Gökcen Eraslan, Lukas M Simon, Maria Mircea, Nikola S Mueller, Fabian J Theis. Nat Commun 2019
Times Cited: 236
Times Cited: 236
Times Cited
Times Co-cited
Similarity
Deep generative modeling for single-cell transcriptomics.
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael I Jordan, Nir Yosef. Nat Methods 2018
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael I Jordan, Nir Yosef. Nat Methods 2018
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An accurate and robust imputation method scImpute for single-cell RNA-seq data.
Wei Vivian Li, Jingyi Jessica Li. Nat Commun 2018
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Massively parallel digital transcriptional profiling of single cells.
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Spatial reconstruction of single-cell gene expression data.
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Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.
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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.
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Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.
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Interpretable dimensionality reduction of single cell transcriptome data with deep generative models.
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Dongfang Wang, Jin Gu. Genomics Proteomics Bioinformatics 2018
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Challenges in unsupervised clustering of single-cell RNA-seq data.
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A general and flexible method for signal extraction from single-cell RNA-seq data.
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Davide Risso, Fanny Perraudeau, Svetlana Gribkova, Sandrine Dudoit, Jean-Philippe Vert. Nat Commun 2018
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Emma Pierson, Christopher Yau. Genome Biol 2015
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DrImpute: imputing dropout events in single cell RNA sequencing data.
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Wuming Gong, Il-Youp Kwak, Pruthvi Pota, Naoko Koyano-Nakagawa, Daniel J Garry. BMC Bioinformatics 2018
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Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.
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A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
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Dimensionality reduction for visualizing single-cell data using UMAP.
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.
Cole Trapnell, Davide Cacchiarelli, Jonna Grimsby, Prapti Pokharel, Shuqiang Li, Michael Morse, Niall J Lennon, Kenneth J Livak, Tarjei S Mikkelsen, John L Rinn. Nat Biotechnol 2014
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Comparative Analysis of Single-Cell RNA Sequencing Methods.
Christoph Ziegenhain, Beate Vieth, Swati Parekh, Björn Reinius, Amy Guillaumet-Adkins, Martha Smets, Heinrich Leonhardt, Holger Heyn, Ines Hellmann, Wolfgang Enard. Mol Cell 2017
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
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Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity.
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Fast, sensitive and accurate integration of single-cell data with Harmony.
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Simultaneous epitope and transcriptome measurement in single cells.
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Current best practices in single-cell RNA-seq analysis: a tutorial.
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Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.
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VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies.
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A comparison of single-cell trajectory inference methods.
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Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.
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The Human Cell Atlas.
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scIGANs: single-cell RNA-seq imputation using generative adversarial networks.
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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.