Imputing metabolomics with graph denoising autoencoders
Sarker, Kowshika
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https://hdl.handle.net/2142/127281
Description
Title
Imputing metabolomics with graph denoising autoencoders
Author(s)
Sarker, Kowshika
Issue Date
2024-12-09
Director of Research (if dissertation) or Advisor (if thesis)
Zhai, ChengXiang
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Metabolomics
Imputation
Graph Denoising Autoencoder
Graph Neural Network
Language
eng
Abstract
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.
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