Inferring phenotypic properties from single-cell characteristics

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 measurement...

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Bibliographic Details
Published in:PloS one 2012-05, Vol.7 (5), p.e37038-e37038
Main Author: Qiu, Peng
Format: Article
Language:English
Subjects:
Acute myeloid leukemia
Algorithms
Analysis
Apoptosis
Artificial intelligence
B cells
Bioinformatics
Biology
Breast cancer
Cancer research
Classification
Cluster Analysis
Computer applications
Computer Science
Cytometry
Flow cytometry
Flow Cytometry - methods
Gene expression
Humans
Image retrieval
Immunophenotyping
Labels
Leukemia
Leukemia, Myeloid, Acute - diagnosis
Lymphoma
Lymphomas
Medical prognosis
Medical research
Medicine
Methods
Multidimensional data
Myeloid leukemia
Nearest-neighbor
Neural networks
Parameter estimation
Phenotype
Predictions
Proteins
Reverse engineering
Signal transduction
Studies
Tissues
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0037038