Non-Parametric Change-Point Method for Differential Gene Expression Detection

by Yao Wang, Chunguo Wu, Zhaohua Ji, Binghong Wang, Yanchun Liang

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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Non-Parametric Change-Point Method for Differential Gene Expression
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