Dataset Information

Robust RNA-seq data analysis using an integrated method of ROC curve and Kolmogorov-Smirnov test.

ABSTRACT: It is a common approach to dichotomize a continuous biomarker in clinical setting for the convenience of application. Analytically, results from using a dichotomized biomarker are often more reliable and resistant to outliers, bi-modal and other unknown distributions. There are two commonly used methods for selecting the best cut-off value for dichotomization of a continuous biomarker, using either maximally selected chi-square statistic or a ROC curve, specifically the Youden Index. In this paper, we explained that in many situations, it is inappropriate to use the former. By using the Maximum Absolute Youden Index (MAYI), we demonstrated that the integration of a MAYI and the Kolmogorov-Smirnov test is not only a robust non-parametric method, but also provides more meaningful p value for selecting the cut-off value than using a Mann-Whitney test. In addition, our method can be applied directly in clinical settings.

SUBMITTER: Yang S

PROVIDER: S-EPMC9793859 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Robust RNA-seq data analysis using an integrated method of ROC curve and Kolmogorov-Smirnov test.

Yang Shengping S Zhang Kun K Fang Zhide Z

Communications in statistics: Simulation and computation 20201025 12

It is a common approach to dichotomize a continuous biomarker in clinical setting for the convenience of application. Analytically, results from using a dichotomized biomarker are often more reliable and resistant to outliers, bi-modal and other unknown distributions. There are two commonly used methods for selecting the best cut-off value for dichotomization of a continuous biomarker, using either maximally selected chi-square statistic or a ROC curve, specifically the Youden Index. In this pap ...[more]

PMID: 36583130

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Project description:BACKGROUND: The receiver operating characteristic (ROC) curve is a fundamental tool to assess the discriminant performance for not only a single marker but also a score function combining multiple markers. The area under the ROC curve (AUC) for a score function measures the intrinsic ability for the score function to discriminate between the controls and cases. Recently, the partial AUC (pAUC) has been paid more attention than the AUC, because a suitable range of the false positive rate can be focused according to various clinical situations. However, existing pAUC-based methods only handle a few markers and do not take nonlinear combination of markers into consideration. RESULTS: We have developed a new statistical method that focuses on the pAUC based on a boosting technique. The markers are combined componentially for maximizing the pAUC in the boosting algorithm using natural cubic splines or decision stumps (single-level decision trees), according to the values of markers (continuous or discrete). We show that the resulting score plots are useful for understanding how each marker is associated with the outcome variable. We compare the performance of the proposed boosting method with those of other existing methods, and demonstrate the utility using real data sets. As a result, we have much better discrimination performances in the sense of the pAUC in both simulation studies and real data analysis. CONCLUSIONS: The proposed method addresses how to combine the markers after a pAUC-based filtering procedure in high dimensional setting. Hence, it provides a consistent way of analyzing data based on the pAUC from maker selection to marker combination for discrimination problems. The method can capture not only linear but also nonlinear association between the outcome variable and the markers, about which the nonlinearity is known to be necessary in general for the maximization of the pAUC. The method also puts importance on the accuracy of classification performance as well as interpretability of the association, by offering simple and smooth resultant score plots for each marker.

Dataset Information

Robust RNA-seq data analysis using an integrated method of ROC curve and Kolmogorov-Smirnov test.

Publications

Robust RNA-seq data analysis using an integrated method of ROC curve and Kolmogorov-Smirnov test.

Similar Datasets

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