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...

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Autores principales:	Yao Wang, Guang Sun, Zhaohua Ji, Chong Xing, Yanchun Liang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4b55832ff38b47b5906c1336ab8b0797
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spelling	oai:doaj.org-article:4b55832ff38b47b5906c1336ab8b07972021-11-18T07:29:45ZWeighted change-point method for detecting differential gene expression in breast cancer microarray data.1932-620310.1371/journal.pone.0029860https://doaj.org/article/4b55832ff38b47b5906c1336ab8b07972012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22276133/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203In 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.Yao WangGuang SunZhaohua JiChong XingYanchun LiangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 1, p e29860 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yao Wang Guang Sun Zhaohua Ji Chong Xing Yanchun Liang Weighted change-point method for detecting differential gene expression in breast cancer microarray data.
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.
format	article
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
title	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_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_sort	weighted change-point method for detecting differential gene expression in breast cancer microarray data.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/4b55832ff38b47b5906c1336ab8b0797
work_keys_str_mv	AT yaowang weightedchangepointmethodfordetectingdifferentialgeneexpressioninbreastcancermicroarraydata AT guangsun weightedchangepointmethodfordetectingdifferentialgeneexpressioninbreastcancermicroarraydata AT zhaohuaji weightedchangepointmethodfordetectingdifferentialgeneexpressioninbreastcancermicroarraydata AT chongxing weightedchangepointmethodfordetectingdifferentialgeneexpressioninbreastcancermicroarraydata AT yanchunliang weightedchangepointmethodfordetectingdifferentialgeneexpressioninbreastcancermicroarraydata
_version_	1718423343026143232

Weighted change-point method for detecting differential gene expression in breast cancer microarray data.

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