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Times Cited: 353
Times Cited: 353
Times Cited
Times Co-cited
Similarity
Simultaneous epitope and transcriptome measurement in single cells.
Marlon Stoeckius, Christoph Hafemeister, William Stephenson, Brian Houck-Loomis, Pratip K Chattopadhyay, Harold Swerdlow, Rahul Satija, Peter Smibert. Nat Methods 2017
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Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.
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Comprehensive Integration of Single-Cell Data.
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Massively parallel digital transcriptional profiling of single cells.
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G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.
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Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.
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Integrating single-cell transcriptomic data across different conditions, technologies, and species.
Andrew Butler, Paul Hoffman, Peter Smibert, Efthymia Papalexi, Rahul Satija. Nat Biotechnol 2018
Andrew Butler, Paul Hoffman, Peter Smibert, Efthymia Papalexi, Rahul Satija. Nat Biotechnol 2018
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Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.
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Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.
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mRNA-Seq whole-transcriptome analysis of a single cell.
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High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell.
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Single-cell chromatin accessibility reveals principles of regulatory variation.
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Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.
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RNA velocity of single cells.
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Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells.
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Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics.
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Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH.
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scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells.
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Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.
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Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity.
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Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.
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Integrated genome and transcriptome sequencing of the same cell.
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Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state.
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Single-cell RNA sequencing technologies and bioinformatics pipelines.
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.
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RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.
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Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry.
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Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer.
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Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.
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SCANPY: large-scale single-cell gene expression data analysis.
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SCENIC: single-cell regulatory network inference and clustering.
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Fast, sensitive and accurate integration of single-cell data with Harmony.
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Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging.
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Highly multiplexed simultaneous detection of RNAs and proteins in single cells.
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Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.
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Comparative Analysis of Single-Cell RNA Sequencing Methods.
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Smart-seq2 for sensitive full-length transcriptome profiling in single cells.
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Tumour evolution inferred by single-cell sequencing.
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Spatial reconstruction of single-cell gene expression data.
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Three-dimensional intact-tissue sequencing of single-cell transcriptional states.
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Comprehensive single-cell transcriptional profiling of a multicellular organism.
Junyue Cao, Jonathan S Packer, Vijay Ramani, Darren A Cusanovich, Chau Huynh, Riza Daza, Xiaojie Qiu, Choli Lee, Scott N Furlan, Frank J Steemers,[...]. Science 2017
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Eleven grand challenges in single-cell data science.
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Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.