An accurate and robust imputation method scImpute for single-cell RNA-seq data
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[1] P. Kharchenko,et al. Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.
[2] A. Regev,et al. Spatial reconstruction of single-cell gene expression , 2015, Nature Biotechnology.
[3] Howard Y. Chang,et al. A molecular signature for purified definitive endoderm guides differentiation and isolation of endoderm from mouse and human embryonic stem cells. , 2012, Stem cells and development.
[4] Shila Ghazanfar,et al. Integrated single cell data analysis reveals cell specific networks and novel coactivation markers , 2016, BMC Systems Biology.
[5] Matthias Hein,et al. Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization , 2012, 1205.0953.
[6] A. Saliba,et al. Single-cell RNA-seq: advances and future challenges , 2014, Nucleic acids research.
[7] Catalina A. Vallejos,et al. BASiCS: Bayesian Analysis of Single-Cell Sequencing Data , 2015, PLoS Comput. Biol..
[8] Valentine Svensson,et al. Power Analysis of Single Cell RNA-Sequencing Experiments , 2016, Nature Methods.
[9] Israel Steinfeld,et al. BMC Bioinformatics BioMed Central , 2008 .
[10] Cole Trapnell,et al. Single-cell transcriptome sequencing: recent advances and remaining challenges , 2016, F1000Research.
[11] Andrew McDavid,et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments , 2012, Bioinform..
[12] Thomas Bieber,et al. Evidence for a differential expression of the Fc ε RI γ chain in dendritic cells of atopic and nonatopic donors , 2003 .
[13] M. Gerstein,et al. RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.
[14] O. Cotoi,et al. S100A8 and S100A9: DAMPs at the Crossroads between Innate Immunity, Traditional Risk Factors, and Cardiovascular Disease , 2013, Mediators of inflammation.
[15] Pei-Rong Wang,et al. Targeting SOX17 in human embryonic stem cells creates unique strategies for isolating and analyzing developing endoderm. , 2011, Cell stem cell.
[16] Judith A. Blake,et al. Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse , 2016, Nucleic Acids Res..
[17] 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.
[18] Allon M. Klein,et al. Single-cell barcoding and sequencing using droplet microfluidics , 2016, Nature Protocols.
[19] Aleksandra A. Kolodziejczyk,et al. The technology and biology of single-cell RNA sequencing. , 2015, Molecular cell.
[20] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[21] M. Newton,et al. SCnorm: robust normalization of single-cell RNA-seq data , 2017, Nature Methods.
[22] E. Pierson,et al. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis , 2015, Genome Biology.
[23] Joshua W. K. Ho,et al. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data , 2016, Genome Biology.
[24] M. Salit,et al. Synthetic Spike-in Standards for Rna-seq Experiments Material Supplemental Open Access License Commons Creative , 2022 .
[25] Jingshu Wang,et al. Gene expression recovery for single cell RNA sequencing , 2017, bioRxiv.
[26] M. Cugmas,et al. On comparing partitions , 2015 .
[27] Allon M. Klein,et al. Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.
[28] Chen Xu,et al. Identification of cell types from single-cell transcriptomes using a novel clustering method , 2015, Bioinform..
[29] G. W. Milligan,et al. A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis. , 1986, Multivariate behavioral research.
[30] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[31] Greg Finak,et al. The contribution of cell cycle to heterogeneity in single-cell RNA-seq data , 2016, Nature Biotechnology.
[32] 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.
[33] Grace X. Y. Zheng,et al. Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.
[34] R. Stewart,et al. Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm , 2016, Genome Biology.
[35] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[36] S. Linnarsson,et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.
[37] Kevin R. Moon,et al. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data , 2017, bioRxiv.
[38] R. Sandberg,et al. Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells , 2014, Science.
[39] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.