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Non-parametric change-point method for differential gene expression detection.

Wang Y, Wu C, Ji Z, Wang B, Liang Y - PLoS ONE (2011)

Bottom Line: NPCPS is based on the change point theory to provide effective DGE detecting ability.An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE.Experiment results showed both good accuracy and reliability of NPCPS.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Jilin, China.

ABSTRACT

Background: We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short), by using a single equation for detecting differential gene expression (DGE) in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability.

Methodology: NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods.

Conclusions: Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods.

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Change-point distribution of DGE genes when                            C(0.05) = 1.628.(A) CP-position distribution of 989 genes with positive                                Dn>1.628.                            (B) CP-position distribution of 989 genes with negative                                Dn<−1.628.
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pone-0020060-g005: Change-point distribution of DGE genes when C(0.05) = 1.628.(A) CP-position distribution of 989 genes with positive Dn>1.628. (B) CP-position distribution of 989 genes with negative Dn<−1.628.

Mentions: For NPCPS, C(0.05) = 1.628 was selected, which yields a detecting result of 1978 DGE genes. Fig. 5 shows the distribution of the estimated position of change-points in the expression value of these genes. We selected the first 30 genes ranked by NPCPS, and searched PubMed and other databases to confirm that whether these genes were relevant to breast cancer or other known cancers. Out of the first 30 genes identified by NPCPS, 17 have been reported as relevant to breast cancer or other cancers (as shown in Table 5 and Table 6 separately according to Dn value). The gene expression values and the change-point (CP) positions of the cancer-relevant genes are illustrated in Fig. 6 and Fig. 7. From Fig. 6 and 7, it could be seen that the estimated change-point positions could successfully locate the change in the trend of the gene expression value.


Non-parametric change-point method for differential gene expression detection.

Wang Y, Wu C, Ji Z, Wang B, Liang Y - PLoS ONE (2011)

Change-point distribution of DGE genes when                            C(0.05) = 1.628.(A) CP-position distribution of 989 genes with positive                                Dn>1.628.                            (B) CP-position distribution of 989 genes with negative                                Dn<−1.628.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3104986&req=5

pone-0020060-g005: Change-point distribution of DGE genes when C(0.05) = 1.628.(A) CP-position distribution of 989 genes with positive Dn>1.628. (B) CP-position distribution of 989 genes with negative Dn<−1.628.
Mentions: For NPCPS, C(0.05) = 1.628 was selected, which yields a detecting result of 1978 DGE genes. Fig. 5 shows the distribution of the estimated position of change-points in the expression value of these genes. We selected the first 30 genes ranked by NPCPS, and searched PubMed and other databases to confirm that whether these genes were relevant to breast cancer or other known cancers. Out of the first 30 genes identified by NPCPS, 17 have been reported as relevant to breast cancer or other cancers (as shown in Table 5 and Table 6 separately according to Dn value). The gene expression values and the change-point (CP) positions of the cancer-relevant genes are illustrated in Fig. 6 and Fig. 7. From Fig. 6 and 7, it could be seen that the estimated change-point positions could successfully locate the change in the trend of the gene expression value.

Bottom Line: NPCPS is based on the change point theory to provide effective DGE detecting ability.An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE.Experiment results showed both good accuracy and reliability of NPCPS.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Jilin, China.

ABSTRACT

Background: We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short), by using a single equation for detecting differential gene expression (DGE) in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability.

Methodology: NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods.

Conclusions: Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods.

Show MeSH
Related in: MedlinePlus