A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
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Keegan D. Korthauer | M. Newton | R. Stewart | Li-Fang Chu | J. Thomson | C. Kendziorski | K. Korthauer | Yuan Li
[1] J. H. Ward. Hierarchical Grouping to Optimize an Objective Function , 1963 .
[2] S. MacEachern. Estimating normal means with a conjugate style dirichlet process prior , 1994 .
[3] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[4] S. MacEachern,et al. A semiparametric Bayesian model for randomised block designs , 1996 .
[5] S. MacEachern,et al. Estimating mixture of dirichlet process models , 1998 .
[6] N. Walworth. Cell-cycle checkpoint kinases: checking in on the cell cycle. , 2000, Current opinion in cell biology.
[7] M. Thattai,et al. Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[8] Alex E. Lash,et al. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..
[9] Ertugrul M. Ozbudak,et al. Regulation of noise in the expression of a single gene , 2002, Nature Genetics.
[10] Takumi Miura,et al. Monitoring early differentiation events in human embryonic stem cells by massively parallel signature sequencing and expressed sequence tag scan. , 2004, Stem cells and development.
[11] T. Elston,et al. Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.
[12] M. Barbacid,et al. Mammalian cyclin-dependent kinases. , 2005, Trends in biochemical sciences.
[13] D. Tranchina,et al. Stochastic mRNA Synthesis in Mammalian Cells , 2006, PLoS biology.
[14] S. Dalton,et al. Cell cycle control of embryonic stem cells , 2007, Stem Cell Reviews.
[15] Gary O Zerbe,et al. Permutation‐based adjustments for the significance of partial regression coefficients in microarray data analysis , 2008, Genetic epidemiology.
[16] Li-Fang Chu,et al. Ronin Is Essential for Embryogenesis and the Pluripotency of Mouse Embryonic Stem Cells , 2008, Cell.
[17] T. Tarpey,et al. Model misspecification , 2008, Statistical modelling.
[18] Kevin R. Coombes,et al. The Bimodality Index: A Criterion for Discovering and Ranking Bimodal Signatures from Cancer Gene Expression Profiling Data , 2009, Cancer informatics.
[19] C. Elsik. The pea aphid genome sequence brings theories of insect defense into question , 2010, Genome Biology.
[20] 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 .
[21] Timothy K Lee,et al. Single-cell NF-κB dynamics reveal digital activation and analogue information processing , 2010, Nature.
[22] Catalin C. Barbacioru,et al. Tracing the Derivation of Embryonic Stem Cells from the Inner Cell Mass by Single-Cell RNA-Seq Analysis , 2010, Cell stem cell.
[23] Jeffrey L. Wrana,et al. An Alternative Splicing Switch Regulates Embryonic Stem Cell Pluripotency and Reprogramming , 2011, Cell.
[24] Matthew S. Shotwell,et al. Bayesian Outlier Detection with Dirichlet Process Mixtures , 2011 .
[25] Jennifer M. Bolin,et al. Chemically defined conditions for human iPS cell derivation and culture , 2011, Nature Methods.
[26] Lianming Wang,et al. Fast Bayesian Inference in Dirichlet Process Mixture Models , 2011, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.
[27] Colin N. Dewey,et al. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.
[28] R. Sandberg,et al. Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells , 2012, Nature Biotechnology.
[29] Gyan Bhanot,et al. Single Cell Profiling of Circulating Tumor Cells: Transcriptional Heterogeneity and Diversity from Breast Cancer Cell Lines , 2012, PloS one.
[30] Boris N. Kholodenko,et al. Emergence of bimodal cell population responses from the interplay between analog single-cell signaling and protein expression noise , 2012, BMC Systems Biology.
[31] T. Hashimshony,et al. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. , 2012, Cell reports.
[32] J. Marioni,et al. Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data , 2013, Genome Biology.
[33] M. Lako,et al. A Putative Role for the Immunoproteasome in the Maintenance of Pluripotency in Human Embryonic Stem Cells , 2012, Stem cells.
[34] Boris N. Kholodenko,et al. Bimodal Protein Distributions in Heterogeneous Oscillating Systems , 2012, CMSB.
[35] Adrian E. Raftery,et al. mclust Version 4 for R : Normal Mixture Modeling for Model-Based Clustering , Classification , and Density Estimation , 2012 .
[36] Yoo Jin Jung,et al. The transcriptional landscape and mutational profile of lung adenocarcinoma , 2012, Genome research.
[37] Ning Leng,et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments , 2013, Bioinform..
[38] S. Horvath,et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing , 2013, Nature.
[39] Ruiqiang Li,et al. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells , 2013, Nature Structural &Molecular Biology.
[40] Stefan Van Aelst,et al. Fast and robust bootstrap for multivariate inference: The R package FRB , 2013 .
[41] Pedro G. Ferreira,et al. Transcriptome and genome sequencing uncovers functional variation in humans , 2013, Nature.
[42] Shintaro Katayama,et al. SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization , 2013, Bioinform..
[43] Matthew S. Shotwell,et al. profdpm: An R Package for MAP Estimation in a Class of Conjugate Product Partition Models , 2013 .
[44] Rona S. Gertner,et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.
[45] Momiao Xiong,et al. Canonical correlation analysis for RNA-seq co-expression networks , 2013, Nucleic acids research.
[46] C. Mason,et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data , 2013, Genome Biology.
[47] Michael Q. Zhang,et al. Epigenomic Analysis of Multilineage Differentiation of Human Embryonic Stem Cells , 2013, Cell.
[48] Charity W. Law,et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.
[49] B. Williams,et al. From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing , 2014, Genome research.
[50] P. Kharchenko,et al. Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.
[51] S. Potter,et al. Single cell dissection of early kidney development: multilineage priming , 2014, Development.
[52] Cole Trapnell,et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.
[53] A. Regev,et al. Preparation of Single‐Cell RNA‐Seq Libraries for Next Generation Sequencing , 2014, Current protocols in molecular biology.
[54] J. D. Engel,et al. Developmental transcriptome analysis of human erythropoiesis. , 2014, Human molecular genetics.
[55] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[56] Charles J. Vaske,et al. Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing , 2014, Proceedings of the National Academy of Sciences.
[57] N. Neff,et al. Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.
[58] B. Tjaden,et al. De novo assembly of bacterial transcriptomes from RNA-seq data , 2015, Genome Biology.
[59] Aviv Regev,et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells , 2014, Nature.
[60] B. Kholodenko,et al. Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses , 2014, Journal of The Royal Society Interface.
[61] Michael B. Elowitz,et al. Dynamic Heterogeneity and DNA Methylation in Embryonic Stem Cells , 2014, Molecular cell.
[62] Shawn M. Gillespie,et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma , 2014, Science.
[63] M. Hemberg,et al. Discrete Distributional Differential Expression (D3E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data , 2015, bioRxiv.
[64] 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.
[65] C. David Page,et al. Human pluripotent stem cell-derived neural constructs for predicting neural toxicity , 2015, Proceedings of the National Academy of Sciences.
[66] Ning Leng,et al. Oscope identifies oscillatory genes in unsynchronized single cell RNA-seq experiments , 2015, Nature Methods.
[67] Do-Hyun Nam,et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells , 2015, Genome Biology.
[68] Greg Finak,et al. MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA-seq data , 2015 .
[69] Fabian J Theis,et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.
[70] C. Tyler-Smith,et al. Ancient DNA and the rewriting of human history: be sparing with Occam’s razor , 2016, Genome Biology.
[71] P. McCullagh. Partition models , 2015 .
[72] Aleksandra A. Kolodziejczyk,et al. Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation , 2015, Cell stem cell.
[73] James A. Thomson,et al. scDD: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments , 2016 .
[74] S. Richardson,et al. Beyond comparisons of means: understanding changes in gene expression at the single-cell level , 2016, Genome Biology.
[75] Martin Hemberg,et al. Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data , 2015, BMC Bioinformatics.
[76] R. Stewart,et al. Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm , 2016, Genome Biology.