Weighted change-point method for detecting differential gene expression in breast cancer microarray data.
In previous work, we proposed a method for detecting differential gene expression based on change-point of expression profile. This non-parametric change-point method gave promising result in both simulation study and public dataset experiment. However, the performance is still limited by the less s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22276133/pdf/?tool=EBI |
| _version_ | 1818426169571147776 |
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| author | Yao Wang Guang Sun Zhaohua Ji Chong Xing Yanchun Liang |
| author_facet | Yao Wang Guang Sun Zhaohua Ji Chong Xing Yanchun Liang |
| author_sort | Yao Wang |
| collection | DOAJ |
| description | In previous work, we proposed a method for detecting differential gene expression based on change-point of expression profile. This non-parametric change-point method gave promising result in both simulation study and public dataset experiment. However, the performance is still limited by the less sensitiveness to the right bound and the statistical significance of the statistics has not been fully explored. To overcome the insensitiveness to the right bound we modified the original method by adding a weight function to the D(n) statistic. Simulation study showed that the weighted change-point statistics method is significantly better than the original NPCPS in terms of ROC, false positive rate, as well as change-point estimate. The mean absolute error of the estimated change-point by weighted change-point method was 0.03, reduced by more than 50% comparing with the original 0.06, and the mean FPR was reduced by more than 55%. Experiment on microarray Dataset I resulted in 3974 differentially expressed genes out of total 5293 genes; experiment on microarray Dataset II resulted in 9983 differentially expressed genes among total 12576 genes. In summary, the method proposed here is an effective modification to the previous method especially when only a small subset of cancer samples has DGE. |
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| format | Article |
| id | doaj.art-4b55832ff38b47b5906c1336ab8b0797 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T14:25:34Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj.art-4b55832ff38b47b5906c1336ab8b07972022-12-21T22:57:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0171e2986010.1371/journal.pone.0029860Weighted change-point method for detecting differential gene expression in breast cancer microarray data.Yao WangGuang SunZhaohua JiChong XingYanchun LiangIn previous work, we proposed a method for detecting differential gene expression based on change-point of expression profile. This non-parametric change-point method gave promising result in both simulation study and public dataset experiment. However, the performance is still limited by the less sensitiveness to the right bound and the statistical significance of the statistics has not been fully explored. To overcome the insensitiveness to the right bound we modified the original method by adding a weight function to the D(n) statistic. Simulation study showed that the weighted change-point statistics method is significantly better than the original NPCPS in terms of ROC, false positive rate, as well as change-point estimate. The mean absolute error of the estimated change-point by weighted change-point method was 0.03, reduced by more than 50% comparing with the original 0.06, and the mean FPR was reduced by more than 55%. Experiment on microarray Dataset I resulted in 3974 differentially expressed genes out of total 5293 genes; experiment on microarray Dataset II resulted in 9983 differentially expressed genes among total 12576 genes. In summary, the method proposed here is an effective modification to the previous method especially when only a small subset of cancer samples has DGE.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22276133/pdf/?tool=EBI |
| spellingShingle | Yao Wang Guang Sun Zhaohua Ji Chong Xing Yanchun Liang Weighted change-point method for detecting differential gene expression in breast cancer microarray data. PLoS ONE |
| title | Weighted change-point method for detecting differential gene expression in breast cancer microarray data. |
| title_full | Weighted change-point method for detecting differential gene expression in breast cancer microarray data. |
| title_fullStr | Weighted change-point method for detecting differential gene expression in breast cancer microarray data. |
| title_full_unstemmed | Weighted change-point method for detecting differential gene expression in breast cancer microarray data. |
| title_short | Weighted change-point method for detecting differential gene expression in breast cancer microarray data. |
| title_sort | weighted change point method for detecting differential gene expression in breast cancer microarray data |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22276133/pdf/?tool=EBI |
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