Imputing metabolomics with graph denoising autoencoders

Metabolomics is an efficacious modality to extract impactful insights in numerous biomedical applications. Metabolomic datasets often exhibit significant sparsity containing many missing entries. Over the years, numerous metabolomic imputation approaches have been studied. Denoising autoencoders have proven powerful for analyzing noisy data in various domains. On the other hand, graph representations are very popular in biochemical research. In this work, we study the efficacy of graph denoising autoencoders (GDAEs) - the mechanism of which is an integration of denoising autoencoders with graph representations - for the imputation of metabolomic data, as this potential avenue is yet unexplored. We propose a GDAE-based metabolomics imputation approach and benchmark it on three metabolomic datasets comparing with the imputation quality of eight existing methods. We also benchmark the imputation methods based on their effect on downstream classification and clustering tasks. We simulate different patterns and proportions of missing entries in metabolomes and compare the level of difficulty of imputation for different missingness. We also inspect the effect of imputation quality on downstream performance. Based on the empirical evidence, we conclude that GDAE-based imputation is an impactful data preprocessing paradigm for metabolomic analyses.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Sarker, Kowshika

Contributors dc:contributor

• Zhai, ChengXiang

• Metabolomics
• Imputation
• Graph Denoising Autoencoder
• Graph Neural Network

Statement dc:rights

• Copyright 2024 Kowshika Sarker

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/127281

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F127281