Abstract
Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique influences the further statistical analysis. It is challenged to choose the appropriate scaling technique for downstream analysis to get accurate results or to make proper decision. Moreover, the existing scaling techniques are sensitive to outliers or extreme values. To fill the gap, our objective is to introduce a robust scaling approach that is not influenced by outliers as well as provides more accurate results for downstream analysis. Here, we introduced a new weighted scaling approach that is robust against outliers; however, no additional outlier detection/treatment step is needed in data preprocessing and also compared it with the conventional scaling and normalization techniques through artificial and real metabolomics datasets. We evaluated the performance of the proposed method in comparison to the other existing conventional scaling techniques using metabolomics data analysis in both the absence and presence of different percentages of outliers. Results show that in most cases, the proposed scaling technique is a better performer than the traditional scaling methods in both the absence and presence of outliers. The proposed method improves the further downstream metabolomics analysis. The R function of the proposed robust scaling method is available at https://github.com/nishithkumarpaul/robustScaling/blob/main/wscaling.R
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Data availability
Two datasets are available at the National Institute of Health (NIH) Common Fund’s National Metabolomics Data Repository (NMDR) website (https://www.metabolomicsworkbench.org/data/index.php). The breast cancer dataset was produced by GC–TOF-MS and processed by ChromaTOF software (v. 2.32) using the blood sample of 134 subjects.
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We thanks and acknowledge the Ministry of Science and Technology, Bangladesh for the National Science and Technology (NST) fellowship.
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BB analyzed the data, drafted the manuscript, and executed the statistical analysis. NK and MAA worked to develop the weighted scaling approach for metabolomics data analysis. NK, MAA, and MAH coordinated and supervised the project. All authors carefully read and finally approved the manuscript.
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Biswas, B., Kumar, N., Hoque, M.A. et al. Weighted scaling approach for metabolomics data analysis. Jpn J Stat Data Sci 6, 785–802 (2023). https://doi.org/10.1007/s42081-023-00205-2
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DOI: https://doi.org/10.1007/s42081-023-00205-2