DrImpute: imputing dropout events in single cell RNA sequencing data
暂无分享,去创建一个
Il-Youp Kwak | Wuming Gong | Naoko Koyano-Nakagawa | Daniel J. Garry | Pruthvi Pota | Il-Youp Kwak | W. Gong | D. Garry | N. Koyano-Nakagawa | Pruthvi Pota
[1] Russell B. Fletcher,et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics , 2017, BMC Genomics.
[2] Peter Goos,et al. Sequential imputation for missing values , 2007, Comput. Biol. Chem..
[3] L. McMillan,et al. A Fast Approximation to Multidimensional Scaling , 2006 .
[4] Ambrose J. Carr,et al. Bayesian Inference for Single-cell Clustering and Imputing , 2017 .
[5] Russ B. Altman,et al. Missing value estimation methods for DNA microarrays , 2001, Bioinform..
[6] E. Shapiro,et al. Single-cell sequencing-based technologies will revolutionize whole-organism science , 2013, Nature Reviews Genetics.
[7] Tero Aittokallio,et al. Improving missing value estimation in microarray data with gene ontology , 2006, Bioinform..
[8] S. Dudoit,et al. A general and flexible method for signal extraction from single-cell RNA-seq data , 2018, Nature Communications.
[9] D. Sculley,et al. Web-scale k-means clustering , 2010, WWW '10.
[10] S. Linnarsson,et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. , 2011, Genome research.
[11] S. Teichmann,et al. Computational and analytical challenges in single-cell transcriptomics , 2015, Nature Reviews Genetics.
[12] Gene H. Golub,et al. Missing value estimation for DNA microarray gene expression data: local least squares imputation , 2005, Bioinform..
[13] Grace X. Y. Zheng,et al. Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.
[14] Joshua W. K. Ho,et al. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data , 2016, Genome Biology.
[15] Mauricio Barahona,et al. SC3 - consensus clustering of single-cell RNA-Seq data , 2016, Nature Methods.
[16] Nir Yosef,et al. A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes , 2017, ArXiv.
[17] Aleksandra A. Kolodziejczyk,et al. The technology and biology of single-cell RNA sequencing. , 2015, Molecular cell.
[18] 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.
[19] A. Regev,et al. Revealing the vectors of cellular identity with single-cell genomics , 2016, Nature Biotechnology.
[20] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[21] Sandhya Prabhakaran,et al. Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data , 2016, ICML.
[22] Hongkai Ji,et al. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis , 2016, Nucleic acids research.
[23] R. Doerge,et al. Statistical Design and Analysis of RNA Sequencing Data , 2010, Genetics.
[24] M. Schaub,et al. SC3 - consensus clustering of single-cell RNA-Seq data , 2016, Nature Methods.
[25] Nicola K. Wilson,et al. Resolving Early Mesoderm Diversification through Single Cell Expression Profiling , 2016, Nature.
[26] Lorenz Wernisch,et al. Pseudotime estimation: deconfounding single cell time series , 2015, bioRxiv.
[27] Wuming Gong,et al. Dpath software reveals hierarchical haemato-endothelial lineages of Etv2 progenitors based on single-cell transcriptome analysis , 2017, Nature Communications.
[28] Jun Li,et al. LEAP: constructing gene co‐expression networks for single‐cell RNA‐sequencing data using pseudotime ordering , 2016, Bioinform..
[29] Ming Ouyang,et al. Gaussian mixture clustering and imputation of microarray data , 2004, Bioinform..
[30] Rhonda Bacher,et al. Design and computational analysis of single-cell RNA-sequencing experiments , 2016, Genome Biology.
[31] P. Kharchenko,et al. Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.
[32] Alex A. Pollen,et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex , 2014, Nature Biotechnology.
[33] S. Linnarsson,et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing , 2014, Nature Neuroscience.
[34] Catalin C. Barbacioru,et al. mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.
[35] Fabian J Theis,et al. Diffusion pseudotime robustly reconstructs lineage branching , 2016, Nature Methods.
[36] Christopher Yau,et al. pcaReduce: hierarchical clustering of single cell transcriptional profiles , 2015, BMC Bioinformatics.
[37] Mikael Huss,et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. , 2010, Developmental cell.
[38] Kevin R. Moon,et al. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data , 2017, bioRxiv.
[39] Cole Trapnell,et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.
[40] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[41] N. Neff,et al. Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq , 2016, Nature.
[42] Alvaro Plaza Reyes,et al. Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos , 2016, Cell.
[43] Kay Elder,et al. Defining the three cell lineages of the human blastocyst by single-cell RNA-seq , 2015, Development.
[44] Bo Wang,et al. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning , 2016, Nature Methods.
[45] B. Williams,et al. From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing , 2014, Genome research.
[46] R. Sandberg,et al. Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells , 2014, Science.
[47] Wei Vivian Li,et al. An accurate and robust imputation method scImpute for single-cell RNA-seq data , 2018, Nature Communications.
[48] E. Pierson,et al. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis , 2015, Genome Biology.
[49] Allon M. Klein,et al. Single-Cell Analysis of Experience-Dependent Transcriptomic States in Mouse Visual Cortex , 2017, Nature Neuroscience.