Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach.
In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/indepe...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0123791&type=printable |
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| author | Mojtaba Ganjali Taban Baghfalaki Damon Berridge |
| author_facet | Mojtaba Ganjali Taban Baghfalaki Damon Berridge |
| author_sort | Mojtaba Ganjali |
| collection | DOAJ |
| description | In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/independent distributions is considered. These distributions include the Student's t and normal distributions which have been used previously, but also include extensions such as the slash, the contaminated normal and the Laplace distributions. The purpose of this paper is to identify differentially expressed genes by considering these distributional assumptions instead of the normal distribution. A Bayesian approach using the Markov Chain Monte Carlo method is adopted for parameter estimation. Two publicly available gene expression data sets are analyzed using the proposed approach. The use of the robust models for detecting differentially expressed genes is investigated. This investigation shows that the choice of model for differentiating gene expression data is very important. This is due to the small number of replicates for each gene and the existence of outlying data. Comparison of the performance of these models is made using different statistical criteria and the ROC curve. The method is illustrated using some simulation studies. We demonstrate the flexibility of these robust models in identifying differentially expressed genes. |
| format | Article |
| id | doaj-art-137bf38b67b44560a045c6c8b38601c6 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-137bf38b67b44560a045c6c8b38601c62025-08-20T02:34:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01104e012379110.1371/journal.pone.0123791Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach.Mojtaba GanjaliTaban BaghfalakiDamon BerridgeIn this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/independent distributions is considered. These distributions include the Student's t and normal distributions which have been used previously, but also include extensions such as the slash, the contaminated normal and the Laplace distributions. The purpose of this paper is to identify differentially expressed genes by considering these distributional assumptions instead of the normal distribution. A Bayesian approach using the Markov Chain Monte Carlo method is adopted for parameter estimation. Two publicly available gene expression data sets are analyzed using the proposed approach. The use of the robust models for detecting differentially expressed genes is investigated. This investigation shows that the choice of model for differentiating gene expression data is very important. This is due to the small number of replicates for each gene and the existence of outlying data. Comparison of the performance of these models is made using different statistical criteria and the ROC curve. The method is illustrated using some simulation studies. We demonstrate the flexibility of these robust models in identifying differentially expressed genes.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0123791&type=printable |
| spellingShingle | Mojtaba Ganjali Taban Baghfalaki Damon Berridge Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach. PLoS ONE |
| title | Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach. |
| title_full | Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach. |
| title_fullStr | Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach. |
| title_full_unstemmed | Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach. |
| title_short | Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach. |
| title_sort | robust modeling of differential gene expression data using normal independent distributions a bayesian approach |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0123791&type=printable |
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