Weighted Scaling Approach for Metabolomics Data Analysis

by   Biplab Biswas, et al.

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


page 1

page 2

page 3

page 4


Adaptive Scaling

Preprocessing data is an important step before any data analysis. In thi...

The choice of scaling technique matters for classification performance

Dataset scaling, also known as normalization, is an essential preprocess...

Exploration of Heterogeneous Data Using Robust Similarity

Heterogeneous data pose serious challenges to data analysis tasks, inclu...

Adaptively Robust Geographically Weighted Regression

We develop a new robust geographically weighted regression method in the...

Detection and treatment of outliers for multivariate robust loss reserving

Traditional techniques for calculating outstanding claim liabilities suc...

Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method

Deep sequencing has become one of the most popular tools for transcripto...

SUnAA: Sparse Unmixing using Archetypal Analysis

This paper introduces a new sparse unmixing technique using archetypal a...

Please sign up or login with your details

Forgot password? Click here to reset