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      KCI등재 SCOPUS SCIE

      Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing

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      https://www.riss.kr/link?id=A106096730

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      다국어 초록 (Multilingual Abstract)

      Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.
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      Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically r...

      Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.

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      참고문헌 (Reference)

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      45 Budnik, B., "SCoPEMS : mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation" 19 : 161-, 2018

      46 Aibar, S., "SCENIC : single-cell regulatory network inference and clustering" 14 : 1083-1086, 2017

      47 Kiselev, V. Y., "SC3 : consensus clustering of single-cell RNA-seq data" 14 : 483-486, 2017

      48 Huang, M., "SAVER : gene expression recovery for single-cell RNA sequencing" 15 : 539-542, 2018

      49 Qiu, X., "Reversed graph embedding resolves complex single-cell trajectories" 14 : 979-982, 2017

      50 Wagner, A., "Revealing the vectors of cellular identity with single-cell genomics" 34 : 1145-1160, 2016

      51 Aran, D., "Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage" 20 : 163-172, 2019

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      67 Raj, A., "Nature, nurture, or chance : stochastic gene expression and its consequences" 135 : 216-226, 2008

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      69 Kang, H. M., "Multiplexed droplet single-cell RNA-sequencing using natural genetic variation" 36 : 89-94, 2018

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      71 Jaitin, D. A., "Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types" 343 : 776-779, 2014

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      78 Finak, G., "MAST : a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data" 16 : 278-, 2015

      79 Cao, J., "Joint profiling of chromatin accessibility and gene expression in thousands of single cells" 361 : 1380-1385, 2018

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      83 Kim, J. K., "Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data" 14 : R7-, 2013

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      92 Ramskold, D., "Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells" 30 : 777-782, 2012

      93 Chen, X., "From tissues to cell types and back : single-cell gene expression analysis of tissue architecture" 1 : 29-51, 2018

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      98 Klein, A. M., "Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells" 161 : 1187-1201, 2015

      99 Lun, A., "Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data"

      100 Jaitin, D. A., "Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq" 167 : 1883-1896, 2016

      101 Treutlein, B., "Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq" 534 : 391-395, 2016

      102 Becht, E., "Dimensionality reduction for visualizing single-cell data using UMAP" 37 : 38-44, 2018

      103 Haghverdi, L., "Diffusion pseudotime robustly reconstructs lineage branching" 13 : 845-848, 2016

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      105 Grun, D., "De novo prediction of stem cell identity using singlecell transcriptome data" 19 : 266-277, 2016

      106 Cannoodt, R., "Computational methods for trajectory inference from single-cell transcriptomics" 46 : 2496-2506, 2016

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      108 Cao, J., "Comprehensive single-cell transcriptional profiling of a multicellular organism" 357 : 661-667, 2017

      109 Ilicic, T., "Classification of low quality cells from single-cell RNA-seq data" 17 : 29-, 2016

      110 Kim, J. K., "Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression" 6 : 8687-, 2015

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      118 Haghverdi, L., "Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors" 36 : 421-427, 2018

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      120 Buttner, M., "Assessment of batch-correction methods for scRNA-seq data with a new test metric"

      121 Li, W. V., "An accurate and robust imputation method scImpute for single-cell RNA-seq data" 9 : 997-, 2018

      122 Brennecke, P., "Accounting for technical noise in single-cell RNA-seq experiments" 10 : 1093-1095, 2013

      123 Alavi, A., "A web server for comparative analysis of single-cell RNA-seq data" 9 : 4768-, 2018

      124 Duo, A., "A systematic performance evaluation of clustering methods for single-cell RNA-seq data" 7 : 1141-, 2018

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      126 Robinson, M. D., "A scaling normalization method for differential expression analysis of RNA-seq data" 11 : R25-, 2010

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