Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling

Learning cell identity from single-cell data presently relies on human experts. Here, we present Marker Enrichment Modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human and machine-readable text label. MEM outperformed tradit...

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Detalles Bibliográficos
Autores principales: Diggins, K. E., Greenplate, A. R., Leelatian, N., Wogsland, C. E., Irish, J. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5330853/
https://www.ncbi.nlm.nih.gov/pubmed/28135256
http://dx.doi.org/10.1038/nmeth.4149
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author Diggins, K. E.
Greenplate, A. R.
Leelatian, N.
Wogsland, C. E.
Irish, J. M.
author_facet Diggins, K. E.
Greenplate, A. R.
Leelatian, N.
Wogsland, C. E.
Irish, J. M.
author_sort Diggins, K. E.
collection PubMed
description Learning cell identity from single-cell data presently relies on human experts. Here, we present Marker Enrichment Modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human and machine-readable text label. MEM outperformed traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.
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spelling pubmed-53308532017-07-30 Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling Diggins, K. E. Greenplate, A. R. Leelatian, N. Wogsland, C. E. Irish, J. M. Nat Methods Article Learning cell identity from single-cell data presently relies on human experts. Here, we present Marker Enrichment Modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human and machine-readable text label. MEM outperformed traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues. 2017-01-30 2017-03 /pmc/articles/PMC5330853/ /pubmed/28135256 http://dx.doi.org/10.1038/nmeth.4149 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Diggins, K. E.
Greenplate, A. R.
Leelatian, N.
Wogsland, C. E.
Irish, J. M.
Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling
title Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling
title_full Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling
title_fullStr Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling
title_full_unstemmed Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling
title_short Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling
title_sort characterizing cell subsets in heterogeneous tissues using marker enrichment modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5330853/
https://www.ncbi.nlm.nih.gov/pubmed/28135256
http://dx.doi.org/10.1038/nmeth.4149
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