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Hongjing Zhang et al.
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Tian Tian et al.
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Seyoung Park et al.
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Jesse M. Zhang et al.
BMC BIOINFORMATICS (2018)
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Xiaoping Han et al.
CELL (2018)
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Jiarui Ding et al.
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Matthew D. Young et al.
SCIENCE (2018)
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Junyue Cao et al.
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Deep generative modeling for single-cell transcriptomics
Romain Lopez et al.
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Sonya A. MacParland et al.
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SCANPY: large-scale single-cell gene expression data analysis
F. Alexander Wolf et al.
GENOME BIOLOGY (2018)
Simultaneous epitope and transcriptome measurement in single cells
Marlon Stoeckius et al.
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Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Bo Wang et al.
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SC3: consensus clustering of single-cell RNA-seq data
Vladimir Yu Kiselev et al.
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Comprehensive single-cell transcriptional profiling of a multicellular organism
Junyue Cao et al.
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Grace X. Y. Zheng et al.
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CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
Peijie Lin et al.
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pcaReduce: hierarchical clustering of single cell transcriptional profiles
Justina Zurauskiene et al.
BMC BIOINFORMATICS (2016)
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Karthik Shekhar et al.
CELL (2016)
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
Zhicheng Ji et al.
NUCLEIC ACIDS RESEARCH (2016)
Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development
Jinmiao Chen et al.
NATURE COMMUNICATIONS (2016)
Identification of cell types from single-cell transcriptomes using a novel clustering method
Chen Xu et al.
BIOINFORMATICS (2015)
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Jacob H. Levine et al.
CELL (2015)
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko et al.
CELL (2015)
The Technology and Biology of Single-Cell RNA Sequencing
Aleksandra A. Kolodziejczyk et al.
MOLECULAR CELL (2015)
Spatial reconstruction of single-cell gene expression data
Rahul Satija et al.
NATURE BIOTECHNOLOGY (2015)
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
Emma Pierson et al.
GENOME BIOLOGY (2015)
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Michael I. Love et al.
GENOME BIOLOGY (2014)
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Gabriela Bindea et al.
IMMUNITY (2013)
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Ehud Shapiro et al.
NATURE REVIEWS GENETICS (2013)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
Normalized cuts and image segmentation
JB Shi et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2000)
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