Ezer, Daphne, Tavakoli, Shahin and Conde, Susana Maria (2024) Functional regression clustering with multiple functional gene expressions. PLoS ONE. e0310991. ISSN 1932-6203
Abstract
Gene expression data is often collected in time series experiments, under different experimental conditions. There may be genes that have very different gene expression profiles over time, but that adjust their gene expression patterns in the same way under experimental conditions. Our aim is to develop a method that finds clusters of genes in which the relationship between these temporal gene expression profiles are similar to one another, even if the individual temporal gene expression profiles differ. We propose a K-means-type algorithm in which each cluster is defined by a function-on-function regression model, which, inter alia, allows for multiple functional explanatory variables. We validate this novel approach through extensive simulations and then apply it to identify groups of genes whose diurnal expression pattern is perturbed by the season in a similar way. Our clusters are enriched for genes with similar biological functions, including one cluster enriched in both photosynthesis-related functions and polysomal ribosomes, which shows that our method provides useful and novel biological insights.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Conde et al. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Biology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 28 Nov 2024 11:40 |
Last Modified: | 14 Feb 2025 00:13 |
Published Version: | https://doi.org/10.1371/journal.pone.0310991 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pone.0310991 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220219 |
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Filename: journal.pone.0310991.pdf
Description: Functional regression clustering with multiple functional gene expressions
Licence: CC-BY 2.5