Characterizing cell subsets using marker enrichment modeling
Marker enrichment modeling (MEM) provides an objective metric for characterizing cell populations from high-content single-cell analysis. The MEM score outperforms standard metrics and provides a machine-readeable label for cell subsets. Learning cell identity from high-content single-cell data pres...
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| Vydáno v: | Nature methods Ročník 14; číslo 3; s. 275 - 278 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Nature Publishing Group US
01.03.2017
Nature Publishing Group |
| Témata: | |
| ISSN: | 1548-7091, 1548-7105, 1548-7105 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Marker enrichment modeling (MEM) provides an objective metric for characterizing cell populations from high-content single-cell analysis. The MEM score outperforms standard metrics and provides a machine-readeable label for cell subsets.
Learning cell identity from high-content single-cell data presently relies on human experts. 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 outperforms 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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| Bibliografie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1548-7091 1548-7105 1548-7105 |
| DOI: | 10.1038/nmeth.4149 |