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Times Cited: 657
Times Cited: 657
Times Cited
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Similarity
Dimensionality reduction for visualizing single-cell data using UMAP.
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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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Integrating single-cell transcriptomic data across different conditions, technologies, and species.
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Comprehensive Integration of Single-Cell Data.
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viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia.
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FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.
Sofie Van Gassen, Britt Callebaut, Mary J Van Helden, Bart N Lambrecht, Piet Demeester, Tom Dhaene, Yvan Saeys. Cytometry A 2015
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STAR: ultrafast universal RNA-seq aligner.
Alexander Dobin, Carrie A Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, Thomas R Gingeras. Bioinformatics 2013
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F Alexander Wolf, Philipp Angerer, Fabian J Theis. Genome Biol 2018
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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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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.
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Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.
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Identification of cell types from single-cell transcriptomes using a novel clustering method.
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Automated mapping of phenotype space with single-cell data.
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Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators.
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Challenges in unsupervised clustering of single-cell RNA-seq data.
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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
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Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.
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Massively parallel digital transcriptional profiling of single cells.
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SC3: consensus clustering of single-cell RNA-seq data.
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Spatial reconstruction of single-cell gene expression data.
Rahul Satija, Jeffrey A Farrell, David Gennert, Alexander F Schier, Aviv Regev. Nat Biotechnol 2015
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Deep generative modeling for single-cell transcriptomics.
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Fast, sensitive and accurate integration of single-cell data with Harmony.
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Single-cell RNA sequencing technologies and bioinformatics pipelines.
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limma powers differential expression analyses for RNA-sequencing and microarray studies.
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.
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Robust enumeration of cell subsets from tissue expression profiles.
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Single-cell RNA-seq denoising using a deep count autoencoder.
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Automated identification of stratifying signatures in cellular subpopulations.
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Critical assessment of automated flow cytometry data analysis techniques.
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Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.
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Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.
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Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development.
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Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE.
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Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry.
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pcaReduce: hierarchical clustering of single cell transcriptional profiles.
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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.