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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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Related in: MedlinePlus

Data distribution of genes bottom-ranked by NPCPS.(A) SLC6A8: rank 500. (B) HLF: rank 1000. (C) ATP5F1: rank 2000. (D)                            HLA-H: rank 3000. (E) ODF3B: rank 4000. (F) SLC20A2: rank 5000. (G)                            SGSH: rank 6000. (H) CXCR2: rank 7000. From the empirical data                            distribution, the differences between cancer and normal groups in                            (A)–(D) were very small, which corresponded with the                                    Dn value.
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pone-0020060-g010: Data distribution of genes bottom-ranked by NPCPS.(A) SLC6A8: rank 500. (B) HLF: rank 1000. (C) ATP5F1: rank 2000. (D) HLA-H: rank 3000. (E) ODF3B: rank 4000. (F) SLC20A2: rank 5000. (G) SGSH: rank 6000. (H) CXCR2: rank 7000. From the empirical data distribution, the differences between cancer and normal groups in (A)–(D) were very small, which corresponded with the Dn value.

Mentions: NPCPS results showed that, among the 7219 genes, 3608 had negative Dn, while the rest 3521 had positive Dn. NPCPS use Dn to evaluate the change in distribution between normal and cancer samples, and directly measure the DGE type as either over expressed or under expressed. This feature is valid based on the expression value in Fig. 6 and 7, where Fig. 6 (positive Dn) shows typical under expression and Fig. 7 (negative Dn) shows typical over expression. Fig. 9 and Fig. 10 can illustrate the relationship between Dn and DGE in a more intuitive manner where cumulative data distributions of several typically ranked genes are given.


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

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

Data distribution of genes bottom-ranked by NPCPS.(A) SLC6A8: rank 500. (B) HLF: rank 1000. (C) ATP5F1: rank 2000. (D)                            HLA-H: rank 3000. (E) ODF3B: rank 4000. (F) SLC20A2: rank 5000. (G)                            SGSH: rank 6000. (H) CXCR2: rank 7000. From the empirical data                            distribution, the differences between cancer and normal groups in                            (A)–(D) were very small, which corresponded with the                                    Dn value.
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Related In: Results  -  Collection

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

pone-0020060-g010: Data distribution of genes bottom-ranked by NPCPS.(A) SLC6A8: rank 500. (B) HLF: rank 1000. (C) ATP5F1: rank 2000. (D) HLA-H: rank 3000. (E) ODF3B: rank 4000. (F) SLC20A2: rank 5000. (G) SGSH: rank 6000. (H) CXCR2: rank 7000. From the empirical data distribution, the differences between cancer and normal groups in (A)–(D) were very small, which corresponded with the Dn value.
Mentions: NPCPS results showed that, among the 7219 genes, 3608 had negative Dn, while the rest 3521 had positive Dn. NPCPS use Dn to evaluate the change in distribution between normal and cancer samples, and directly measure the DGE type as either over expressed or under expressed. This feature is valid based on the expression value in Fig. 6 and 7, where Fig. 6 (positive Dn) shows typical under expression and Fig. 7 (negative Dn) shows typical over expression. Fig. 9 and Fig. 10 can illustrate the relationship between Dn and DGE in a more intuitive manner where cumulative data distributions of several typically ranked genes are given.

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