Inferring Phenotypic Properties from Single-Cell Characteristics
Open Access
- 25 May 2012
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 7 (5), e37038
- https://doi.org/10.1371/journal.pone.0037038
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.This publication has 32 references indexed in Scilit:
- B-cell signaling networks reveal a negative prognostic human lymphoma cell subset that emerges during tumor progressionProceedings of the National Academy of Sciences of the United States of America, 2010
- Merging Mixture Components for Cell Population Identification in Flow CytometryAdvances in Bioinformatics, 2009
- flowCore: a Bioconductor package for high throughput flow cytometryBMC Bioinformatics, 2009
- Automated Gating of flow cytometry data via robust model-based clusteringCytometry Part A, 2008
- Mixture modeling approach to flow cytometry dataCytometry Part A, 2008
- Using Smudge Cells on Routine Blood Smears to Predict Clinical Outcome in Chronic Lymphocytic Leukemia: A Universally Available Prognostic TestMayo Clinic Proceedings, 2007
- CD33 expression and P-glycoprotein–mediated drug efflux inversely correlate and predict clinical outcome in patients with acute myeloid leukemia treated with gemtuzumab ozogamicin monotherapyBlood, 2007
- Quantum dot semiconductor nanocrystals for immunophenotyping by polychromatic flow cytometryNature Medicine, 2006
- Interpreting flow cytometry data: a guide for the perplexedNature Immunology, 2006
- Automated identification of subpopulations in flow cytometric list mode data using cluster analysisCytometry, 1985