Functional regression clustering with multiple functional gene expressions.
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 dev...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0310991 |
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| _version_ | 1846149056979533824 |
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| author | Susana Conde Shahin Tavakoli Daphne Ezer |
| author_facet | Susana Conde Shahin Tavakoli Daphne Ezer |
| author_sort | Susana Conde |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cbbfa6a42ea74439843e0af9a523fcd4 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-cbbfa6a42ea74439843e0af9a523fcd42024-11-30T05:31:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011911e031099110.1371/journal.pone.0310991Functional regression clustering with multiple functional gene expressions.Susana CondeShahin TavakoliDaphne EzerGene 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.https://doi.org/10.1371/journal.pone.0310991 |
| spellingShingle | Susana Conde Shahin Tavakoli Daphne Ezer Functional regression clustering with multiple functional gene expressions. PLoS ONE |
| title | Functional regression clustering with multiple functional gene expressions. |
| title_full | Functional regression clustering with multiple functional gene expressions. |
| title_fullStr | Functional regression clustering with multiple functional gene expressions. |
| title_full_unstemmed | Functional regression clustering with multiple functional gene expressions. |
| title_short | Functional regression clustering with multiple functional gene expressions. |
| title_sort | functional regression clustering with multiple functional gene expressions |
| url | https://doi.org/10.1371/journal.pone.0310991 |
| work_keys_str_mv | AT susanaconde functionalregressionclusteringwithmultiplefunctionalgeneexpressions AT shahintavakoli functionalregressionclusteringwithmultiplefunctionalgeneexpressions AT daphneezer functionalregressionclusteringwithmultiplefunctionalgeneexpressions |