ArticlePLoS computational biology2016
Utility and Limitations of Using Gene Expression Data to Identify Functional Associations.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 66 citations in OpenAlex.
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- Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes.Computational and structural biotechnology journal · 2023Article
- Integrative omics approaches for biosynthetic pathway discovery in plants.Natural product reports · 2022Review
- Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis.BMC bioinformatics · 2022Article
- Causal network inference from gene transcriptional time-series response to glucocorticoids.PLoS computational biology · 2021Article
- Subfunctionalization of Paralog Transcription Factors Contributes to Regulation of Alkaloid Pathway Branch Choice inFrontiers in plant science · 2021Article
- Stepwise target controllability identifies dysregulations of macrophage networks in multiple sclerosis.Network neuroscience (Cambridge, Mass.) · 2021Article
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- Putative cis-Regulatory Elements Predict Iron Deficiency Responses in Arabidopsis Roots.Plant physiology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.