Unsupervised assessment of microarray data quality using a Gaussian mixture model

BioMed Central 2018-11-30T09:22:02Z 2018-11-30T09:22:02Z 2009

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.

Algorithms; Computational biology; Gene expression profiling; Normal distribution; Oligonucleotide array sequence analysis; 005: Computerprogrammierung, Programme und Daten; 570: Biologie; Beitrag in wissenschaftlicher Zeitschrift; Text

application/pdf
BMC Bioinformatics
English

CHZHA oai:digitalcollection.zhaw.ch:11475/13394
https://doi.org/10.21256/zhaw-4929
https://doi.org/10.1186/1471-2105-10-191
info:doi/10.21256/zhaw-4929
info:doi/10.1186/1471-2105-10-191
https://hdl.handle.net/11475/13394
https://digitalcollection.zhaw.ch/handle/11475/13394
info:hdl/11475/13394
urn:issn:1471-2105
19545436
1079396366

ZHAW UNIV LIBR
From OAIster®, provided by the OCLC Cooperative.

edsoai.on1079396366