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Times Cited: 766
Times Cited: 766
Times Cited
Times Co-cited
Similarity
Comprehensive Integration of Single-Cell Data.
Tim Stuart, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M Mauck, Yuhan Hao, Marlon Stoeckius, Peter Smibert, Rahul Satija. Cell 2019
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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
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
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Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.
Christoph Hafemeister, Rahul Satija. Genome Biol 2019
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.
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Mark D Robinson, Davis J McCarthy, Gordon K Smyth. Bioinformatics 2010
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Dimensionality reduction for visualizing single-cell data using UMAP.
Etienne Becht, Leland McInnes, John Healy, Charles-Antoine Dutertre, Immanuel W H Kwok, Lai Guan Ng, Florent Ginhoux, Evan W Newell. Nat Biotechnol 2018
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Fast, sensitive and accurate integration of single-cell data with Harmony.
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SCANPY: large-scale single-cell gene expression data analysis.
F Alexander Wolf, Philipp Angerer, Fabian J Theis. Genome Biol 2018
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Massively parallel digital transcriptional profiling of single cells.
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Integrated analysis of multimodal single-cell data.
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Spatial reconstruction of single-cell gene expression data.
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The single-cell transcriptional landscape of mammalian organogenesis.
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Reversed graph embedding resolves complex single-cell trajectories.
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Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.
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Simultaneous epitope and transcriptome measurement in single cells.
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SCENIC: single-cell regulatory network inference and clustering.
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Bias, robustness and scalability in single-cell differential expression analysis.
Charlotte Soneson, Mark D Robinson. Nat Methods 2018
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DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors.
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A scaling normalization method for differential expression analysis of RNA-seq data.
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Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.
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A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease.
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Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.
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STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.
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RNA velocity of single cells.
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Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity.
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Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.
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Deep generative modeling for single-cell transcriptomics.
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Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.
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Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.
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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.