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Weighted Scaling Approach for Metabolomics Data Analysis | | Biplab Biswas
; Nishith Kumar
; Md Aminul Hoque
; Md Ashad Alam
; | | Date: |
1 Aug 2022 | | 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
this https URL | | Source: | arXiv, 2208.00603 | | Services: | Forum | Review | PDF | Favorites | |
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