ArticleCurrent protocols in cytometry2018
Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).
Article in Current protocols in cytometry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Build Heatmaps of Cell Frequencies and Marker Intensities for Correct Interpretations.Methods in molecular biology (Clifton, N.J.) · 2027Article
- The Natural Killer Cell Landscape in the Natural History of Hantavirus Cardiopulmonary Syndrome in a Chilean Cohort.Viruses · 2026Article
- CARGO: A Cytometry Analysis framework via Regularized Graph Optimal-transport.PLoS computational biology · 2026Article
- Single cell protein profiling of focal cortical dysplasia in a patient requiring multiple resections.Acta neuropathologica communications · 2026Article
- Distinct time-dependentFrontiers in cellular and infection microbiology · 2026Article
- Automated descriptive cell type naming in flow and mass cytometry with CytoPheno.Scientific reports · 2025Article
- GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.Neuro-oncology · 2023Article
- The SWI/SNF chromatin-remodeling subunit DPF2 facilitates NRF2-dependent antiinflammatory and antioxidant gene expression.The Journal of clinical investigation · 2023Article
- Two New Neutrophil Subsets Define a Discriminating Sepsis Signature.American journal of respiratory and critical care medicine · 2022Observational
- Article
- Defining T Cell Subsets in Human Tonsils Using ChipCytometry.Journal of immunology (Baltimore, Md. : 1950) · 2021Article
- Targeting In Vivo Metabolic Vulnerabilities of Th2 and Th17 Cells Reduces Airway Inflammation.Journal of immunology (Baltimore, Md. : 1950) · 2021Observational
- Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.bioRxiv : the preprint server for biology · 2020Article
- Article
- Training Novices in Generation and Analysis of High-Dimensional Human Cell Phospho-Flow Cytometry Data.Current protocols in cytometry · 2020Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
- Human Germinal Center B Cells Differ from Naïve and Memory B Cells in CD40 Expression and CD40L-Induced Signaling Response.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
- Picturing Polarized Myeloid Phagocytes and Regulatory Cells by Mass Cytometry.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Machine learning reveals chronic graft-Haematologica · 2019Article
- BRAF and MEK inhibitor therapy eliminates Nestin-expressing melanoma cells in human tumors.Pigment cell & melanoma research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Multiplexed single-cell experimental techniques like mass cytometry measure 40 or more features and enable deep characterization of well-known and novel cell populations. However, traditional data analysis techniques rely extensively on human experts or prior knowledge, and novel machine learning algorithms may generate unexpected population groupings. Marker enrichment modeling (MEM) creates quantitative identity labels based on features enriched in a population relative to a reference. While developed for cell type analysis, MEM labels can be generated for a wide range of multidimensional data types, and MEM works effectively with output from expert analysis and diverse machine learning algorithms. MEM is implemented as an R package and includes three steps: (1) calculation of MEM values that quantify each feature's relative enrichment in the population, (2) reporting of MEM labels as a heatmap or as a text label, and (3) quantification of MEM label similarity between populations. The protocols here show MEM analysis using datasets from immunology and oncology. These MEM implementations provide a way to characterize population identity and novelty in the context of computational and expert analyses. © 2018 by John Wiley & Sons, Inc.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.