Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

By Karthik Shekhar, Petter Brodin, Mark M. Davis, Arup K. Chakraborty. 2013

223 citations recorded by the source corpus.

Subfields: Biophysics, Immunology, Molecular Biology

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Citations in the corpus

Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets (2015, background): “Singlecell analysis at this scale enabled characterization of cell-cycle gene expression without chemical synchronization and at high temporal resolution.”
Approximated and User Steerable tSNE for Progressive Visual Analytics (2015, methodology): “Density-based visualization of the tSNE embedding has been used in several works [5], [6], [9], however, they employ slow-to-compute offline techniques.”
Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data (2015, methodology): “Nonlinear dimensionality reduction methods such as t-SNE (15) have also been applied in the context of flow and mass cytometry (23,24).”
Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline (2016, methodology): “DensVM (Density-based clustering aided by support Vector Machine) is an extension of ACCENSE’s density-based clustering algorithm [15].”
Mass cytometry of follicular lymphoma tumors reveals intrinsic heterogeneity in proteins including HLA-DR and a deficit in non-malignant plasmablast and germinal center B cell populations (2017, background): “5) to cluster cells into cell subsets (16,24,28).”
Mapping the Diversity of Follicular Helper T Cells in Human Blood and Tonsils Using High-Dimensional Mass Cytometry Analysis. (2015, methodology): “We have recently used a modified form of ACCENSE, DensVM, to analyze the mouse myeloid system (Becher et al., 2014).”
Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “A related alternative, ACCENSE 110 , down-samples the data in a density-dependent manner (as in SPADE 96 , described below), uses t-SNE to embed only the remaining cells, and finds clusters, by applying a kernel density transformation to the t-SNE map and seeking the local maxima that represent…”
Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (2016, methodology): “One such method, called t -distributed stochastic neighbor embedding ( t -SNE) (9), performs exceptionally well on mass cytometry data (10, 12, 14). t -SNE performs pair-wise comparisons of all events and maps them in a low dimensional space, optimally arranging similar events nearby and dissimilar…”
Computational flow cytometry: helping to make sense of high-dimensional immunology data (2016, methodology): “This approach is used by ACCENSE 65 and DensVM 66 .”
Cytomegalovirus Infection Drives Adaptive Epigenetic Diversification of NK Cells with Altered Signaling and Effector Function (2015, unknown): “Novel methods based on t-distributed stochastic neighbor embedding (t-SNE), including ViSNE and ACCENSE (Amir el et al., 2013; Shekhar et al., 2014), have proven useful in truthfully representing multidimensional flow cytometry data.”