ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis
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Howard Y. Chang | William J. Greenleaf | Jeffrey M. Granja | M. Ryan Corces | Sarah E. Pierce | S. Tansu Bagdatli | Hani Choudhry | Sarah E. Pierce | W. Greenleaf | M. Corces | S. Bagdatli | H. Choudhry | Jeffrey M. Granja
[1] Miguel A. Andrade-Navarro,et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data , 2019, Genome Biology.
[2] Leland McInnes,et al. UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..
[3] Howard Y. Chang,et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion , 2019, bioRxiv.
[4] Andrew C. Adey,et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition , 2010, Genome Biology.
[5] Nathan C. Sheffield,et al. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor , 2015, Bioinform..
[6] Kai Zhang,et al. SnapATAC: A Comprehensive Analysis Package for Single Cell ATAC-seq , 2019, bioRxiv.
[7] 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.
[8] William S. DeWitt,et al. A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility , 2018, Cell.
[9] Raphael Gottardo,et al. Orchestrating high-throughput genomic analysis with Bioconductor , 2015, Nature Methods.
[10] Mauro A. A. Castro,et al. The chromatin accessibility landscape of primary human cancers , 2018, Science.
[11] Howard Y. Chang,et al. Lineage-specific and single cell chromatin accessibility charts human hematopoiesis and leukemia evolution , 2016, Nature Genetics.
[12] Christoph Hafemeister,et al. Comprehensive integration of single cell data , 2018, bioRxiv.
[13] William J. Greenleaf,et al. chromVAR: Inferring transcription factor-associated accessibility from single-cell epigenomic data , 2017, Nature Methods.
[14] Hannah A. Pliner,et al. The cis-regulatory dynamics of embryonic development at single cell resolution , 2017, Nature.
[15] B. Ren,et al. Fast and Accurate Clustering of Single Cell Epigenomes Reveals Cis-Regulatory Elements in Rare Cell Types , 2019 .
[16] Howard Y. Chang,et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements , 2017, Nature Genetics.
[17] Chun Jimmie Ye,et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation , 2017, Nature Biotechnology.
[18] Allon M Klein,et al. Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. , 2019, Cell systems.
[19] Ido Goldstein,et al. Bivariate Genomic Footprinting Detects Changes in Transcription Factor Activity. , 2017, Cell reports.
[20] Leland McInnes,et al. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.
[21] Howard Y. Chang,et al. Transcript-indexed ATAC-seq for precision immune profiling , 2018, Nature Medicine.
[22] D. Arnosti. Analysis and function of transcriptional regulatory elements: insights from Drosophila. , 2003, Annual review of entomology.
[23] Andrew C. Adey,et al. Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. , 2018, Molecular cell.
[24] Steve Mao,et al. Cancer chromatin accessibility landscape , 2018, Science.
[25] Martin J. Aryee,et al. Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation , 2018, Cell.
[26] Nicholas A. Sinnott-Armstrong,et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues , 2017, Nature Methods.
[27] Howard Y. Chang,et al. Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease , 2020, bioRxiv.
[28] Fan Zhang,et al. Fast, sensitive, and accurate integration of single cell data with Harmony , 2018, bioRxiv.
[29] Howard Y. Chang,et al. Single-cell chromatin accessibility reveals principles of regulatory variation , 2015, Nature.
[30] Kamil Slowikowski,et al. Fast, sensitive, and accurate integration of single cell data with Harmony , 2019, Nature Methods.
[31] Christopher S. McGinnis,et al. DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors , 2018, bioRxiv.
[32] Howard Y. Chang,et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia , 2019, Nature Biotechnology.
[33] David A. Knowles,et al. Landscape of stimulation-responsive chromatin across diverse human immune cells , 2018, Nature Genetics.
[34] A. Sandelin,et al. Determinants of enhancer and promoter activities of regulatory elements , 2019, Nature Reviews Genetics.
[35] Nathan C. Sheffield,et al. The accessible chromatin landscape of the human genome , 2012, Nature.
[36] Han Xu,et al. Analysis of optimized DNase-seq reveals intrinsic bias in transcription factor footprint identification , 2013, Nature methods.
[37] Fabian J Theis,et al. The Human Cell Atlas , 2017, bioRxiv.
[38] David A. Knowles,et al. Landscape of stimulation-responsive chromatin across diverse human immune cells , 2018, Nature Genetics.
[39] Martin J. Aryee,et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility , 2019, Nature Biotechnology.
[40] Andrew C. Adey,et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing , 2015, Science.
[41] Kevin R. Moon,et al. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion , 2018, Cell.