Weighted Scaling Approach for Metabolomics Data Analysis

TL;DR

This paper introduces a new weighted scaling method for metabolomics data that is robust against outliers, improving the accuracy of downstream analysis without requiring prior outlier removal.

Contribution

A novel weighted scaling approach for metabolomics data that is robust to outliers and enhances analysis accuracy, eliminating the need for outlier detection steps.

Findings

Proposed method outperforms traditional scaling techniques in various datasets.

The new scaling method is effective both with and without outliers.

Improves downstream metabolomics data analysis accuracy.

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 influence the further statistical analysis. It is challenging to choose the appropriate scaling technique for downstream analysis to get accurate results or to make a 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, where 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 performs better 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