SnapATAC: A Comprehensive Analysis Package for Single Cell ATAC-seq
暂无分享,去创建一个
Kai Zhang | Bing Ren | Xinzhu Zhou | Rongxin Fang | Sebastian Preissl | Yang Li | Xiaomeng Hou | Jacinta Lucero | Xinxin Wang | Amir Motamedi | Andrew K. Shiau | Fangming Xie | Eran A. Mukamel | Yanxiao Zhang | M. Margarita Behrens | Joseph R. Ecker | B. Ren | J. Ecker | E. Mukamel | B. Ren | J. Lucero | M. Behrens | S. Preissl | Rongxin Fang | Yanxiao Zhang | A. Shiau | Kai Zhang | Xiaomeng Hou | Y. Li | Fangming Xie | Xinxin Wang | Amir Motamedi | Bing Ren | Xinzhu Zhou | Bing-Jie Ren
[1] C. Glass,et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. , 2010, Molecular cell.
[2] Justin P Sandoval,et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex , 2017, Science.
[3] Neva C. Durand,et al. A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping , 2014, Cell.
[4] Cory Y. McLean,et al. GREAT improves functional interpretation of cis-regulatory regions , 2010, Nature Biotechnology.
[5] William J. Greenleaf,et al. chromVAR: Inferring transcription factor-associated accessibility from single-cell epigenomic data , 2017, Nature Methods.
[6] Ameet Talwalkar,et al. Ensemble Nystrom Method , 2009, NIPS.
[7] Bing Ren,et al. Systematic mapping of chromatin state landscapes during mouse development , 2017, bioRxiv.
[8] Christoph Hafemeister,et al. Comprehensive integration of single cell data , 2018, bioRxiv.
[9] Stein Aerts,et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data , 2019, Nature Methods.
[10] Jean-Loup Guillaume,et al. Fast unfolding of communities in large networks , 2008, 0803.0476.
[11] P. Kharchenko,et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain , 2017, Nature Biotechnology.
[12] Hannah A. Pliner,et al. The cis-regulatory dynamics of embryonic development at single cell resolution , 2017, Nature.
[13] Andrew C. Adey,et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing , 2015, Science.
[14] Z Josh Huang,et al. The diversity of GABAergic neurons and neural communication elements , 2019, Nature Reviews Neuroscience.
[15] Ansuman T. Satpathy,et al. Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks , 2018, Cell.
[16] V. Corces,et al. CTCF: Master Weaver of the Genome , 2009, Cell.
[17] Richard A. Muscat,et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding , 2018, Science.
[18] Staci A. Sorensen,et al. Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics , 2016 .
[19] Garreck H. Lenz,et al. Enhancer viruses and a transgenic platform for combinatorial cell subclass-specific labeling , 2019 .
[20] Ameet Talwalkar,et al. Sampling Methods for the Nyström Method , 2012, J. Mach. Learn. Res..
[21] Aviv Regev,et al. BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization , 2018, BMC Bioinformatics.
[22] Howard Y. Chang,et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion , 2019, bioRxiv.
[23] Lee E. Edsall,et al. A map of the cis-regulatory sequences in the mouse genome , 2012, Nature.
[24] Fan Zhang,et al. Fast, sensitive, and accurate integration of single cell data with Harmony , 2018, bioRxiv.
[25] Sandy L. Klemm,et al. High-throughput chromatin accessibility profiling at single-cell resolution , 2018, Nature Communications.
[26] Andrew C. Adey,et al. Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. , 2018, Molecular cell.
[27] K. Tomita,et al. bHLH transcription factors and mammalian neuronal differentiation. , 1997, The international journal of biochemistry & cell biology.
[28] Clifford A. Meyer,et al. Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.
[29] R. Satija,et al. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression , 2019, Genome Biology.
[30] F. Gage,et al. Mechanisms and Functional Implications of Adult Neurogenesis , 2008, Cell.
[31] Lars E. Borm,et al. Molecular Architecture of the Mouse Nervous System , 2018, Cell.
[32] Christoph Hafemeister,et al. Developmental diversification of cortical inhibitory interneurons , 2017, Nature.
[33] Åsa K. Björklund,et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects , 2014, Genome research.
[34] Howard Y. Chang,et al. Single-cell chromatin accessibility reveals principles of regulatory variation , 2015, Nature.
[35] R. Satija,et al. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression , 2019, Genome Biology.
[36] Allon M Klein,et al. Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. , 2019, Cell systems.
[37] Russell B. Fletcher,et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics , 2017, BMC Genomics.
[38] Ian R. Wickersham,et al. The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas , 2017, Neuron.
[39] Howard Y. Chang,et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position , 2013, Nature Methods.
[40] Wei Xie,et al. The landscape of accessible chromatin in mammalian preimplantation embryos , 2016, Nature.
[41] Steven L. Brunton,et al. Diffusion Maps meet Nyström , 2018, ArXiv.
[42] Z. Weng,et al. High-Resolution Mapping and Characterization of Open Chromatin across the Genome , 2008, Cell.
[43] William S. DeWitt,et al. A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility , 2018, Cell.
[44] D. Dickel,et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation , 2018, Nature Neuroscience.
[45] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[46] Miguel A. Andrade-Navarro,et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data , 2019, Genome Biology.
[47] Andrew L. Ferguson,et al. Landmark diffusion maps (L-dMaps): Accelerated manifold learning out-of-sample extension , 2017, Applied and Computational Harmonic Analysis.
[48] David R. Powell,et al. From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis , 2020, Genome Biology.
[49] Inna Dubchak,et al. VISTA Enhancer Browser—a database of tissue-specific human enhancers , 2006, Nucleic Acids Res..
[50] I. Amit,et al. Comprehensive mapping of long range interactions reveals folding principles of the human genome , 2011 .
[51] Garreck H. Lenz,et al. Prospective, brain-wide labeling of neuronal subclasses with enhancer-driven AAVs , 2019, bioRxiv.
[52] Matthew D. Schultz,et al. Global Epigenomic Reconfiguration During Mammalian Brain Development , 2013, Science.
[53] Martin J. Aryee,et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility , 2019, Nature Biotechnology.
[54] R. Andrews,et al. Innate Immune Activity Conditions the Effect of Regulatory Variants upon Monocyte Gene Expression , 2014, Science.