Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data
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Ahmed Hossain
und
Joseph Beyene
Abstract
MicroRNAs (miRNAs) are short non-coding RNAs that play critical roles in numerous cellular processes through post-transcriptional functions. The aberrant role of miRNAs has been reported in a number of diseases. A robust computational method is vital to discover novel miRNAs where level of noise varies dramatically across the different miRNAs. In this paper, we propose a flexible rank-based procedure for estimating a weighted log partial area under the receiver operating characteristic (ROC) curve statistic for selecting differentially expressed miRNAs. The statistic combines results taking partial area under the curve (pAUC) and their corresponding variance. The proposed method does not involve complicated formulas and does not require advanced programming skills. Two real datasets are analyzed to illustrate the method and a simulation study is carried out to assess the performance of different miRNA ranking statistics. We conclude that the proposed method offers robust results with large samples for miRNA expression data, and the method can be used as an alternative analytical tool for identifying a list of target miRNAs for further biological and clinical investigation.
AH acknowledges post-doctoral fellowship funding from the Drug Safety and Effectiveness Cross-Disciplinary Training (DSECT) Program. JB would like to acknowledge Discovery Grant funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) (grant number 293295-2009) and Canadian Institutes of Health Research (CIHR) (grant number 84392). JB holds the John D. Cameron Endowed Chair in the Genetic Determinants of Chronic Diseases, Department of Clinical Epidemiology and Biostatistics, McMaster University. We would like to thank two anonymous reviewers and the editor for insightful comments that improved the presentation and clarity of our manuscript.
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Artikel in diesem Heft
- Masthead
- Masthead
- Research Articles
- A new variance stabilizing transformation for gene expression data analysis
- Kernel approximate Bayesian computation in population genetic inferences
- Permutation tests for analyzing cospeciation in multiple phylogenies: applications in tri-trophic ecology
- Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies
- Modeling, simulation and analysis of methylation profiles from reduced representation bisulfite sequencing experiments
- Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data
- Random forests on distance matrices for imaging genetics studies
Artikel in diesem Heft
- Masthead
- Masthead
- Research Articles
- A new variance stabilizing transformation for gene expression data analysis
- Kernel approximate Bayesian computation in population genetic inferences
- Permutation tests for analyzing cospeciation in multiple phylogenies: applications in tri-trophic ecology
- Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies
- Modeling, simulation and analysis of methylation profiles from reduced representation bisulfite sequencing experiments
- Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data
- Random forests on distance matrices for imaging genetics studies
