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Robust gene selection methods using weighting schemes for microarray data analysis

Title
Robust gene selection methods using weighting schemes for microarray data analysis
Authors
Kang S.Song J.
Ewha Authors
송종우
SCOPUS Author ID
송종우scopus
Issue Date
2017
Journal Title
BMC Bioinformatics
ISSN
1471-2105JCR Link
Citation
vol. 18, no. 1
Keywords
False discovery rateGene selection methodMicroarray dataNoisy dataRobustnessSignificance analysis of microarrays
Publisher
BioMed Central Ltd.
Indexed
SCIE; SCOPUS WOS scopus
Abstract
Background: A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. Results: We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. Conclusions: The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis. © 2017 The Author(s).
DOI
10.1186/s12859-017-1810-x
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자연과학대학 > 통계학전공 > Journal papers
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