Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).

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Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).

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2018 nî lūn-bûn

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2018年の論文

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2018年学术文章

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2018年学术文章

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Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).

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Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling

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Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).

@en

Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling

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Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).

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Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling

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P1476

Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).

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P2093

Caroline E Roe

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Jocelyn S Gandelman

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Kirsten E Diggins

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P2860

Critical assessment of automated flow cytometry data analysis techniques.

Normalization of mass cytometry data with bead standards.

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia.

FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data

Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.

Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning.

Automated identification of stratifying signatures in cellular subpopulations

Mass Cytometry: Single Cells, Many Features.

P304

10.21.1-10.21.28

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P31

scholarly article

P356

10.1002/CPCY.34

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P478

http://www.w3.org/2001/XMLSchema#string

P50

Jonathan M. Irish

P577

2018-01-18T00:00:00Z

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P698

29345329

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P932

5774653

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