Dataset Information

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

ABSTRACT: Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches relying on manual "gating." Clustering cells based on phenotypic similarity comes at a loss of single-cell resolution and often the number of subpopulations is unknown a priori. Here we describe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning, and displays multivariate cellular phenotypes on a 2D plot. We apply ACCENSE to 35-parameter mass cytometry data from CD8(+) T cells derived from specific pathogen-free and germ-free mice, and stratify cells into phenotypic subpopulations. Our results show significant heterogeneity within the known CD8(+) T-cell subpopulations, and of particular note is that we find a large novel subpopulation in both specific pathogen-free and germ-free mice that has not been described previously. This subpopulation possesses a phenotypic signature that is distinct from conventional naive and memory subpopulations when analyzed by ACCENSE, but is not distinguishable on a biaxial plot of standard markers. We are able to automatically identify cellular subpopulations based on all proteins analyzed, thus aiding the full utilization of powerful new single-cell technologies such as mass cytometry.

SUBMITTER: Shekhar K

PROVIDER: S-EPMC3890841 | biostudies-other | 2014 Jan

REPOSITORIES: biostudies-other

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Publications

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

Shekhar Karthik K Brodin Petter P Davis Mark M MM Chakraborty Arup K AK

Proceedings of the National Academy of Sciences of the United States of America 20131216 1

Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches relying on manual "gating." Clustering cells based on phenotypic similarity comes at a loss of single-cell resolution and often the number of subpopulations is unknown a priori. Here we describe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning, and display ...[more]

PMID: 24344260

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Dataset Information

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

Publications

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

Similar Datasets

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