Cluster-Partial Least Squares (c-PLS) regression analysis: Application to miRNA and metabolomic data.
Journal
Analytica chimica acta
ISSN: 1873-4324
Titre abrégé: Anal Chim Acta
Pays: Netherlands
ID NLM: 0370534
Informations de publication
Date de publication:
15 Jan 2024
15 Jan 2024
Historique:
received:
13
10
2023
accepted:
20
11
2023
medline:
6
12
2023
pubmed:
5
12
2023
entrez:
4
12
2023
Statut:
ppublish
Résumé
Biomedicine and biological research frequently involve analyzing large datasets generated by high-throughput technologies like genomics, transcriptomics, miRNomics, and metabolomics. Pathway analysis is a common computational approach used to understand the impact of experimental conditions, phenotypes, or interventions on biological pathways and networks. This involves statistical analysis of omic data to identify differentially expressed variables and mapping them onto predefined pathways. Analyzing such datasets often requires multivariate techniques to extract meaningful insights such as Partial Least Squares (PLS). Variable selection strategies like interval-PLS (iPLS) help improve understanding and predictive performance by identifying informative variables or intervals. However, iPLS is suboptimal to treat omic data such as metabolic or miRNA profiles, where features cannot be distributed along a continuous dimension describing their relationships as in e.g., vibrational or nuclear magnetic resonance spectroscopy. This study introduces a novel variable selection approach called cluster PLS (c-PLS) that aims to assess the joint impact of variable groups selected based on biological characteristics (such as miRNA-regulated metabolic pathway or lipid classes) on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using miRNomic and metabolomic datasets obtained from the analysis of 24 liver tissue biopsies collected in the frame of a clinical study of steatosis. Results obtained show that c-PLS enables analyzing the effect of biologically relevant variable clusters, facilitating the identification of biological processes associated with the independent variable, and the prioritization of the biological factors influencing model performance, thereby improving the understanding of the biological factors driving model predictions. While the strategy is tested for the evaluation of PLS models, it could be extended to other linear and non-linear multivariate models.
Sections du résumé
BACKGROUND
BACKGROUND
Biomedicine and biological research frequently involve analyzing large datasets generated by high-throughput technologies like genomics, transcriptomics, miRNomics, and metabolomics. Pathway analysis is a common computational approach used to understand the impact of experimental conditions, phenotypes, or interventions on biological pathways and networks. This involves statistical analysis of omic data to identify differentially expressed variables and mapping them onto predefined pathways. Analyzing such datasets often requires multivariate techniques to extract meaningful insights such as Partial Least Squares (PLS). Variable selection strategies like interval-PLS (iPLS) help improve understanding and predictive performance by identifying informative variables or intervals. However, iPLS is suboptimal to treat omic data such as metabolic or miRNA profiles, where features cannot be distributed along a continuous dimension describing their relationships as in e.g., vibrational or nuclear magnetic resonance spectroscopy.
RESULTS
RESULTS
This study introduces a novel variable selection approach called cluster PLS (c-PLS) that aims to assess the joint impact of variable groups selected based on biological characteristics (such as miRNA-regulated metabolic pathway or lipid classes) on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using miRNomic and metabolomic datasets obtained from the analysis of 24 liver tissue biopsies collected in the frame of a clinical study of steatosis.
SIGNIFICANCE AND NOVELTY
UNASSIGNED
Results obtained show that c-PLS enables analyzing the effect of biologically relevant variable clusters, facilitating the identification of biological processes associated with the independent variable, and the prioritization of the biological factors influencing model performance, thereby improving the understanding of the biological factors driving model predictions. While the strategy is tested for the evaluation of PLS models, it could be extended to other linear and non-linear multivariate models.
Identifiants
pubmed: 38049234
pii: S0003-2670(23)01273-4
doi: 10.1016/j.aca.2023.342052
pii:
doi:
Substances chimiques
MicroRNAs
0
Biological Factors
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
342052Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.