A random forest based biomarker discovery and power analysis framework for diagnostics research
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RESEARCH ARTICLE
A random forest based biomarker discovery and power analysis framework for diagnostics research Animesh Acharjee1,2,3*† , Joseph Larkman1,2†, Yuanwei Xu1,2, Victor Roth Cardoso1,2,4 and Georgios V. Gkoutos1,2,3,4,5,6
Abstract Background: Biomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale –omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge. Methods: In this study, using both simulated and published experimentally derived datasets, we assessed the performance of several state-of-the-art Random Forest (RF) based decision approaches, namely the Boruta method, the permutation based feature selection without correction method, the permutation based feature selection with correction method, and the backward elimination based feature selection method. Moreover, we conducted a power analysis to estimate the number of samples required for potential future studies. Results: We present a number of different RF based stable feature selection methods and compare their performances using simulated, as well as published, experimentally derived, datasets. Across all of the scenarios considered, we found the Boruta method to be the most stable methodology, whilst the Permutation (Raw) approach offered the largest number of relevant features, when allowed to stabilise over a number of iterations. Finally, we developed and made available a web interface (https://joelarkman.shinyapps.io/PowerTools/) to streamline power calculations thereby aiding the design of potential future studies within a translational medicine context. Conclusions: We developed a RF-based biomarker discovery framework and provide a web interface for our framework, termed PowerTools, that caters the design of appropriate and cost-effective subsequent future omics study. Keywords: Random forest, Feature selection, Power study, Biomarker
*Correspondence: [email protected] † Animesh Acharjee and Joseph Larkman joint first authors 1 College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK Full list of author information is available at the end of the article
Background Over the last few years there has been lots of emphasis on the high dimensional omics data generation, including untargeted –omics datasets, like transcriptomics [1, 2] metabolomics [3, 4], proteomics [5, 6], microbiomes [7–9], as well as
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