A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects

dc.contributor.authorDundar, Murat
dc.contributor.authorAkova, Ferit
dc.contributor.authorYerebakan, Halid Z.
dc.contributor.authorRajwa, Bartek
dc.contributor.departmentDepartment of Computer & Information Science, School of Scienceen_US
dc.date.accessioned2016-05-13T17:52:47Z
dc.date.available2016-05-13T17:52:47Z
dc.date.issued2014
dc.description.abstractBACKGROUND: Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way. RESULTS: We demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively. CONCLUSIONS: These results demonstrate that anomalous samples can be identified by ASPIRE with almost perfect accuracy without a priori access to samples of anomalous subtypes in the training set. The ASPIRE approach is unique in its ability to form generalizations regarding normal and anomalous states given only very weak assumptions regarding sample characteristics and origin. Thus, ASPIRE could become highly instrumental in providing unique insights about observed biological phenomena in the absence of full information about the investigated samples.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationDundar, M., Akova, F., Yerebakan, H. Z., & Rajwa, B. (2014). A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects. BMC Bioinformatics, 15(1), 314. http://doi.org/10.1186/1471-2105-15-314en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttps://hdl.handle.net/1805/9588
dc.language.isoen_USen_US
dc.publisherSpringer (Biomed Central Ltd.)en_US
dc.relation.isversionof10.1186/1471-2105-15-314en_US
dc.relation.journalBMC bioinformaticsen_US
dc.rightsAttribution 3.0 United States
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.sourcePMCen_US
dc.subjectAlgorithmsen_US
dc.subjectComputational Biologyen_US
dc.subjectmethodsen_US
dc.subjectFlow Cytometryen_US
dc.subjectPhenotypeen_US
dc.titleA non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effectsen_US
dc.typeArticleen_US
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