ArticlePloS one2012
Inferring phenotypic properties from single-cell characteristics.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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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.
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Who cites it
20 citing papers in PubMed.
- Immune profiling of patients with extranodal natural killer/T cell lymphoma treated with daratumumab.Annals of hematology · 2024Trial
- Effects of Release of TSG-6 from Heparin Hydrogels on Supraspinatus Muscle Regeneration.Tissue engineering. Part A · 2025Article
- A Stochastic Characterization of Hydrogen Peroxide-Induced Regulated Cell Death inFrontiers in microbiology · 2021Article
- Dual delivery of IL-10 and AT-RvD1 from PEG hydrogels polarize immune cells towards pro-regenerative phenotypes.Biomaterials · 2021Article
- Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- High-Parameter Mass Cytometry Evaluation of Relapsed/Refractory Multiple Myeloma Patients Treated with Daratumumab Demonstrates Immune Modulation as a Novel Mechanism of Action.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
- Comparative exploration of multidimensional flow cytometry software: a model approach evaluating T cell polyfunctional behavior.Journal of leukocyte biology · 2017Article
- Novel data analysis method for multicolour flow cytometry links variability of multiple markers on single cells to a clinical phenotype.Scientific reports · 2017Article
- Toward deterministic and semiautomated SPADE analysis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2017Article
- Integrated genomic analysis of recurrence-associated small non-coding RNAs in oesophageal cancer.Gut · 2017Article
- Visualization and cellular hierarchy inference of single-cell data using SPADE.Nature protocols · 2016Article
- Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples.Frontiers in oncology · 2016Article
- Single-cell mass cytometry reveals intracellular survival/proliferative signaling in FLT3-ITD-mutated AML stem/progenitor cells.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2015Article
- Understanding health and disease with multidimensional single-cell methods.Journal of physics. Condensed matter : an Institute of Physics journal · 2014Review
- The Shortlist Method for fast computation of the Earth Mover's Distance and finding optimal solutions to transportation problems.PloS one · 2014Article
- From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells.PLoS computational biology · 2013Article
- TreeVis: a MATLAB-based tool for tree visualization.Computer methods and programs in biomedicine · 2013Article
- Flow cytometry bioinformatics.PLoS computational biology · 2013Article
- Immune monitoring in cancer vaccine clinical trials: critical issues of functional flow cytometry-based assays.BioMed research international · 2013Review
- Unfold High-Dimensional Clouds for Exhaustive Gating of Flow Cytometry Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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1 author.
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Abstract
Flow cytometry provides multi-dimensional data at the single-cell level. Such data contain information about the cellular heterogeneity of bulk samples, making it possible to correlate single-cell features with phenotypic properties of bulk tissues. Predicting phenotypes from single-cell measurements is a difficult challenge that has not been extensively studied. The 6th Dialogue for Reverse Engineering Assessments and Methods (DREAM6) invited the research community to develop solutions to a computational challenge: classifying acute myeloid leukemia (AML) positive patients and healthy donors using flow cytometry data. DREAM6 provided flow cytometry data for 359 normal and AML samples, and the class labels for half of the samples. Researchers were asked to predict the class labels of the remaining half. This paper describes one solution that was constructed by combining three algorithms: spanning-tree progression analysis of density-normalized events (SPADE), earth mover's distance, and a nearest-neighbor classifier called Relief. This solution was among the top-performing methods that achieved 100% prediction accuracy.
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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.