ArticlePloS one2011
Non-parametric change-point method for differential gene expression detection.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence.Stats · 2024Article
- A Weighted Error Distance Metrics (WEDM) for Performance Evaluation on Multiple Change-Point (MCP) Detection in Synthetic Time Series.Computational intelligence and neuroscience · 2022Article
- Structural change detection in ordinal time series.PloS one · 2021Article
- Discriminant Analysis of Lung Cancer Using Nonlinear Clustering of Copy Numbers.Cancer investigation · 2020Article
- Statistical analysis of multi-dimensional, temporal gene expression of stem cells to elucidate colony size-dependent neural differentiation.Molecular omics · 2018Article
- FDR control of detected regions by multiscale matched filtering.Communications in statistics: Simulation and computation · 2017Article
- A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.Computational intelligence and neuroscience · 2016Article
- A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns.PloS one · 2015Article
- Detection of Local DNA Copy Number Changes in Lung Cancer Population Analyses Using A Multi-Scale Approach.Communications in statistics. Case studies, data analysis and applications · 2015Article
- Integrative gene set analysis of multi-platform data with sample heterogeneity.Bioinformatics (Oxford, England) · 2014Article
- A novel method for fast Change-Point detection on simulated time series and electrocardiogram data.PloS one · 2014Article
- Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine.Genome medicine · 2013Article
- β-empirical Bayes inference and model diagnosis of microarray data.BMC bioinformatics · 2012Article
- Weighted change-point method for detecting differential gene expression in breast cancer microarray data.PloS one · 2012Article
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5 authors.
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Abstract
backgroundWe 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.
conclusionsSimulation 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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