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https://doi.org/10.21256/zhaw-4929| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Unsupervised assessment of microarray data quality using a Gaussian mixture model |
| Authors: | Howard, Brian E. Sick, Beate Heber, Steffen |
| DOI: | 10.21256/zhaw-4929 10.1186/1471-2105-10-191 |
| Published in: | BMC Bioinformatics |
| Volume(Issue): | 10 |
| Issue: | 191 |
| Issue Date: | 2009 |
| Publisher / Ed. Institution: | BioMed Central |
| ISSN: | 1471-2105 |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/13394 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 2.0: Attribution 2.0 Generic |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Appears in collections: | Publikationen School of Engineering |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 2009_Howard_Unsupervised_assessment_of_microarray_data_quality.pdf | 1.22 MB | Adobe PDF |  View/Open |
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Howard, B. E., Sick, B., & Heber, S. (2009). Unsupervised assessment of microarray data quality using a Gaussian mixture model. BMC Bioinformatics, 10(191). https://doi.org/10.21256/zhaw-4929
Howard, B.E., Sick, B. and Heber, S. (2009) ‘Unsupervised assessment of microarray data quality using a Gaussian mixture model’, BMC Bioinformatics, 10(191). Available at: https://doi.org/10.21256/zhaw-4929.
B. E. Howard, B. Sick, and S. Heber, “Unsupervised assessment of microarray data quality using a Gaussian mixture model,” BMC Bioinformatics, vol. 10, no. 191, 2009, doi: 10.21256/zhaw-4929.
HOWARD, Brian E., Beate SICK und Steffen HEBER, 2009. Unsupervised assessment of microarray data quality using a Gaussian mixture model. BMC Bioinformatics. 2009. Bd. 10, Nr. 191. DOI 10.21256/zhaw-4929
Howard, Brian E., Beate Sick, and Steffen Heber. 2009. “Unsupervised Assessment of Microarray Data Quality Using a Gaussian Mixture Model.” BMC Bioinformatics 10 (191). https://doi.org/10.21256/zhaw-4929.
Howard, Brian E., et al. “Unsupervised Assessment of Microarray Data Quality Using a Gaussian Mixture Model.” BMC Bioinformatics, vol. 10, no. 191, 2009, https://doi.org/10.21256/zhaw-4929.
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