Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database
暂无分享,去创建一个
[1] Hannah A. Pliner,et al. Reversed graph embedding resolves complex single-cell trajectories , 2017, Nature Methods.
[2] Yin Hu,et al. Robust detection of alternative splicing in a population of single cells , 2016, Nucleic acids research.
[3] A. Regev,et al. Spatial reconstruction of single-cell gene expression data , 2015 .
[4] P. Kharchenko,et al. Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.
[5] Catalin C. Barbacioru,et al. mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.
[6] Matthew E. Ritchie,et al. limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.
[7] Nancy R. Zhang,et al. SCALE: modeling allele-specific gene expression by single-cell RNA sequencing , 2017, Genome Biology.
[8] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[9] S. Linnarsson,et al. Counting absolute numbers of molecules using unique molecular identifiers , 2011, Nature Methods.
[10] Andrew J. Hill,et al. Single-cell mRNA quantification and differential analysis with Census , 2017, Nature Methods.
[11] S. Linnarsson,et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.
[12] Shintaro Katayama,et al. SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization , 2013, Bioinform..
[13] Luyi Tian,et al. scPipe: a flexible data preprocessing pipeline for single-cell RNA-sequencing data , 2017, bioRxiv.
[14] Keegan D. Korthauer,et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments , 2016, Genome Biology.
[15] Hadley Wickham,et al. ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .
[16] Philip E. Bourne,et al. Ten simple rules to consider regarding preprint submission , 2017, PLoS Comput. Biol..
[17] Fabian J Theis,et al. Diffusion pseudotime robustly reconstructs lineage branching , 2016, Nature Methods.
[18] Davis J. McCarthy,et al. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation , 2012, Nucleic acids research.
[19] Fabian J Theis,et al. The Human Cell Atlas , 2017, bioRxiv.
[20] Rhonda Bacher,et al. Design and computational analysis of single-cell RNA-sequencing experiments , 2016, Genome Biology.
[21] Cole Trapnell,et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.
[22] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[23] Sarah A. Teichmann,et al. Single-cell insights into transcriptomic diversity in immunity , 2017 .
[24] Aaron Diaz,et al. SCell: integrated analysis of single-cell RNA-seq data , 2016, Bioinform..
[25] Scott Chamberlain,et al. Create Interactive Web Graphics via Plotly's JavaScript GraphingLibrary , 2015 .
[26] Valentine Svensson,et al. Power Analysis of Single Cell RNA-Sequencing Experiments , 2016, Nature Methods.
[27] S. Teichmann,et al. Exponential scaling of single-cell RNA-seq in the past decade , 2017, Nature Protocols.
[28] Aaron T. L. Lun,et al. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R , 2017, Bioinform..
[29] Paul Hoffman,et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.
[30] Scott Chamberlain,et al. Create Interactive Web Graphics via 'plotly.js' [R package plotly version 4.9.2.1] , 2020 .
[31] Travers Ching,et al. Single-Cell Transcriptomics Bioinformatics and Computational Challenges , 2016, Front. Genet..
[32] Viktor Petukhov,et al. Accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments , 2017 .
[33] Raphael Gottardo,et al. Orchestrating high-throughput genomic analysis with Bioconductor , 2015, Nature Methods.
[34] Maria K. Jaakkola,et al. Comparison of methods to detect differentially expressed genes between single-cell populations , 2016, Briefings Bioinform..
[35] Mauricio Barahona,et al. SC3 - consensus clustering of single-cell RNA-Seq data , 2016, Nature Methods.
[36] Aly A. Khan,et al. BASIC: BCR assembly from single cells , 2016, Bioinform..
[37] P. Klenerman,et al. Targeted reconstruction of T cell receptor sequence from single cell RNA-seq links CDR3 length to T cell differentiation state , 2017, Nucleic acids research.
[38] Gene W. Yeo,et al. Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dynamics during Neuron Differentiation. , 2017, Molecular cell.
[39] Xun Zhu,et al. Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Tumor Subpopulations and Genotype-phenotype Linkage , 2016 .
[40] Charlotte Soneson,et al. Bias, robustness and scalability in single-cell differential expression analysis , 2018, Nature Methods.
[41] G. Sanguinetti,et al. BRIE: transcriptome-wide splicing quantification in single cells , 2017, Genome Biology.
[42] A. Regev,et al. Revealing the vectors of cellular identity with single-cell genomics , 2016, Nature Biotechnology.
[43] Chengchen Zhao,et al. Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data , 2017, bioRxiv.
[44] Sarah A. Teichmann,et al. Computational approaches for interpreting scRNA‐seq data , 2017, FEBS letters.
[45] Barbara Di Camillo,et al. Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods , 2017, Front. Genet..
[46] Christoph Ziegenhain,et al. powsimR: Power analysis for bulk and single cell RNA-seq experiments , 2017, bioRxiv.
[47] A. Oshlack,et al. Splatter: simulation of single-cell RNA sequencing data , 2017, Genome Biology.
[48] Anneliese O. Speak,et al. T cell fate and clonality inference from single cell transcriptomes , 2016, Nature Methods.
[49] Karthik Ram,et al. Interface to the arXiv API , 2015 .
[50] Xun Zhu,et al. Using Single Nucleotide Variations in Cancer Single-Cell RNA-Seq Data for Subpopulation Identification and Genotype-phenotype Linkage Analysis , 2016 .
[51] Xuegong Zhang,et al. Differential expression analyses for single-cell RNA-Seq: old questions on new data , 2016, Quantitative Biology.
[52] M. Schaub,et al. SC3 - consensus clustering of single-cell RNA-Seq data , 2016, Nature Methods.
[53] Shawn M. Gillespie,et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma , 2014, Science.
[54] C. Wilke. Streamlined Plot Theme and Plot Annotations for 'ggplot2' , 2015 .
[55] Sean C. Bendall,et al. Wishbone identifies bifurcating developmental trajectories from single-cell data , 2016, Nature Biotechnology.
[56] S. Teichmann,et al. Computational and analytical challenges in single-cell transcriptomics , 2015, Nature Reviews Genetics.
[57] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[58] Scott Chamberlain,et al. Client for Various 'CrossRef' 'APIs' , 2016 .
[59] Viktor Petukhov,et al. dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments , 2018, Genome Biology.
[60] Fabian J Theis,et al. SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.
[61] A. Heger,et al. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy , 2016, bioRxiv.
[62] Christoph Ziegenhain,et al. zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs , 2017, bioRxiv.