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Preprint / Version 1

Single sample enrichment analysis for mass spectrometry-based omics data

##article.authors##

  • Hiroyuki Yamamoto Japan Computational Mass Spectrometry (JCompMS) group

DOI:

https://doi.org/10.51094/jxiv.484

Keywords:

single sample enrichment analysis, mssing value, metabolomics

Abstract

In omics studies using mass spectrometry, including metabolomics, it is challenging to detect all metabolites, leading to potential biases when performing traditional enrichment analyses. In this study, we applied a single sample enrichment analysis to the metabolome data of fasting mice. This method, distinct from conventional approaches, utilizes information from both detected and undetected metabolites. It is particularly useful for omics data from mass spectrometry where it is difficult to comprehensively capture all metabolites and where there are many missing values.

Conflicts of Interest Disclosure

There are no conflicts of interest to disclose.

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References

Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013).

Wieder, C., Lai, R.P.J. & Ebbels, T.M.D. Single sample pathway analysis in metabolomics: performance evaluation and application. BMC Bioinformatics 23, 481 (2022).

Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA, Global function profiling of gene expression. Genomics. 2003, 81: 98-104.

Yamamoto, H., Fujimori, T., Sato, H. et al. Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis. BMC Bioinformatics 15, 51 (2014).

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Submitted: 2023-08-16 13:13:17 UTC

Published: 2023-08-18 00:39:47 UTC

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Information Sciences