Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
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[1] D. Craig,et al. Transcriptomics , 2020, Nature Biotechnology.
[2] F. Jamali,et al. Single dose pharmacokinetics and bioavailability of glucosamine in the rat. , 2002, Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques.
[3] Principal Investigators,et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris , 2018 .
[4] Leland McInnes,et al. UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..
[5] P. Verstreken,et al. A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain , 2018, Cell.
[6] Ambrose J. Carr,et al. Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment , 2018, Cell.
[7] Luyi Tian,et al. scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data , 2018, PLoS Comput. Biol..
[8] Lucas E. Wange,et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq , 2018, Nature Communications.
[9] Kevin R. Moon,et al. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion , 2018, Cell.
[10] Christoph Ziegenhain,et al. zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs , 2017, bioRxiv.
[11] Omri Wurtzel,et al. Cell type transcriptome atlas for the planarian Schmidtea mediterranea , 2018, Science.
[12] Lai Guan Ng,et al. Evaluation of UMAP as an alternative to t-SNE for single-cell data , 2018, bioRxiv.
[13] Laleh Haghverdi,et al. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors , 2018, Nature Biotechnology.
[14] M. Hemberg,et al. scmap: projection of single-cell RNA-seq data across data sets , 2018, Nature Methods.
[15] Yvan Saeys,et al. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools , 2018, bioRxiv.
[16] Charlotte Soneson,et al. Bias, robustness and scalability in single-cell differential expression analysis , 2018, Nature Methods.
[17] S. Orkin,et al. Mapping the Mouse Cell Atlas by Microwell-Seq , 2018, Cell.
[18] I. Nikaido,et al. Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs , 2018, Nature Communications.
[19] Leland McInnes,et al. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.
[20] David J. Jörg,et al. Defining murine organogenesis at single cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation , 2018, Nature Cell Biology.
[21] Richard H. Scheuermann,et al. Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing , 2017, bioRxiv.
[22] Chun Jimmie Ye,et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation , 2017, Nature Biotechnology.
[23] Ze-Guang Han,et al. Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development , 2017, BMC Genomics.
[24] John C. Marioni,et al. Pluripotent state transitions coordinate morphogenesis in mouse and human embryos , 2017, Nature.
[25] Phuong Dao,et al. Single-Cell Immune Map of Breast Carcinoma Reveals Diverse Phenotypic States Driven by the Tumor Microenvironment , 2017, bioRxiv.
[26] Joseph T. Roland,et al. Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut. , 2017, Cell systems.
[27] Shawn M. Gillespie,et al. Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer , 2017, Cell.
[28] Yarden Katz,et al. A single-cell survey of the small intestinal epithelium , 2017, Nature.
[29] Itai Yanai,et al. scDual-Seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNA-sequencing , 2017, Genome Biology.
[30] Luke Zappia,et al. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database , 2017, bioRxiv.
[31] Aleksandar Janjic,et al. mcSCRB-seq: sensitive and powerful single-cell RNA sequencing , 2017, bioRxiv.
[32] Fabian J. Theis,et al. Assessment of batch-correction methods for scRNA-seq data with a new test metric , 2017, bioRxiv.
[33] O. Stegle,et al. Single-cell epigenomics: Recording the past and predicting the future , 2017, Science.
[34] Lars E. Borm,et al. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing , 2017, Science.
[35] S. Quake,et al. Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns , 2017, Cell.
[36] Giovanni Iacono,et al. bigSCale: an analytical framework for big-scale single-cell data , 2017, bioRxiv.
[37] A. van Oudenaarden,et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations , 2017, Nature Methods.
[38] Jafar S. Jabbari,et al. Single cell RNA sequencing of stem cell-derived retinal ganglion cells , 2018, Scientific Data.
[39] Mattias Hansson,et al. Single-Cell Gene Expression Analysis of a Human ESC Model of Pancreatic Endocrine Development Reveals Different Paths to β-Cell Differentiation , 2017, Stem cell reports.
[40] Hannah A. Pliner,et al. Reversed graph embedding resolves complex single-cell trajectories , 2017, Nature Methods.
[41] Aviv Regev,et al. Massively-parallel single nucleus RNA-seq with DroNc-seq , 2017, Nature Methods.
[42] Berthold Göttgens,et al. Single-cell RNA-sequencing reveals a distinct population of proglucagon-expressing cells specific to the mouse upper small intestine , 2017, Molecular metabolism.
[43] H. Swerdlow,et al. Large-scale simultaneous measurement of epitopes and transcriptomes in single cells , 2017, Nature Methods.
[44] I. Nikaido,et al. Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads , 2017, bioRxiv.
[45] Cole Trapnell,et al. Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing , 2017, bioRxiv.
[46] Camille Stephan-Otto Attolini,et al. Mex3a Marks a Slowly Dividing Subpopulation of Lgr5+ Intestinal Stem Cells. , 2017, Cell stem cell.
[47] J. Aerts,et al. SCENIC: Single-cell regulatory network inference and clustering , 2017, Nature Methods.
[48] Wei Vivian Li,et al. scImpute: accurate and robust imputation for single cell RNA-seq data , 2017, bioRxiv.
[49] G. Sanguinetti,et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells , 2018, Nature Communications.
[50] R. Sandberg,et al. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia , 2017, Nature Medicine.
[51] Fabian J Theis,et al. The Human Cell Atlas , 2017, bioRxiv.
[52] T. Tuschl,et al. Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. , 2017, JCI insight.
[53] Rebecca Hodge,et al. STRT-seq-2i: dual-index 5ʹ single cell and nucleus RNA-seq on an addressable microwell array , 2017, bioRxiv.
[54] M. Newton,et al. SCnorm: robust normalization of single-cell RNA-seq data , 2017, Nature Methods.
[55] David A. Weitz,et al. Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices , 2017, Nature Reviews Genetics.
[56] N. Hacohen,et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors , 2017, Science.
[57] Nikolaus Rajewsky,et al. The Drosophila embryo at single-cell transcriptome resolution , 2017, Science.
[58] L. Penland,et al. High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis , 2017, bioRxiv.
[59] I. Amit,et al. Single-cell transcriptome conservation in cryopreserved cells and tissues , 2016, Genome Biology.
[60] I. Hellmann,et al. Comparative Analysis of Single-Cell RNA Sequencing Methods , 2016, bioRxiv.
[61] J. C. Love,et al. Seq-Well: A Portable, Low-Cost Platform for High-Throughput Single-Cell RNA-Seq of Low-Input Samples , 2017 .
[62] S. Linnarsson,et al. Single-cell mRNA isoform diversity in the mouse brain , 2017, BMC Genomics.
[63] Richard A. Muscat,et al. Scaling single cell transcriptomics through split pool barcoding , 2017, bioRxiv.
[64] Andrew C. Adey,et al. Single-Cell Transcriptional Profiling of a Multicellular Organism , 2017 .
[65] Salah Ayoub,et al. Cell fixation and preservation for droplet-based single-cell transcriptomics , 2017, bioRxiv.
[66] Andrew J. Hill,et al. Single-cell mRNA quantification and differential analysis with Census , 2017, Nature Methods.
[67] I. Amit,et al. Single-cell spatial reconstruction reveals global division of labor in the mammalian liver , 2016, Nature.
[68] Allon M. Klein,et al. Single-cell barcoding and sequencing using droplet microfluidics , 2016, Nature Protocols.
[69] B. Stripp,et al. Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis. , 2016, JCI insight.
[70] Wilko Weichert,et al. Single-Cell Analysis Uncovers Clonal Acinar Cell Heterogeneity in the Adult Pancreas. , 2016, Developmental cell.
[71] Mariella G. Filbin,et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma , 2016, Nature.
[72] Rickard Sandberg,et al. Single-cell sequencing of the small-RNA transcriptome , 2016, Nature Biotechnology.
[73] Mauro J. Muraro,et al. A Single-Cell Transcriptome Atlas of the Human Pancreas , 2016, Cell systems.
[74] Samuel L. Wolock,et al. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. , 2016, Cell systems.
[75] D. M. Smith,et al. Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes , 2016, Cell metabolism.
[76] Maria Kasper,et al. Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity , 2016, Cell systems.
[77] Hazen P Babcock,et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization , 2016, Proceedings of the National Academy of Sciences.
[78] Valentine Svensson,et al. Power Analysis of Single Cell RNA-Sequencing Experiments , 2016, Nature Methods.
[79] M. Schaub,et al. SC3 - consensus clustering of single-cell RNA-Seq data , 2016, Nature Methods.
[80] Cynthia C. Hession,et al. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons , 2016, Science.
[81] Aaron T. L. Lun,et al. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R , 2017, Bioinform..
[82] Marco Mignardi,et al. Fourth Generation of Next‐Generation Sequencing Technologies: Promise and Consequences , 2016, Human mutation.
[83] Mauro J. Muraro,et al. De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data , 2016, Cell stem cell.
[84] Evan Z. Macosko,et al. Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics , 2016, Cell.
[85] Grace X. Y. Zheng,et al. Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.
[86] William Stafford Noble,et al. Massively multiplex single-cell Hi-C , 2016, Nature Methods.
[87] Hongkai Ji,et al. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis , 2016, Nucleic acids research.
[88] Keegan D. Korthauer,et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments , 2016, Genome Biology.
[89] A. Heger,et al. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy , 2016, bioRxiv.
[90] Shuqiang Li,et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq , 2016, Genome Biology.
[91] J. Marioni,et al. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.
[92] Jonathan Y. Hsu,et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation , 2016, Nature Communications.
[93] Lior Pachter,et al. Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.
[94] Charles H. Yoon,et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq , 2016, Science.
[95] Anneliese O. Speak,et al. T cell fate and clonality inference from single cell transcriptomes , 2016, Nature Methods.
[96] Cuong To,et al. Miniaturization Technologies for Efficient Single-Cell Library Preparation for Next-Generation Sequencing , 2016, Journal of laboratory automation.
[97] Sara B. Linker,et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons , 2016, Nature Protocols.
[98] Aleksandra A. Kolodziejczyk,et al. Classification of low quality cells from single-cell RNA-seq data , 2016, Genome Biology.
[99] I. Amit,et al. Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors , 2016, Cell.
[100] C. Ponting,et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity , 2015, Nature Methods.
[101] Hui Wang,et al. SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis , 2015, PLoS Comput. Biol..
[102] Joseph L. Herman,et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis , 2015, Nature Methods.
[103] Fabian J. Theis,et al. Diffusion maps for high-dimensional single-cell analysis of differentiation data , 2015, Bioinform..
[104] Hans Clevers,et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types , 2015, Nature.
[105] Howard Y. Chang,et al. Single-cell chromatin accessibility reveals principles of regulatory variation , 2015, Nature.
[106] P. Linsley,et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data , 2015, Genome Biology.
[107] Chen Xu,et al. Identification of cell types from single-cell transcriptomes using a novel clustering method , 2015, Bioinform..
[108] Catalina A. Vallejos,et al. BASiCS: Bayesian Analysis of Single-Cell Sequencing Data , 2015, PLoS Comput. Biol..
[109] Andrew C. Adey,et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing , 2015, Science.
[110] Allon M. Klein,et al. Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.
[111] Aleksandra A. Kolodziejczyk,et al. The technology and biology of single-cell RNA sequencing. , 2015, Molecular cell.
[112] Evan Z. Macosko,et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.
[113] C. Ponting,et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes , 2015, Nature Methods.
[114] Camille Stephan-Otto Attolini,et al. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer , 2015, Nature Genetics.
[115] A. Regev,et al. Spatial reconstruction of single-cell gene expression , 2015, Nature Biotechnology.
[116] Matthew E. Ritchie,et al. limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.
[117] Åsa K. Björklund,et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects , 2014, Genome research.
[118] Paul Theodor Pyl,et al. HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.
[119] P. Kharchenko,et al. Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.
[120] Aaron M. Streets,et al. Microfluidic single-cell whole-transcriptome sequencing , 2014, Proceedings of the National Academy of Sciences.
[121] Sean C. Bendall,et al. Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development , 2014, Cell.
[122] N. Neff,et al. Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.
[123] D. Cacchiarelli,et al. Characterization of directed differentiation by high-throughput single-cell RNA-Seq , 2014, bioRxiv.
[124] I. Amit,et al. Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.
[125] Åsa K. Björklund,et al. Full-length RNA-seq from single cells using Smart-seq2 , 2014, Nature Protocols.
[126] Pawel Zajac,et al. Base Preferences in Non-Templated Nucleotide Incorporation by MMLV-Derived Reverse Transcriptases , 2013, PloS one.
[127] Gioele La Manno,et al. Quantitative single-cell RNA-seq with unique molecular identifiers , 2013, Nature Methods.
[128] N. Neff,et al. Quantitative assessment of single-cell RNA-sequencing methods , 2013, Nature Methods.
[129] Aleksandra A. Kolodziejczyk,et al. Accounting for technical noise in single-cell RNA-seq experiments , 2013, Nature Methods.
[130] Åsa K. Björklund,et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.
[131] Carolina Wählby,et al. In situ sequencing for RNA analysis in preserved tissue and cells , 2013, Nature Methods.
[132] S. Tai,et al. Complete disassociation of adult pancreas into viable single cells through cold trypsin-EDTA digestion , 2013, Journal of Zhejiang University SCIENCE B.
[133] H. Ueda,et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity , 2013, Genome Biology.
[134] Alexander van Oudenaarden,et al. Single molecule fluorescent in situ hybridization (smFISH) of C. elegans worms and embryos. , 2012, WormBook : the online review of C. elegans biology.
[135] T. Hashimshony,et al. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. , 2012, Cell reports.
[136] D. Bruder,et al. Impact of enzymatic tissue disintegration on the level of surface molecule expression and immune cell function. , 2012, European journal of microbiology & immunology.
[137] R. Sandberg,et al. Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells , 2012, Nature Biotechnology.
[138] Pawel Zajac,et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing , 2012, Nature Protocols.
[139] Davis J. McCarthy,et al. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation , 2012, Nucleic acids research.
[140] Wei Liu,et al. Sample preparation method for isolation of single‐cell types from mouse liver for proteomic studies , 2011, Proteomics.
[141] Colin N. Dewey,et al. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.
[142] Arnoud Sonnenberg,et al. Integrin α6β4 identifies an adult distal lung epithelial population with regenerative potential in mice. , 2011, The Journal of clinical investigation.
[143] Hans Clevers,et al. The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. , 2011, Cell stem cell.
[144] P. Robinson,et al. Whole-exome sequencing for finding de novo mutations in sporadic mental retardation , 2010, Genome Biology.
[145] H. Abdi,et al. Principal component analysis , 2010 .
[146] Cole Trapnell,et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. , 2010, Nature biotechnology.
[147] W. Huber,et al. which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .
[148] M. Robinson,et al. A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.
[149] Sandrine Dudoit,et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.
[150] Peter Lindblad,et al. A guide for in-house design of template-switch-based 5' rapid amplification of cDNA ends systems. , 2010, Analytical biochemistry.
[151] Catalin C. Barbacioru,et al. mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.
[152] Lior Pachter,et al. Sequence Analysis , 2020, Definitions.
[153] M. Grompe,et al. Surface markers for the murine oval cell response , 2008, Hepatology.
[154] David E Draper,et al. Effects of osmolytes on RNA secondary and tertiary structure stabilities and RNA-Mg2+ interactions. , 2007, Journal of Molecular Biology.
[155] R. Ivell,et al. A highly efficient method for long-chain cDNA synthesis using trehalose and betaine. , 2002, Analytical biochemistry.
[156] N Sasaki,et al. Thermostabilization and thermoactivation of thermolabile enzymes by trehalose and its application for the synthesis of full length cDNA. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[157] Thomas R. Gingeras,et al. STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..
[158] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[159] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.