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A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes.

ABSTRACT: MOTIVATION:The proportion of non-differentially expressed genes (pi(0)) is an important quantity in microarray data analysis. Although many statistical methods have been proposed for its estimation, it is still necessary to develop more efficient methods. METHODS:Our approach for improving pi(0) estimation is to modify an existing simple method by introducing artificial censoring to P-values. In a comprehensive simulation study and the applications to experimental datasets, we compare our method with eight existing estimation methods. RESULTS:The simulation study confirms that our method can clearly improve the estimation performance. Compared with the existing methods, our method can generally provide a relatively accurate estimate with relatively small variance. Using experimental microarray datasets, we also demonstrate that our method can generally provide satisfactory estimates in practice. AVAILABILITY:The R code is freely available at http://home.gwu.edu/~ylai/research/CBpi0/.

SUBMITTER: Markitsis A

PROVIDER: S-EPMC2828110 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes.

Markitsis Anastasios A Lai Yinglei Y

Bioinformatics (Oxford, England) 20100115 5

<h4>Motivation</h4>The proportion of non-differentially expressed genes (pi(0)) is an important quantity in microarray data analysis. Although many statistical methods have been proposed for its estimation, it is still necessary to develop more efficient methods.<h4>Methods</h4>Our approach for improving pi(0) estimation is to modify an existing simple method by introducing artificial censoring to P-values. In a comprehensive simulation study and the applications to experimental datasets, we com ...[more]

PMID: 20080506

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Project description:MotivationThe statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary.ResultsWe present MEMO, a flexible mixture modeling framework that enables the simultaneous, automated analysis of censored and uncensored data acquired under multiple experimental conditions. MEMO is based on maximum-likelihood inference and allows for testing competing hypotheses. MEMO can be applied to a variety of different single-cell data types. We demonstrate the advantages of MEMO by analyzing right and interval censored single-cell microscopy data. Our results show that an examination of censoring and the simultaneous consideration of different experimental conditions are necessary to reveal biologically meaningful subpopulation structures. MEMO allows for a stringent analysis of single-cell data and enables researchers to avoid misinterpretation of censored data. Therefore, MEMO is a valuable asset for all fields that infer the characteristics of populations by looking at single individuals such as cell biology and medicine.Availability and implementationMEMO is implemented in MATLAB and freely available via github (https://github.com/MEMO-toolbox/MEMO).Contactseva-maria.geissen@ist.uni-stuttgart.de or nicole.radde@ist.uni-stuttgart.deSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes.

Publications

A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes.

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