjamin D. and Nichols, Jennifer and Marioni, John C. and G{\"{o}}ttgens, Berthold},
doi = {10.1038/s41586-019-0933-9},
file = {:Users/nils/Documents/Mendeley Desktop/10.1038@s41586-019-0933-9.pdf:pdf},
issn = {14764687},
journal = {Nature},
number = {7745},
pages = {490--495},
title = {{A single-cell molecular map of mouse gastrulation and early organogenesis}},
volume = {566},
year = {2019}
}
@article{Ohnishi2014,
abstract = {It is now recognized that extensive expression heterogeneities among cells precede the emergence of lineages in the early mammalian embryo. To establish a map of pluripotent epiblast (EPI) versus primitive endoderm (PrE) lineage segregation within the inner cell mass (ICM) of the mouse blastocyst, we characterized the gene expression profiles of individual ICM cells. Clustering analysis of the transcriptomes of 66 cells demonstrated that initially they are non-distinguishable. Early in the segregation, lineage-specific marker expression exhibited no apparent correlation, and a hierarchical relationship was established only in the late blastocyst. Fgf4 exhibited a bimodal expression at the earliest stage analysed, and in its absence, the differentiation of PrE and EPI was halted, indicating that Fgf4 drives, and is required for, ICM lineage segregation. These data lead us to propose a model where stochastic cell-to-cell expression heterogeneity followed by signal reinforcement underlies ICM lineage segregation by antagonistically separating equivalent cells.},
author = {Ohnishi, Yusuke and Huber, Wolfgang and Tsumura, Akiko and Kang, Minjung and Xenopoulos, Panagiotis and Kurimoto, Kazuki and Ole{\'{s}}, Andrzej K and Ara{\'{u}}zo-Bravo, Marcos J and Saitou, Mitinori and Hadjantonakis, Anna-Katerina and Hiiragi, Takashi},
doi = {10.1038/ncb2881},
file = {:Users/nils/Documents/Mendeley Desktop/Ohnishi et al. - 2014 - Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineag.pdf:pdf},
issn = {1476-4679},
journal = {Nature Cell Biology},
keywords = {Animals,Biological,Biological Markers,Biological Markers: metabolism,Blastocyst Inner Cell Mass,Blastocyst Inner Cell Mass: cytology,Blastocyst Inner Cell Mass: metabolism,Cell Lineage,Cell Lineage: drug effects,Cell Separation,Developmental,Endoderm,Endoderm: cytology,Endoderm: metabolism,Fibroblast Growth Factor 4,Fibroblast Growth Factor 4: metabolism,Gene Expression Profiling,Gene Expression Regulation,Germ Layers,Germ Layers: cytology,Germ Layers: metabolism,Mice,Models,Oligonucleotide Array Sequence Analysis,Polymerase Chain Reaction,Principal Component Analysis,Signal Transduction,Signal Transduction: genetics,Single-Cell Analysis},
number = {1},
pages = {27--37},
pmid = {24292013},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
shorttitle = {Nat Cell Biol},
title = {{Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages.}},
url = {http://dx.doi.org/10.1038/ncb2881},
volume = {16},
year = {2014}
}
@article{Kiselev2019,
abstract = {Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.},
author = {Kiselev, Vladimir Yu and Andrews, Tallulah S. and Hemberg, Martin},
doi = {10.1038/s41576-018-0088-9},
file = {:Users/nils/Documents/Mendeley Desktop/10.1038@s41576-018-0088-9.pdf:pdf},
issn = {1471-0064},
journal = {Nature Reviews Genetics 2018},
keywords = {Functional clustering,Gene expression analysis,Genome,RNA sequencing,Statistical methods,Transcriptomics,wide analysis of gene expression},
pmid = {30617341},
publisher = {Springer US},
title = {{Challenges in unsupervised clustering of single-cell RNA-seq data}},
url = {https://www.nature.com/articles/s41576-018-0088-9?utm{\_}source=feedburner{\&}utm{\_}medium=feed{\&}utm{\_}campaign=Feed{\%}3A+nrg{\%}2Frss{\%}2Fcurrent+{\%}28Nature+Reviews+Genetics+-+Issue{\%}29},
year = {2019}
}
@article{Elowitz2002,
abstract = {Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.},
author = {Elowitz, Michael B and Levine, Arnold J and Siggia, Eric D and Swain, Peter S},
doi = {10.1126/science.1070919},
file = {:Users/nils/Documents/Mendeley Desktop/science02.pdf:pdf},
isbn = {1095-9203 (Electronic)$\backslash$r0036-8075 (Linking)},
issn = {1095-9203},
journal = {Science},
keywords = {*Gene Expression Regulation,*Genes,Bacterial,Bacterial Proteins/biosynthesis/*genetics,Computer-Assisted,Culture Media,Escherichia coli/*genetics,Experimental noise,Fluorescence,Gene Dosage,Genes,Genetic,Image Processing,Isopropyl Thiogalactoside/metabolism/pharmacology,Luminescent Proteins/biosynthesis/*genetics,Noise in prokaryotes,Non-U.S. Gov't,P.H.S.,Plasmids,Promoter Regions (Genetics),Reporter,Repressor Proteins,Stochastic Processes,Support,Transcription,U.S. Gov't},
number = {5584},
pages = {1183--1186},
pmid = {12183631},
title = {{Stochastic gene expression in a single cell}},
url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve{\&}db=PubMed{\&}dopt=Citation{\&}list{\_}uids=12183631},
volume = {297},
year = {2002}
}
@article{Zopf2013,
abstract = {The large variability in mRNA and protein levels found from both static and dynamic measurements in single cells has been largely attributed to random periods of transcription, often occurring in bursts. The cell cycle has a pronounced global role in affecting transcriptional and translational output, but how this influences transcriptional statistics from noisy promoters is unknown and generally ignored by current stochastic models. Here we show that variable transcription from the synthetic tetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. Real-time measurements of fluorescent protein at high expression levels indicate tetO promoters increase transcription rate ∼2-fold in S/G2/M similar to constitutive genes. At low expression levels, where tetO promoters are thought to generate infrequent bursts of transcription, we observe random pulses of expression restricted to S/G2/M, which are correlated between homologous promoters present in the same cell. The analysis of static, single-cell mRNA measurements at different points along the cell cycle corroborates these findings. Our results demonstrate that highly variable mRNA distributions in yeast are not solely the result of randomly switching between periods of active and inactive gene expression, but instead largely driven by differences in transcriptional activity between G1 and S/G2/M.},
author = {Zopf, Cristopher J. and Quinn, Katie and Zeidman, Joshua and Maheshri, Narendra},
doi = {10.1371/journal.pcbi.1003161},
file = {:Users/nils/Documents/Mendeley Desktop/journal.pcbi.1003161.PDF:PDF},
isbn = {1553-7358 (Electronic)$\backslash$n1553-734X (Linking)},
issn = {1553734X},
journal = {PLoS Computational Biology},
number = {7},
pages = {1--12},
pmid = {23935476},
title = {{Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression}},
volume = {9},
year = {2013}
}
@article{Iwamoto2016,
abstract = {Cellular heterogeneity, which plays an essential role in biological phenomena, such as drug resistance and migration, is considered to arise from intrinsic (i.e., reaction kinetics) and extrinsic (i.e., protein variability) noise in the cell. However, the mechanistic effects of these types of noise to determine the heterogeneity of signal responses have not been elucidated. Here, we report that the output of epidermal growth factor (EGF) signaling activity is modulated by cellular noise, particularly by extrinsic noise of particular signaling components in the pathway. We developed a mathematical model of the EGF signaling pathway incorporating regulation between extracellular signal-regulated kinase (ERK) and nuclear pore complex (NPC), which is necessary for switch-like activation of the nuclear ERK response. As the threshold of switch-like behavior is more sensitive to perturbations than the graded response, the effect of biological noise is potentially critical for cell fate decision. Our simulation analysis indicated that extrinsic noise, but not intrinsic noise, contributes to cell-to-cell heterogeneity of nuclear ERK. In addition, we accurately estimated variations in abundance of the signal proteins between individual cells by direct comparison of experimental data with simulation results using Apparent Measurement Error (AME). AME was constant regardless of whether the protein levels varied in a correlated manner, while covariation among proteins influenced cell-to-cell heterogeneity of nuclear ERK, suppressing the variation. Simulations using the estimated protein abundances showed that each protein species has different effects on cell-to-cell variation in the nuclear ERK response. In particular, variability of EGF receptor, Ras, Raf, and MEK strongly influenced cellular heterogeneity, while others did not. Overall, our results indicated that cellular heterogeneity in response to EGF is strongly driven by extrinsic noise, and that such heterogeneity results from variability of particular protein species that function as sensitive nodes, which may contribute to the pathogenesis of human diseases.},
author = {Iwamoto, Kazunari and Shindo, Yuki and Takahashi, Koichi},
doi = {10.1371/journal.pcbi.1005222},
file = {:Users/nils/Documents/Mendeley Desktop/Iwamoto, Shindo, Takahashi - 2016 - Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signa.pdf:pdf},
issn = {15537358},
journal = {PLoS Computational Biology},
number = {11},
pages = {1--18},
pmid = {27902699},
title = {{Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway}},
volume = {12},
year = {2016}
}
@article{Kiviet2014,
abstract = {Elucidating the role of molecular stochasticity in cellular growth is central to understanding phenotypic heterogeneity and the stability of cellular proliferation. The inherent stochasticity of metabolic reaction events should have negligible effect, because of averaging over the many reaction events contributing to growth. Indeed, metabolism and growth are often considered to be constant for fixed conditions. Stochastic fluctuations in the expression level of metabolic enzymes could produce variations in the reactions they catalyse. However, whether such molecular fluctuations can affect growth is unclear, given the various stabilizing regulatory mechanisms, the slow adjustment of key cellular components such as ribosomes, and the secretion and buffering of excess metabolites. Here we use time-lapse microscopy to measure fluctuations in the instantaneous growth rate of single cells of Escherichia coli, and quantify time-resolved cross-correlations with the expression of lac genes and enzymes in central metabolism. We show that expression fluctuations of catabolically active enzymes can propagate and cause growth fluctuations, with transmission depending on the limitation of the enzyme to growth. Conversely, growth fluctuations propagate back to perturb expression. Accordingly, enzymes were found to transmit noise to other unrelated genes via growth. Homeostasis is promoted by a noise-cancelling mechanism that exploits fluctuations in the dilution of proteins by cell-volume expansion. The results indicate that molecular noise is propagated not only by regulatory proteins but also by metabolic reactions. They also suggest that cellular metabolism is inherently stochastic, and a generic source of phenotypic heterogeneity.},
author = {Kiviet, Daniel J. and Nghe, Philippe and Walker, Noreen and Boulineau, Sarah and Sunderlikova, Vanda and Tans, Sander J.},
doi = {10.1038/nature13582},
file = {:Users/nils/Documents/Mendeley Desktop/Kiviet et al. - 2014 - Stochasticity of metabolism and growth at the single-cell level.pdf:pdf},
isbn = {1476-4687 (Electronic)$\backslash$r0028-0836 (Linking)},
issn = {0028-0836},
journal = {Nature},
number = {7522},
pages = {376--379},
pmid = {25186725},
publisher = {Nature Publishing Group},
title = {{Stochasticity of metabolism and growth at the single-cell level}},
url = {http://www.nature.com/doifinder/10.1038/nature13582},
volume = {514},
year = {2014}
}
@article{Faure2017,
abstract = {Isogenic cells in a common environment show substantial cell-to-cell variation in gene expression, often referred to as “expression noise.” Here, we use multiple single-cell RNA-sequencing datasets to identify features associated with high or low expression noise in mouse embryonic stem cells. These include the core promoter architecture of a gene, with CpG island promoters and a TATA box associated with low and high noise, respectively. High noise is also associated with “conflicting” chromatin states—the absence of transcription-associated histone modifications or the presence of repressive ones in active genes. Genes regulated by pluripotency factors through super-enhancers show high and correlated expression variability, consistent with fluctuations in the pluripotent state. Together, our results provide an integrated view of how core promoters, chromatin, regulation, and pluripotency fluctuations contribute to the variability of gene expression across individual stem cells. An integrative computational analysis identifies regulatory features that are statistically associated with high and low gene expression noise in mouse embryonic stem cells.},
author = {Faure, Andre J. and Schmiedel, J{\"{o}}rn M. and Lehner, Ben},
doi = {10.1016/j.cels.2017.10.003},
file = {:Users/nils/Documents/Mendeley Desktop/Faure, Schmiedel, Lehner - 2017 - Systematic Analysis of the Determinants of Gene Expression Noise in Embryonic Stem Cells.pdf:pdf},
issn = {24054720},
journal = {Cell Systems},
keywords = {chromatin,dosage compensation,embryonic stem cells,noise,promoter architectures,transcription},
number = {5},
pages = {471--484},
pmid = {29102610},
title = {{Systematic Analysis of the Determinants of Gene Expression Noise in Embryonic Stem Cells}},
volume = {5},
year = {2017}
}
@article{Morgan2018,
abstract = {Population phenotypic variation can arise from genetic differences between individuals, or from cellular heterogeneity in an isogenic group of cells or organisms. The emergence of gene expression differences between genetically identical cells is referred to as gene expression noise, the sources of which are not well understood. In this work, by studying gene expression noise between multiple cell lineages and mammalian species, we find consistent evidence of a role for CpG islands as sources of gene expression noise. Variation in noise among CpG island promoters can be partially attributed to differences in island size, in which short islands have noisier gene expression. Building on these findings, we investigate the potential for short CpG islands to act as fast response elements to environmental stimuli. Specifically, we find that these islands are enriched amongst primary response genes in SWI/SNF-independent stimuli, suggesting that expression noise is an indicator of promoter responsiveness. Thus, through the integration of single-cell RNA expression profiling, chromatin landscape and temporal gene expression dynamics, we have uncovered a role for short CpG island promoters as fast response elements.},
author = {Morgan, Michael D. and Marioni, John C.},
doi = {10.1186/s13059-018-1461-x},
file = {:Users/nils/Documents/Mendeley Desktop/Morgan, Marioni - 2018 - CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness.pdf:pdf},
issn = {1474760X},
journal = {Genome Biology},
keywords = {Gene expression noise,Promoter response,Single cell},
number = {1},
pages = {1--13},
pmid = {29945659},
publisher = {Genome Biology},
title = {{CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness}},
volume = {19},
year = {2018}
}
@article{Ecker2017a,
author = {Ecker, Simone and Pancaldi, Vera and Valencia, Alfonso and Beck, Stephan and Paul, Dirk S.},
doi = {10.1002/bies.201700148},
file = {:Users/nils/Documents/Mendeley Desktop/Ecker et al. - 2017 - Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity.pdf:pdf},
issn = {02659247},
journal = {BioEssays},
pages = {1700148},
pmid = {29251357},
title = {{Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity}},
url = {http://doi.wiley.com/10.1002/bies.201700148},
year = {2017}
}
@article{Vallejos2016,
author = {Vallejos, Catalina A and Richardson, Sylvia and Marioni, John C},
doi = {10.1101/035949},
file = {:Users/nils/Documents/Mendeley Desktop/art{\%}3A10.1186{\%}2Fs13059-016-0930-3.pdf:pdf},
journal = {Genome Biology},
keywords = {cellular heterogeneity,differential expression,single-cell rna-seq},
number = {70},
title = {{Beyond comparisons of means: understanding changes in gene expression at the single-cell level}},
url = {http://biorxiv.org/content/early/2016/01/05/035949.abstract},
volume = {17},
year = {2016}
}
@article{Eling2018,
author = {Eling, Nils and Richard, Arianne C. and Richardson, Sylvia and Marioni, John C. and Vallejos, Catalina A.},
doi = {10.1016/j.cels.2018.06.011},
file = {:Users/nils/Documents/Mendeley Desktop/PIIS2405471218302783 (1).pdf:pdf},
issn = {24054712},
journal = {Cell Systems},
number = {3},
pages = {1--11},
pmid = {30172840},
publisher = {Elsevier Inc.},
title = {{Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2405471218302783},
volume = {7},
year = {2018}
}
@article{Martinez-jimenez2017,
author = {Martinez-Jimenez, Celia P. and Eling, Nils and Chen, Hung-Chang and Vallejos, Catalina A and Kolodziejczyk, Aleksandra A and Connor, Frances and Stojic, Lovorka and Rayner, Timothy F and Stubbington, Michael J T and Teichmann, Sarah A and de la Roche, Maike and Marioni, John C and Odom, Duncan T},
doi = {10.1126/science.aah4115},
file = {:Users/nils/Documents/Mendeley Desktop/1433.full.pdf:pdf},
journal = {Science},
pages = {1433--1436},
title = {{Aging increases cell-to-cell transcriptional variability upon immune stimulation}},
volume = {1436},
year = {2017}
}
@incollection{Kim2019,
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. In this chapter, we review the challenges in splicing isoform quantification in scRNA-seq data and discuss BRIE (Bayesian regression for isoform estimation), a recently proposed Bayesian hierarchical model which resolves these problems by learning an informative prior distribution from sequence features. We illustrate the usage of BRIE with a case study on 130 mouse cells during gastrulation.},
author = {Kim, Beomseok and Lee, Eunmin and Kim, Jong Kyoung},
booktitle = {Computational Methods for Single-Cell Data Analysis},
doi = {10.1007/978-1-4939-9057-3},
file = {:Users/nils/Documents/Mendeley Desktop/2019{\_}Book{\_}ComputationalMethodsForSingle-.pdf:pdf},
isbn = {978-1-4939-9056-6},
issn = {1940-6029},
keywords = {Alternative splicing,Bayesian model,Isoform quantification,Single-cell RNA-seq},
pages = {25--43},
pmid = {30758827},
title = {{Analysis of Technical and Biological Variabilityin Single-Cell RNA Sequencing}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/30758827{\%}0Ahttp://link.springer.com/10.1007/978-1-4939-9057-3{\_}12{\%}0Ahttp://link.springer.com/10.1007/978-1-4939-9057-3},
volume = {1935},
year = {2019}
}
@article{Lun2016pooling,
abstract = {Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses.},
author = {Lun, Aaron T. L. and Bach, Karsten and Marioni, John C.},
doi = {10.1186/s13059-016-0947-7},
file = {:Users/nils/Documents/Mendeley Desktop/L. Lun, Bach, Marioni - 2016 - Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.pdf:pdf},
issn = {1474-760X},
journal = {Genome Biology},
keywords = {differential expression,normalization,single-cell rna-seq},
number = {1},
pages = {75},
pmid = {27122128},
publisher = {Genome Biology},
title = {{Pooling across cells to normalize single-cell RNA sequencing data with many zero counts}},
url = {http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0947-7},
volume = {17},
year = {2016}
}
@article{Kolodziejczyk2015cell,
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 B{\"{u}}hler, Marc and Liu, Pentao and Marioni, John C. and Teichmann, Sarah A.},
doi = {10.1016/j.stem.2015.09.011},
file = {:Users/nils/Documents/Mendeley Desktop/Kolodziejczyk et al. - 2015 - Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation.pdf:pdf},
issn = {19345909},
journal = {Cell Stem Cell},
pages = {471--485},
title = {{Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S193459091500418X},
volume = {17},
year = {2015}
}
@article{Young2010,
abstract = {We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.},
author = {Young, Matthew D and Wakefield, Matthew J and Smyth, Gordon K and Oshlack, Alicia},
file = {:Users/nils/Documents/Mendeley Desktop/document (3).pdf:pdf},
journal = {Genome Biology},
title = {{Gene ontology analysis for RNA-seq: accounting for selection bias}},
url = {http://genomebiology.com/2010/11/2/R14},
volume = {11},
year = {2010}
}
@article{Durinck2005,
abstract = {biomaRt is a new Bioconductor package that integrates BioMart data resources with data analysis software in Bioconductor. It can annotate a wide range of gene or gene product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Furthermore biomaRt enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis. Fast and up-to-date data retrieval is possible as the package executes direct SQL queries to the BioMart databases (e.g. Ensembl). The biomaRt package provides a tight integration of large, public or locally installed BioMart databases with data analysis in Bioconductor creating a powerful environment for biological data mining.},
author = {Durinck, Steffen and Moreau, Yves and Kasprzyk, Arek and Davis, Sean and {De Moor}, Bart and Brazma, Alvis and Huber, Wolfgang},
doi = {10.1093/bioinformatics/bti525},
file = {:Users/nils/Documents/Mendeley Desktop/bti525.pdf:pdf},
issn = {13674803},
journal = {Bioinformatics},
number = {16},
pages = {3439--3440},
title = {{BioMart and Bioconductor: A powerful link between biological databases and microarray data analysis}},
volume = {21},
year = {2005}
}
@article{Ilicic2016,
author = {Ilicic, Tomislav and Kim, Jong Kyoung and Kolodziejczyk, Aleksandra A and Bagger, Frederik Otzen and Mccarthy, Davis James and Marioni, John C and Teichmann, Sarah A},
doi = {10.1186/s13059-016-0888-1},
file = {:Users/nils/Documents/Mendeley Desktop/s13059-016-0888-1.pdf:pdf},
isbn = {1305901608881},
issn = {1474-760X},
journal = {Genome Biology},
number = {29},
pages = {1--15},
publisher = {Genome Biology},
title = {{Classification of low quality cells from single-cell RNA-seq data}},
url = {http://dx.doi.org/10.1186/s13059-016-0888-1},
volume = {17},
year = {2016}
}
@article{Vallejos2017,
abstract = {Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.},
author = {Vallejos, Catalina A and Risso, Davide and Scialdone, Antonio and Dudoit, Sandrine and Marioni, John C},
doi = {10.1038/nmeth.4292},
file = {:Users/nils/Documents/Mendeley Desktop/vallejos2017.pdf:pdf},
issn = {1548-7091},
journal = {Nature Methods},
number = {6},
pages = {565--571},
pmid = {28504683},
publisher = {Nature Publishing Group},
title = {{Normalizing single-cell RNA sequencing data: challenges and opportunities}},
url = {http://www.nature.com/doifinder/10.1038/nmeth.4292},
volume = {14},
year = {2017}
}
@article{Shalek2014,
author = {Shalek, Alex K and Satija, Rahul and Shuga, Joe and Trombetta, John J and Gennert, Dave and Lu, Diana and Chen, Peilin and Gertner, Rona S and Gaublomme, Jellert T and Yosef, Nir and Schwartz, Schraga and Fowler, Brian and Weaver, Suzanne and Wang, Jing and Wang, Xiaohui and Ding, Ruihua and Raychowdhury, Raktima and Friedman, Nir and Hacohen, Nir and Park, Hongkun and May, Andrew P and Regev, Aviv},
doi = {10.1038/nature13437},
file = {:Users/nils/Documents/Mendeley Desktop/Shalek{\_}2014{\_}Nature13437.pdf:pdf},
issn = {0028-0836},
journal = {Nature},
number = {7505},
pages = {263--269},
publisher = {Nature Publishing Group},
title = {{Single-cell RNA-seq reveals dynamic paracrine control of cellular variation}},
url = {http://dx.doi.org/10.1038/nature13437},
volume = {510},
year = {2014}
}
@article{Antolovic2017,
author = {Antolovi{\'{c}}, Vlatka and Miermont, Agnes and Corrigan, Adam M. and Chubb, Jonathan R.},
doi = {10.1016/j.cub.2017.05.028},
file = {:Users/nils/Documents/Mendeley Desktop/PIIS096098221730564X.pdf:pdf},
issn = {09609822},
journal = {Current Biology},
pages = {1811--1817},
title = {{Generation of Single-Cell Transcript Variability by Repression}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S096098221730564X},
volume = {27},
year = {2017}
}
@article{Borroto2000,
author = {Jose, Ester San and Borroto, Aldo and Niedergang, Florence and Alcover, Andres and Alarcon, Balbino},
file = {:Users/nils/Documents/Mendeley Desktop/1-s2.0-S1074761300801697-main.pdf:pdf},
journal = {Immunity},
pages = {161--170},
title = {{Triggering the TCR complexes causes the downregulation of nonengaged receptors by signal transduction-dependent mechanism}},
volume = {12},
year = {2000}
}
@article{Araki2017,
abstract = {Translation is a critical process in protein synthesis, but translational regulation in antigen-specific T cells in vivo has not been well defined. Here we have characterized the translatome of virus-specific CD8(+) effector T cells (Teff cells) during acute infection of mice with lymphocytic choriomeningitis virus (LCMV). Antigen-specific T cells exerted dynamic translational control of gene expression that correlated with cell proliferation and stimulation via the T cell antigen receptor (TCR). The translation of mRNAs that encode translation machinery, including ribosomal proteins, was upregulated during the T cell clonal-expansion phase, followed by inhibition of the translation of those transcripts when the CD8(+) Teff cells stopped dividing just before the contraction phase. That translational suppression was more pronounced in terminal effector cells than in memory precursor cells and was regulated by antigenic stimulation and signals from the kinase mTOR. Our studies show that translation of transcripts encoding ribosomal proteins is regulated during the differentiation of CD8(+) Teff cells and might have a role in fate 'decisions' involved in the formation of memory cells.},
author = {Araki, Koichi and Morita, Masahiro and Bederman, Annelise G. and Konieczny, Bogumila T. and Kissick, Haydn T. and Sonenberg, Nahum and Ahmed, Rafi},
doi = {10.1038/ni.3795},
file = {:Users/nils/Documents/Mendeley Desktop/araki2017.pdf:pdf},
issn = {15292916},
journal = {Nature Immunology},
number = {9},
pages = {1046--1057},
pmid = {28714979},
publisher = {Nature Publishing Group},
title = {{Translation is actively regulated during the differentiation of CD8 + effector T cells}},
url = {http://dx.doi.org/10.1038/ni.3795},
volume = {18},
year = {2017}
}
@article{Tan2017,
abstract = {Global transcriptomic and proteomic analyses of T cells have been rich sources of unbiased data for understanding T-cell activation. Lack of full concordance of these datasets has illustrated that important facets of T-cell activation are controlled at the level of translation. We undertook translatome analysis of CD8 T-cell activation, combining polysome profiling and microarray analysis. We revealed that altering T-cell receptor stimulation influenced recruitment of mRNAs to heavy polysomes and translation of subsets of genes. A major pathway that was compromised, when TCR signaling was suboptimal, was linked to ribosome biogenesis, a rate-limiting factor in both cell growth and proliferation. Defective TCR signaling affected transcription and processing of ribosomal RNA precursors, as well as the translation of specific ribosomal proteins and translation factors. Mechanistically, IL-2 production was compromised in weakly stimulated T cells, affecting the abundance of Myc protein, a known regulator of ribosome biogenesis. Consequently, weakly activated T cells showed impaired production of ribosomes and a failure to maintain proliferative capacity after stimulation. We demonstrate that primary T cells respond to various environmental cues by regulating ribosome biogenesis and mRNA translation at multiple levels to sustain proliferation and differentiation.},
author = {Tan, Thomas C. J. and Knight, John and Sbarrato, Thomas and Dudek, Kate and Willis, Anne E. and Zamoyska, Rose},
doi = {10.1073/pnas.1700939114},
file = {:Users/nils/Documents/Mendeley Desktop/Tan et al. - 2017 - Suboptimal T-cell receptor signaling compromises protein translation, ribosome biogenesis, and proliferation of mous.pdf:pdf},
isbn = {1700939114},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
number = {30},
pages = {E6117--E6126},
pmid = {28696283},
title = {{Suboptimal T-cell receptor signaling compromises protein translation, ribosome biogenesis, and proliferation of mouse CD8 T cells}},
url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1700939114},
volume = {114},
year = {2017}
}
@article{Hagai2018,
author = {Hagai, Tzachi and Chen, Xi and Miragaia, Ricardo J and Rostom, Raghd and Gomes, Tom{\'{a}}s and Kunowska, Natalia and Henriksson, Johan and Park, Jong-eun and Proserpio, Valentina and Donati, Giacomo and Bossini-castillo, Lara and Braga, Felipe A Vieira and Naamati, Guy and Fletcher, James and Stephenson, Emily and Vegh, Peter and Trynka, Gosia and Kondova, Ivanela and Dennis, Mike and Haniffa, Muzlifah and Nourmohammad, Armita and L{\"{a}}ssig, Michael and Teichmann, Sarah A.},
doi = {10.1038/s41586-018-0657-2},
file = {:Users/nils/Documents/Mendeley Desktop/hagai2018.pdf:pdf},
journal = {Nature},
pages = {197--202},
title = {{Gene expression variability across cells and species shapes innate immunity}},
volume = {563},
year = {2018}
}
@article{Klein2015,
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},
file = {:Users/nils/Documents/Mendeley Desktop/Klein et al. - 2015 - Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells.pdf:pdf},
issn = {00928674},
journal = {Cell},
number = {5},
pages = {1187--1201},
publisher = {Elsevier Inc.},
title = {{Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0092867415005000},
volume = {161},
year = {2015}
}
@article{Kernfeld2018,
abstract = {Thymus development is critical to the adaptive immune system, yet a comprehensive transcriptional framework capturing thymus organogenesis at single-cell resolution is still needed. We applied single-cell RNA sequencing (RNA-seq) to capture 8 days of thymus development, perturbations of T cell receptor rearrangement, and in vitro organ cultures, producing profiles of 24,279 cells. We resolved transcriptional heterogeneity of developing lymphocytes, and genetic perturbation confirmed T cell identity of conventional and non-conventional lymphocytes. We characterized maturation dynamics of thymic epithelial cells in vivo, classified cell maturation state in a thymic organ culture, and revealed the intrinsic capacity of thymic epithelium to preserve transcriptional regularity despite exposure to exogenous retinoic acid. Finally, by integrating the cell atlas with human genome-wide association study (GWAS) data and autoimmune-disease-related genes, we implicated embryonic thymus-resident cells as possible participants in autoimmune disease etiologies. This resource provides a single-cell transcriptional framework for biological discovery and molecular analysis of thymus organogenesis. Studies of thymus development typically lack single-cell resolution, use indirect readouts, or target narrow cell subsets. Using single-cell RNA sequencing during thymus organogenesis, Kernfeld and Genga et al. reveal cellular heterogeneity, interrogate developmental dynamics by direct observation, and pinpoint cell-specific expression patterns in stromal and blood populations.},
author = {Kernfeld, Eric M. and Genga, Ryan M.J. and Neherin, Kashfia and Magaletta, Margaret E. and Xu, Ping and Maehr, Ren{\'{e}}},
doi = {10.1016/j.immuni.2018.04.015},
file = {:Users/nils/Documents/Mendeley Desktop/10.1016@j.immuni.2018.04.015.pdf:pdf},
isbn = {1074-7613},
issn = {10974180},
journal = {Immunity},
keywords = {Drop-seq,cell atlas,development,lymphocytes,lymphoid organ,single-cell RNA-seq,thymic epithelium,thymus,thymus organogenesis,transcriptomics},
pages = {1--13},
pmid = {29884461},
title = {{A Single-Cell Transcriptomic Atlas of Thymus Organogenesis Resolves Cell Types and Developmental Maturation}},
volume = {48},
year = {2018}
}
@book{Carroll1998,
address = {Chichester, UK},
author = {Carroll, Raymond J.},
booktitle = {Encyclopedia of Biostatistics},
doi = {10.1002/9781118445112.stat05178},
file = {:Users/nils/Documents/Mendeley Desktop/Carroll, Carroll, J. - 2014 - Measurement Error in Epidemiologic Studies.pdf:pdf},
keywords = {SIMEX (simulation extrapolation),attenuation,errors‐in‐variables,instrumental variables,latent variables,measurement error,misclassification,regression calibration},
month = {dec},
publisher = {John Wiley {\&} Sons, Ltd},
title = {{Measurement Error in Epidemiologic Studies}},
year = {1998}
}
@article{Macosko2015,
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},
file = {:Users/nils/Documents/Mendeley Desktop/Macosko et al. - 2015 - Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.pdf:pdf},
issn = {00928674},
journal = {Cell},
number = {5},
pages = {1202--1214},
publisher = {Elsevier},
title = {{Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0092867415005498},
volume = {161},
year = {2015}
}
@article{Saelens2019,
  title = {A Comparison of Single-Cell Trajectory Inference Methods},
  author = {Saelens, Wouter and Cannoodt, Robrecht and Todorov, Helena and Saeys, Yvan},
  year = {2019},
  month = may,
  volume = {37},
  pages = {547--554},
  issn = {1087-0156, 1546-1696},
  doi = {10.1038/s41587-019-0071-9},
  file = {/home/alan/Zotero/storage/YZ3DGBAE/Saelens et al. - 2019 - A comparison of single-cell trajectory inference m.pdf},
  journal = {Nature Biotechnology},
  language = {en},
  number = {5}
}
@article{Smith1993,
  title = {Bayesian {{Computation Via}} the {{Gibbs Sampler}} and {{Related Markov Chain Monte Carlo Methods}}},
  author = {Smith, A. F. M. and Roberts, G. O.},
  year = {1993},
  month = sep,
  volume = {55},
  pages = {3--23},
  issn = {00359246},
  doi = {10.1111/j.2517-6161.1993.tb01466.x},
  abstract = {The use of the Gibbs sampler for Bayesian computation is reviewed and illustrated in the context of some canonical examples. Other Markov chain Monte Carlo simulation methods are also briefly described, and comments are made on the advantages of sample-based approaches for Bayesian inference summaries.},
  file = {/home/alan/Zotero/storage/W9RP9PJS/Smith and Roberts - 1993 - Bayesian Computation Via the Gibbs Sampler and Rel.pdf},
  journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
  language = {en},
  number = {17}
}
@article{Cowles1996,
  title = {Markov {{Chain Monte Carlo Convergence Diagnostics}}: {{A Comparative Review}}},
  shorttitle = {Markov {{Chain Monte Carlo Convergence Diagnostics}}},
  author = {Cowles, Mary Kathryn and Carlin, Bradley P.},
  year = {1996},
  month = jun,
  volume = {91},
  pages = {883--904},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.1996.10476956},
  file = {/home/alan/Zotero/storage/HI2LZNDX/Cowles and Carlin - 1996 - Markov Chain Monte Carlo Convergence Diagnostics .pdf},
  journal = {Journal of the American Statistical Association},
  language = {en},
  number = {434}
}
@article{Eling2019,
  title = {Challenges in Measuring and Understanding Biological Noise},
  author = {Eling, Nils and Morgan, Michael D. and Marioni, John C.},
  year = {2019},
  month = sep,
  volume = {20},
  pages = {536--548},
  issn = {1471-0056, 1471-0064},
  doi = {10.1038/s41576-019-0130-6},
  abstract = {Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as `noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-t o-cell variability in numerous biological contexts has been revolutionized by recent advances in single-c ell technology, from imaging approaches through to `omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.},
  file = {/home/alan/Zotero/storage/X26SMRRG/Eling et al. - 2019 - Challenges in measuring and understanding biologic.pdf},
  journal = {Nature Reviews Genetics},
  language = {en},
  number = {9}
}

@article{Townes2019,
  title = {Feature Selection and Dimension Reduction for Single-Cell {{RNA}}-{{Seq}} Based on a Multinomial Model},
  author = {Townes, F. William and Hicks, Stephanie C. and Aryee, Martin J. and Irizarry, Rafael A.},
  year = {2019},
  month = dec,
  volume = {20},
  pages = {295},
  issn = {1474-760X},
  doi = {10.1186/s13059-019-1861-6},
  abstract = {Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.},
  file = {/home/alan/Zotero/storage/DVCYIZEA/Townes et al. - 2019 - Feature selection and dimension reduction for sing.pdf},
  journal = {Genome Biology},
  language = {en},
  number = {1}
}
@article{Lopez2018,
  title = {Deep Generative Modeling for Single-Cell Transcriptomics},
  author = {Lopez, Romain and Regier, Jeffrey and Cole, Michael B. and Jordan, Michael I. and Yosef, Nir},
  year = {2018},
  month = dec,
  volume = {15},
  pages = {1053--1058},
  issn = {1548-7091, 1548-7105},
  doi = {10.1038/s41592-018-0229-2},
  file = {/home/alan/Zotero/storage/ZGXHGR5W/Lopez et al. - 2018 - Deep generative modeling for single-cell transcrip.pdf},
  journal = {Nature Methods},
  language = {en},
  number = {12}
}
@techreport{Amezquita2019,
  title = {Orchestrating {{Single}}-{{Cell Analysis}} with {{Bioconductor}}},
  author = {Amezquita, Robert A. and Carey, Vince J. and Carpp, Lindsay N. and Geistlinger, Ludwig and Lun, Aaron T. L. and Marini, Federico and {Rue-Albrecht}, Kevin and Risso, Davide and Soneson, Charlotte and Waldron, Levi and Pag{\`e}s, Herv{\'e} and Smith, Mike and Huber, Wolfgang and Morgan, Martin and Gottardo, Raphael and Hicks, Stephanie C.},
  year = {2019},
  month = mar,
  institution = {{Genomics}},
  doi = {10.1101/590562},
  file = {/home/alan/Zotero/storage/SAKIES9G/Amezquita et al. - 2019 - Orchestrating Single-Cell Analysis with Bioconduct.pdf},
  language = {en},
  type = {Preprint}
}
@article{Ritchie2015,
  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.},
  year = {2015},
  month = apr,
  volume = {43},
  pages = {e47-e47},
  issn = {1362-4962, 0305-1048},
  doi = {10.1093/nar/gkv007},
  file = {/home/alan/Zotero/storage/PJFCJ3CA/Ritchie et al. - 2015 - limma powers differential expression analyses for .pdf},
  journal = {Nucleic Acids Research},
  language = {en},
  number = {7}
}
@article{Love2014,
  title = {Moderated Estimation of Fold Change and Dispersion for {{RNA}}-Seq Data with {{DESeq2}}},
  author = {Love, Michael I and Huber, Wolfgang and Anders, Simon},
  year = {2014},
  month = dec,
  volume = {15},
  pages = {550},
  issn = {1474-760X},
  doi = {10.1186/s13059-014-0550-8},
  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.},
  file = {/home/alan/Zotero/storage/M292X3QP/Love et al. - 2014 - Moderated estimation of fold change and dispersion.pdf},
  journal = {Genome Biology},
  language = {en},
  number = {12}
}
@article{McCarthy2012,
  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.},
  year = {2012},
  month = may,
  volume = {40},
  pages = {4288--4297},
  issn = {1362-4962, 0305-1048},
  doi = {10.1093/nar/gks042},
  file = {/home/alan/Zotero/storage/H9C59RSH/McCarthy et al. - 2012 - Differential expression analysis of multifactor RN.pdf},
  journal = {Nucleic Acids Research},
  language = {en},
  number = {10}
}
@article{Wills2013,
  title = {Single-Cell Gene Expression Analysis Reveals Genetic Associations Masked in Whole-Tissue Experiments},
  author = {Wills, Quin F and Livak, Kenneth J and Tipping, Alex J and Enver, Tariq and Goldson, Andrew J and Sexton, Darren W and Holmes, Chris},
  year = {2013},
  month = aug,
  volume = {31},
  pages = {748--752},
  issn = {1087-0156, 1546-1696},
  doi = {10.1038/nbt.2642},
  file = {/home/alan/Zotero/storage/MSJEVS7L/Wills et al. - 2013 - Single-cell gene expression analysis reveals genet.pdf},
  journal = {Nature Biotechnology},
  language = {en},
  number = {8}
}
@article{Buettner2015,
  title = {Computational Analysis of Cell-to-Cell Heterogeneity in Single-Cell {{RNA}}-Sequencing Data Reveals Hidden Subpopulations of Cells},
  author = {Buettner, Florian and Natarajan, Kedar N and Casale, F Paolo and Proserpio, Valentina and Scialdone, Antonio and Theis, Fabian J and Teichmann, Sarah A and Marioni, John C and Stegle, Oliver},
  year = {2015},
  month = feb,
  volume = {33},
  pages = {155--160},
  issn = {1087-0156, 1546-1696},
  doi = {10.1038/nbt.3102},
  file = {/home/alan/Zotero/storage/XBBMLKDS/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.pdf},
  journal = {Nature Biotechnology},
  language = {en},
  number = {2}
}
@article{Arriaga2009,
  title = {Determining Biological Noise via Single Cell Analysis},
  author = {Arriaga, Edgar A.},
  year = {2009},
  month = jan,
  volume = {393},
  pages = {73--80},
  issn = {1618-2642, 1618-2650},
  doi = {10.1007/s00216-008-2431-z},
  abstract = {Single cell analysis techniques describe the cellular heterogeneity that originates from fundamental stochastic variations in each of the molecular processes underlying cell function. The quantitative description of this set of variations is called biological noise and includes intrinsic and extrinsic noise. The former refers to stochastic variations directly involved with a given process, while the latter is due to environmental factors associated with other processes. Mathematical models are successful in predicting noise trends in simple biological systems, but it takes single cell techniques such as flow cytometry and time lapse microscopy to determine and dissect biological noise. This review describes several approaches that have been successfully used to describe biological noise.},
  file = {/home/alan/Zotero/storage/GQEQ3BQ7/Arriaga - 2009 - Determining biological noise via single cell analy.pdf},
  journal = {Analytical and Bioanalytical Chemistry},
  language = {en},
  number = {1}
}
@article{Fu2012,
  title = {A Multiply Redundant Genetic Switch 'locks in' the Transcriptional Signature of Regulatory {{T}} Cells},
  author = {Fu, Wenxian and Ergun, Ayla and Lu, Ting and Hill, Jonathan A and Haxhinasto, Sokol and Fassett, Marlys S and Gazit, Roi and Adoro, Stanley and Glimcher, Laurie and Chan, Susan and Kastner, Philippe and Rossi, Derrick and Collins, James J and Mathis, Diane and Benoist, Christophe},
  year = {2012},
  month = oct,
  volume = {13},
  pages = {972--980},
  issn = {1529-2908, 1529-2916},
  doi = {10.1038/ni.2420},
  file = {/home/alan/Zotero/storage/2XI5IE9L/Fu et al. - 2012 - A multiply redundant genetic switch 'locks in' the.pdf},
  journal = {Nature Immunology},
  language = {en},
  number = {10}
}
@article{Best2013,
  title = {Transcriptional Insights into the {{CD8}}+ {{T}} Cell Response to Infection and Memory {{T}} Cell Formation},
  author = {Best, J Adam and Knell, Jamie and Yang, Edward and Mayya, Viveka and Doedens, Andrew and Dustin, Michael L and Goldrath, Ananda W},
  year = {2013},
  month = apr,
  volume = {14},
  pages = {404--412},
  issn = {1529-2908, 1529-2916},
  doi = {10.1038/ni.2536},
  file = {/home/alan/Zotero/storage/WMXXIRCC/The Immunological Genome Project Consortium et al. - 2013 - Transcriptional insights into the CD8+ T cell resp.pdf},
  journal = {Nature Immunology},
  language = {en},
  number = {4}
}
@article{Zhu2010,
  title = {Peripheral {{CD4}}+ {{T}}-Cell Differentiation Regulated by Networks of Cytokines and Transcription Factors: {{Transcription}} Factor Network in {{Th}} Cells},
  shorttitle = {Peripheral {{CD4}}+ {{T}}-Cell Differentiation Regulated by Networks of Cytokines and Transcription Factors},
  author = {Zhu, Jinfang and Paul, William E.},
  year = {2010},
  month = nov,
  volume = {238},
  pages = {247--262},
  issn = {01052896},
  doi = {10.1111/j.1600-065X.2010.00951.x},
  abstract = {CD4+ T cells, also known as T-helper (Th) cells, play an important role in orchestrating adaptive immune responses to various infectious agents. They are also involved in the induction of autoimmune and allergic diseases. Upon T-cell receptor (TCR)-mediated cell activation, naive CD4+ T cells can differentiate into at least four major lineages, Th1, Th2, Th17, and iTreg cells, that participate in different types of immune responses. Networks of cytokines and transcription factors are critical for determining CD4+ T-cell fates and effector cytokine production. Here, we review collaboration and cross-regulation between various essential cytokines in the activation {$\fracslash$} induction of key transcription factors during the process of Th cell differentiation towards these distinct lineages. We also discuss the interactions of key transcription factors at both genetic and protein levels and the function of the resulting network(s) in regulating the expression of effector cytokines.},
  file = {/home/alan/Zotero/storage/EX9N6CQP/Zhu and Paul - 2010 - Peripheral CD4+ T-cell differentiation regulated b.pdf},
  journal = {Immunological Reviews},
  language = {en},
  number = {1}
}
@article{Islam2014,
  title = {Quantitative Single-Cell {{RNA}}-Seq with Unique Molecular Identifiers},
  author = {Islam, Saiful and Zeisel, Amit and Joost, Simon and La Manno, Gioele and Zajac, Pawel and Kasper, Maria and L{\"o}nnerberg, Peter and Linnarsson, Sten},
  year = {2014},
  month = feb,
  volume = {11},
  pages = {163--166},
  issn = {1548-7091, 1548-7105},
  doi = {10.1038/nmeth.2772},
  file = {/home/alan/Zotero/storage/YXAL4TB8/Islam et al. - 2014 - Quantitative single-cell RNA-seq with unique molec.pdf},
  journal = {Nature Methods},
  language = {en},
  number = {2}
}
@article{Mojtahedi2016,
  title = {Cell {{Fate Decision}} as {{High}}-{{Dimensional Critical State Transition}}},
  author = {Mojtahedi, Mitra and Skupin, Alexander and Zhou, Joseph and Casta{\~n}o, Ivan G. and {Leong-Quong}, Rebecca Y. Y. and Chang, Hannah and Trachana, Kalliopi and Giuliani, Alessandro and Huang, Sui},
  year = {2016},
  month = dec,
  volume = {14},
  pages = {e2000640},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.2000640},
  file = {/home/alan/Zotero/storage/GUJ9QJRV/Mojtahedi et al. - 2016 - Cell Fate Decision as High-Dimensional Critical St.pdf},
  journal = {PLOS Biology},
  language = {en},
  number = {12}
}
@article{Lin2019,
  title = {Evaluating Stably Expressed Genes in Single Cells},
  author = {Lin, Yingxin and Ghazanfar, Shila and Strbenac, Dario and Wang, Andy and Patrick, Ellis and Lin, David M and Speed, Terence and Yang, Jean Y H and Yang, Pengyi},
  year = {2019},
  month = sep,
  volume = {8},
  pages = {giz106},
  issn = {2047-217X},
  doi = {10.1093/gigascience/giz106},
  abstract = {Background: Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. Results: Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the ''Mouse Atlas'' dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells. Conclusions: SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, ''scSEGIndex,'' is incorporated into the scMerge Bioconductor R package (https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html) and can be used for identifying genes with stable expression in scRNA-seq datasets.},
  file = {/home/alan/Zotero/storage/EFQFCDP9/Lin et al. - 2019 - Evaluating stably expressed genes in single cells.pdf},
  journal = {GigaScience},
  language = {en},
  number = {9}
}
@article{Haque2017,
  title = {A Practical Guide to Single-Cell {{RNA}}-Sequencing for Biomedical Research and Clinical Applications},
  author = {Haque, Ashraful and Engel, Jessica and Teichmann, Sarah A. and L{\"o}nnberg, Tapio},
  year = {2017},
  month = dec,
  volume = {9},
  pages = {75},
  issn = {1756-994X},
  doi = {10.1186/s13073-017-0467-4},
  abstract = {RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology\textemdash{}the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.},
  file = {/home/alan/Zotero/storage/59XCU6GG/Haque et al. - 2017 - A practical guide to single-cell RNA-sequencing fo.pdf},
  journal = {Genome Medicine},
  language = {en},
  number = {1}
}
@article{Roberts2009,
  title = {Examples of {{Adaptive MCMC}}},
  author = {Roberts, Gareth O. and Rosenthal, Jeffrey S.},
  year = {2009},
  month = jan,
  volume = {18},
  pages = {349--367},
  issn = {1061-8600, 1537-2715},
  doi = {10.1198/jcgs.2009.06134},
  file = {/home/alan/Zotero/storage/SUSDK726/Roberts and Rosenthal - 2009 - Examples of Adaptive MCMC.pdf},
  journal = {Journal of Computational and Graphical Statistics},
  language = {en},
  number = {2}
}
@article{Boettiger2015,
  title = {An Introduction to {{Docker}} for Reproducible Research},
  author = {Boettiger, Carl},
  year = {2015},
  month = jan,
  volume = {49},
  pages = {71--79},
  issn = {0163-5980},
  doi = {10.1145/2723872.2723882},
  abstract = {As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a `DevOps' philosophy, to address these challenges. I illustrate this with several examples of Docker use with a focus on the R statistical environment.},
  file = {/home/alan/Zotero/storage/537ICY95/Boettiger - 2015 - An introduction to Docker for reproducible researc.pdf},
  journal = {ACM SIGOPS Operating Systems Review},
  language = {en},
  number = {1}
}
@article{Lahnemann2020,
  title = {Eleven Grand Challenges in Single-Cell Data Science},
  author = {L{\"a}hnemann, David and K{\"o}ster, Johannes and Szczurek, Ewa and McCarthy, Davis J. and Hicks, Stephanie C. and Robinson, Mark D. and Vallejos, Catalina A. and Campbell, Kieran R. and Beerenwinkel, Niko and Mahfouz, Ahmed and Pinello, Luca and Skums, Pavel and Stamatakis, Alexandros and Attolini, Camille Stephan-Otto and Aparicio, Samuel and Baaijens, Jasmijn and Balvert, Marleen and de Barbanson, Buys and Cappuccio, Antonio and Corleone, Giacomo and Dutilh, Bas E. and Florescu, Maria and Guryev, Victor and Holmer, Rens and Jahn, Katharina and Lobo, Thamar Jessurun and Keizer, Emma M. and Khatri, Indu and Kielbasa, Szymon M. and Korbel, Jan O. and Kozlov, Alexey M. and Kuo, Tzu-Hao and Lelieveldt, Boudewijn P.F. and Mandoiu, Ion I. and Marioni, John C. and Marschall, Tobias and M{\"o}lder, Felix and Niknejad, Amir and Raczkowski, Lukasz and Reinders, Marcel and de Ridder, Jeroen and Saliba, Antoine-Emmanuel and Somarakis, Antonios and Stegle, Oliver and Theis, Fabian J. and Yang, Huan and Zelikovsky, Alex and McHardy, Alice C. and Raphael, Benjamin J. and Shah, Sohrab P. and Sch{\"o}nhuth, Alexander},
  year = {2020},
  month = dec,
  volume = {21},
  pages = {31},
  issn = {1474-760X},
  doi = {10.1186/s13059-020-1926-6},
  abstract = {The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands\textemdash{}or even millions\textemdash{}of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.},
  file = {/home/alan/Zotero/storage/CR6KK5HU/Lähnemann et al. - 2020 - Eleven grand challenges in single-cell data scienc.pdf},
  journal = {Genome Biology},
  language = {en},
  number = {1}
}

@article{Soneson2018,
  title = {Bias, Robustness and Scalability in Single-Cell Differential Expression Analysis},
  author = {Soneson, Charlotte and Robinson, Mark D},
  year = {2018},
  month = apr,
  volume = {15},
  pages = {255--261},
  issn = {1548-7091, 1548-7105},
  doi = {10.1038/nmeth.4612},
  file = {/home/alan/Zotero/storage/BACKPF75/Soneson and Robinson - 2018 - Bias, robustness and scalability in single-cell di.pdf},
  journal = {Nature Methods},
  language = {en},
  number = {4}
}


@article{Finak2015,
  title = {{{MAST}}: A Flexible Statistical Framework for Assessing Transcriptional Changes and Characterizing Heterogeneity in Single-Cell {{RNA}} Sequencing Data},
  shorttitle = {{{MAST}}},
  author = {Finak, Greg and McDavid, Andrew and Yajima, Masanao and Deng, Jingyuan and Gersuk, Vivian and Shalek, Alex K. and Slichter, Chloe K. and Miller, Hannah W. and McElrath, M. Juliana and Prlic, Martin and Linsley, Peter S. and Gottardo, Raphael},
  year = {2015},
  month = dec,
  volume = {16},
  pages = {278},
  issn = {1474-760X},
  doi = {10.1186/s13059-015-0844-5},
  abstract = {Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST.},
  file = {/home/alan/Zotero/storage/QJSG7KQM/Finak et al. - 2015 - MAST a flexible statistical framework for assessi.pdf},
  journal = {Genome Biology},
  language = {en},
  number = {1}
}

@article{Durinck2009,
  title = {Mapping Identifiers for the Integration of Genomic Datasets with the {{R}}/{{Bioconductor}} Package {{biomaRt}}},
  author = {Durinck, Steffen and Spellman, Paul T and Birney, Ewan and Huber, Wolfgang},
  year = {2009},
  month = aug,
  volume = {4},
  pages = {1184--1191},
  issn = {1754-2189, 1750-2799},
  doi = {10.1038/nprot.2009.97},
  file = {/home/alan/Zotero/storage/YMJ6I3U9/Durinck et al. - 2009 - Mapping identifiers for the integration of genomic.pdf},
  journal = {Nature Protocols},
  language = {en},
  number = {8}
}

@Article{Gu2016,
  title = {Complex heatmaps reveal patterns and correlations in multidimensional genomic data},
  author = {Zuguang Gu and Roland Eils and Matthias Schlesner},
  journal = {Bioinformatics},
  year = {2016},
}

@article{Carroll2005,
  title = {Measurement {{Error}} in {{Epidemiologic Studies}}},
  author = {Carroll, Raymond J},
  year = {2005},
  pages = {38},
  abstract = {This article concerns measurement error in epidemilogic studies, often called errors-in-variables. I discuss what measurement error is, its impact on parameter estimates and hypothesis tests, and how to perform statistical analyses that account for the measurement error.},
  file = {/home/alan/Zotero/storage/PPHYZWTG/Carroll - 2005 - Measurement Error in Epidemiologic Studies.pdf},
  language = {en}
}

@article{Ziegler1994,
  title = {The Activation Antigen {{CD69}}},
  author = {Ziegler, Steven F. and Ramsdell, Fred and Alderson, Mark R.},
  year = {1994},
  volume = {12},
  pages = {456--465},
  issn = {10665099, 15494918},
  doi = {10.1002/stem.5530120502},
  abstract = {One of the earliest cell surface antigens expressed by T cells following activation is CD69, which is detectable within one h of ligation of the T cell receptorKD3 complex. Once expressed, CD69 acts as a costimulatory molecule for T cell activation and proliferation. In addition to mature T cells, CD69 is inducibly expressed by immature thymocytes, B cells, natural killer (NK) cells, monocytes, neutrophils and eosinophils, and is constitutively expressed by mature thymocytes and platelets. Recently, cDNA clones encoding human and mouse CD69 were isolated and showed CD69 to be a member of the C-type lectin superfamily. Gene mapping studies have placed CD69 on distal mouse chromosome 6 and human chromosome 1 2 \textasciitilde{} 1 3c,lose to, if not in, the NK gene complex. The structure, chromosomal localization, expression and function of CD69 suggest that it is likely a pleiotropic immune regulator, potentially important not only in NK cell function but also in the activation and differentiation of a wide variety of hematopoietic cells.},
  file = {/home/alan/Zotero/storage/6QNQBXY2/Ziegler et al. - 1994 - The activation antigen CD69.pdf},
  journal = {Stem Cells},
  language = {en},
  number = {5}
}

@article{Svensson2018,
  title = {Exponential Scaling of Single-Cell {{RNA}}-Seq in the Past Decade},
  author = {Svensson, Valentine and {Vento-Tormo}, Roser and Teichmann, Sarah A},
  year = {2018},
  month = apr,
  volume = {13},
  pages = {599--604},
  issn = {1754-2189, 1750-2799},
  doi = {10.1038/nprot.2017.149},
  file = {/home/alan/Zotero/storage/77G2MMA2/Svensson et al. - 2018 - Exponential scaling of single-cell RNA-seq in the .pdf},
  journal = {Nature Protocols},
  language = {en},
  number = {4}
}

@article{Newton2004,
  title = {Detecting Differential Gene Expression with a Semiparametric Hierarchical Mixture Method},
  author = {Newton, M. A.},
  year = {2004},
  month = apr,
  volume = {5},
  pages = {155--176},
  issn = {1465-4644, 1468-4357},
  doi = {10.1093/biostatistics/5.2.155},
  abstract = {Mixture modeling provides an effective approach to the differential expression problem in microarray data analysis. Methods based on fully parametric mixture models are available, but lack of fit in some examples indicates that more flexible models may be beneficial. Existing, more flexible, mixture models work at the level of one-dimensional gene-specific summary statistics, and so when there are relatively few measurements per gene these methods may not provide sensitive detectors of differential expression. We propose a hierarchical mixture model to provide methodology that is both sensitive in detecting differential expression and sufficiently flexible to account for the complex variability of normalized microarray data. EM-based algorithms are used to fit both parametric and semiparametric versions of the model. We restrict attention to the two-sample comparison problem; an experiment involving Affymetrix microarrays and yeast translation provides the motivating case study. Gene-specific posterior probabilities of differential expression form the basis of statistical inference; they define short gene lists and false discovery rates. Compared to several competing methodologies, the proposed methodology exhibits good operating characteristics in a simulation study, on the analysis of spike-in data, and in a cross-validation calculation.},
  file = {/home/alan/Zotero/storage/TU37UG4R/Newton - 2004 - Detecting differential gene expression with a semi.pdf},
  journal = {Biostatistics},
  language = {en},
  number = {2}
}

@article{Geweke1995,
author = {Geweke, John and In, Forthcoming},
year = {1995},
month = {11},
pages = {},
title = {Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments},
volume = {4}
}


@article{Koehler2009,
  title = {On the {{Assessment}} of {{Monte Carlo Error}} in {{Simulation}}-{{Based Statistical Analyses}}},
  author = {Koehler, Elizabeth and Brown, Elizabeth and Haneuse, Sebastien J.-P. A.},
  year = {2009},
  month = may,
  volume = {63},
  pages = {155--162},
  issn = {0003-1305, 1537-2731},
  doi = {10.1198/tast.2009.0030},
  file = {/home/alan/Zotero/storage/HEVTU93D/Koehler et al. - 2009 - On the Assessment of Monte Carlo Error in Simulati.pdf},
  journal = {The American Statistician},
  language = {en},
  number = {2}
}
@article{Heidelberger1981,
author = {Heidelberger, Philip and Welch, Peter D.},
title = {A Spectral Method for Confidence Interval Generation and Run Length Control in Simulations},
year = {1981},
issue_date = {April 1981},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {24},
number = {4},
issn = {0001-0782},
url = {https://doi.org/10.1145/358598.358630},
doi = {10.1145/358598.358630},
journal = {Commun. ACM},
month = apr,
pages = {233–245},
numpages = {13},
keywords = {variance estimation, spectral analysis, simulation, confidence interval, batch means}
}

@article{Townes2020,
  title = {Review of {{Probability Distributions}} for {{Modeling Count Data}}},
  author = {Townes, F. William},
  year = {2020},
  month = jan,
  abstract = {Count data take on non-negative integer values and are challenging to properly analyze using standard linear-Gaussian methods such as linear regression and principal components analysis. Generalized linear models enable direct modeling of counts in a regression context using distributions such as the Poisson and negative binomial. When counts contain only relative information, multinomial or Dirichlet-multinomial models can be more appropriate. We review some of the fundamental connections between multinomial and count models from probability theory, providing detailed proofs. These relationships are useful for methods development in applications such as topic modeling of text data and genomics.},
  archivePrefix = {arXiv},
  eprint = {2001.04343},
  eprinttype = {arxiv},
  file = {/home/alan/Zotero/storage/SF6VLUAP/Townes - 2020 - Review of Probability Distributions for Modeling C.pdf},
  journal = {arXiv:2001.04343 [stat]},
  keywords = {Statistics - Machine Learning,Statistics - Methodology},
  language = {en},
  primaryClass = {stat}
}
@article{Svensson2020,
  title = {Droplet {{scRNA}}-Seq Is Not Zero-Inflated},
  author = {Svensson, Valentine},
  year = {2020},
  month = feb,
  volume = {38},
  pages = {147--150},
  issn = {1087-0156, 1546-1696},
  doi = {10.1038/s41587-019-0379-5},
  file = {/home/alan/Zotero/storage/22HLS9S7/Svensson - 2020 - Droplet scRNA-seq is not zero-inflated.pdf},
  journal = {Nature Biotechnology},
  language = {en},
  number = {2}
}


@article{Bochkina2007,
  title = {Tail {{Posterior Probability}} for {{Inference}} in {{Pairwise}} and {{Multiclass Gene Expression Data}}},
  author = {Bochkina, N. and Richardson, S.},
  year = {2007},
  month = dec,
  volume = {63},
  pages = {1117--1125},
  issn = {0006341X},
  doi = {10.1111/j.1541-0420.2007.00807.x},
  abstract = {We consider the problem of identifying differentially expressed genes in microarray data in a Bayesian framework with a noninformative prior distribution on the parameter quantifying differential expression. We introduce a new rule, tail posterior probability, based on the posterior distribution of the standardized difference, to identify genes differentially expressed between two conditions, and we derive a frequentist estimator of the false discovery rate associated with this rule. We compare it to other Bayesian rules in the considered settings. We show how the tail posterior probability can be extended to testing a compound null hypothesis against a class of specific alternatives in multiclass data.},
  file = {/home/alan/Zotero/storage/TGJB3W6K/Bochkina and Richardson - 2007 - Tail Posterior Probability for Inference in Pairwi.pdf},
  journal = {Biometrics},
  language = {en},
  number = {4}
}

@article{Maksimovic2020,
  title = {A Cross-Package {{Bioconductor}} Workflow for Analysing Methylation Array Data [Version 3; Peer Review: 4 Approved]},
  author = {Maksimovic, Jovana and Phipson, Belinda and Oshlack, Alicia},
  year = {2020},
  volume = {5},
  doi = {10.12688/f1000research.8839.3},
  file = {/home/alan/Zotero/storage/Q6MDS35H/5529c8af-85c8-491e-896e-a5c54ec6ec08_8839_-_jovana_maksimovic_v3.pdf},
  journal = {F1000Research},
  number = {1281}
}

@article{Casella1985,
  title = {An {{Introduction}} to {{Empirical Bayes Data Analysis}}},
  author = {Casella, George},
  year = {1985},
  month = may,
  volume = {39},
  pages = {83},
  issn = {00031305},
  doi = {10.2307/2682801},
  file = {/home/alan/Zotero/storage/2TFBGKG2/Casella - 1985 - An Introduction to Empirical Bayes Data Analysis.pdf},
  journal = {The American Statistician},
  number = {2}
}

@misc{Neufeld2022,
  title = {Inference after Latent Variable Estimation for Single-Cell {{RNA}} Sequencing Data},
  author = {Neufeld, Anna and Gao, Lucy L. and Popp, Joshua and Battle, Alexis and Witten, Daniela},
  year = {2022},
  month = jul,
  number = {arXiv:2207.00554},
  eprint = {2207.00554},
  eprinttype = {arxiv},
  primaryclass = {stat},
  publisher = {{arXiv}},
  abstract = {In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the individual cell's state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps, then standard methods for computing p-values and confidence intervals in the second step will fail to achieve statistical guarantees such as Type 1 error control. Furthermore, approaches such as sample splitting that can be applied to solve similar problems in other settings are not applicable in this context. In this paper, we introduce count splitting, a flexible framework that allows us to carry out valid inference in this setting, for virtually any latent variable estimation technique and inference approach, under a Poisson assumption. We demonstrate the Type 1 error control and power of count splitting in a simulation study, and apply count splitting to a dataset of pluripotent stem cells differentiating to cardiomyocytes.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Statistics - Applications,Statistics - Methodology},
  file = {/home/alan/Zotero/storage/Z434AAIK/Neufeld et al. - 2022 - Inference after latent variable estimation for sin.pdf}
}

@article{Benjamini1995,
  title = {Controlling the {{False Discovery Rate}}: {{A Practical}} and {{Powerful Approach}} to {{Multiple Testing}}},
  author = {Benjamini, Yoav and Hochberg, Yosef},
  year = {1995},
  journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
  volume = {57},
  number = {1},
  pages = {289--300},
  abstract = {The commonapproach to the multiplicityproblemcalls forcontrollingthe familywise errorrate(FWER). Thisapproach,though,has faults,and we pointout a few.A different approachto problemsof multiplesignificancetestingis presented.It calls forcontrolling theexpectedproportionof falselyrejectedhypothese-sthe falsediscoveryrate.Thiserror rateis equivalentto theFWER whenall hypotheseasretruebutis smallerotherwiseT. herefore,in problemswherethe controlof the false discoveryrate ratherthan thatof the FWER is desired,thereis potentialfora gain in power.A simplesequentialBonferronitypeprocedureis provedto controlthefalsediscoveryrateforindependenteststatistics, and a simulationstudyshows thatthe gain in poweris substantial.The use of the new procedureand theappropriatenessof thecriterionare illustratedwithexamples.},
  langid = {english},
  file = {/home/alan/Zotero/storage/D5NPYHK8/Benjamini and Hochberg - 1995 - Controlling the False Discovery Rate A Practical .pdf}
}

@article{Marx2021,
  title = {Method of the {{Year}}: Spatially Resolved Transcriptomics},
  shorttitle = {Method of the {{Year}}},
  author = {Marx, Vivien},
  year = {2021},
  month = jan,
  journal = {Nature Methods},
  volume = {18},
  number = {1},
  pages = {9--14},
  issn = {1548-7091, 1548-7105},
  doi = {10.1038/s41592-020-01033-y},
  langid = {english},
  file = {/home/alan/Zotero/storage/4UT58BEI/Marx - 2021 - Method of the Year spatially resolved transcripto.pdf}
}

@techreport{Aijo2019,
  type = {Preprint},
  title = {Splotch: {{Robust}} Estimation of Aligned Spatial Temporal Gene Expression Data},
  shorttitle = {Splotch},
  author = {{\"A}ij{\"o}, Tarmo and Maniatis, Silas and Vickovic, Sanja and Kang, Kristy and Cuevas, Miguel and Braine, Catherine and Phatnani, Hemali and Lundeberg, Joakim and Bonneau, Richard},
  year = {2019},
  month = sep,
  institution = {{Bioinformatics}},
  doi = {10.1101/757096},
  abstract = {Abstract           Spatial genomics technologies enable new approaches to study how cells interact and function in intact multicellular environments but present a host of technical and computational challenges. Here we describe Splotch, a novel computational framework for the analysis of spatially resolved transcriptomics data. Splotch aligns transcriptomics data from multiple tissue sections and timepoints to generate improved posterior estimates of gene expression. We demonstrate alignment of a large corpus of single-cell RNA-seq data into an automatically generated spatial-temporal coordinate and study optimal design for spatial transcriptomics experiments.},
  langid = {english}
}

@article{Lloyd-Smith2007,
  title = {Maximum {{Likelihood Estimation}} of the {{Negative Binomial Dispersion Parameter}} for {{Highly Overdispersed Data}}, with {{Applications}} to {{Infectious Diseases}}},
  author = {{Lloyd-Smith}, James O.},
  editor = {Rees, Mark},
  year = {2007},
  month = feb,
  journal = {PLoS ONE},
  volume = {2},
  number = {2},
  pages = {e180},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0000180},
  langid = {english},
  file = {/home/alan/Zotero/storage/FUMMVMLI/Lloyd-Smith - 2007 - Maximum Likelihood Estimation of the Negative Bino.pdf}
}
@article{Zappia2017,
  title = {Splatter: Simulation of Single-Cell {{RNA}} Sequencing Data},
  shorttitle = {Splatter},
  author = {Zappia, Luke and Phipson, Belinda and Oshlack, Alicia},
  year = {2017},
  month = dec,
  journal = {Genome Biology},
  volume = {18},
  number = {1},
  pages = {174},
  issn = {1474-760X},
  doi = {10.1186/s13059-017-1305-0},
  urldate = {2020-02-07},
  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.},
  langid = {english},
  file = {/home/alan/Zotero/storage/YLXBGQVH/Zappia et al. - 2017 - Splatter simulation of single-cell RNA sequencing.pdf}
}