Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-4929
Title: Unsupervised assessment of microarray data quality using a Gaussian mixture model
Authors : Howard, Brian E.
Sick, Beate
Heber, Steffen
Published in : BMC bioinformatics
Volume(Issue) : 10
Issue : 191
Publisher / Ed. Institution : BioMed Central
Issue Date: 2009
License (according to publishing contract) : CC BY 2.0: Attribution 2.0 Generic
Type of review: Peer review (publication)
Language : English
Subjects : Algorithms; Computational biology; Gene expression profiling; Normal distribution; Oligonucleotide array sequence analysis
Subject (DDC) : 005: Computer programming, programs and data
570: Biology
Abstract: Background: Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny. Results: We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach. Conclusion: This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Article in scientific journal
DOI : 10.21256/zhaw-4929
10.1186/1471-2105-10-191
ISSN: 1471-2105
URI: https://digitalcollection.zhaw.ch/handle/11475/13394
Appears in Collections:Publikationen School of Engineering

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