ArticlePloS one2015
A null model for Pearson coexpression networks.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Molecular Transducers of Human Skeletal Muscle Remodeling under Different Loading States.Cell reports · 2020Trial
- Coexpression network revealing the plasticity and robustness of population transcriptome during the initial stage of domesticating energy crop Miscanthus lutarioriparius.Plant molecular biology · 2018Article
- From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data.EURASIP journal on bioinformatics & systems biology · 2017Review
- Metric projection for dynamic multiplex networks.Heliyon · 2016Article
- DTW-MIC Coexpression Networks from Time-Course Data.PloS one · 2016Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges.
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