Preprint
Generating weighted and thresholded gene coexpression networks using signed distance correlation
- Abstract:
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Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming correlation values into unweighted networks may lead to a loss of important biological information related to the intensity of the correlation. Here introduce a principled method to construct weighted gene coexpression networks using signed distance correlation. These networks contain weighted edges only between those pairs of genes whose correlation value is higher than a given threshold. We analyse data from different organisms and find that networks generated with our method based on signed distance correlation are more stable and capture more biological information compared to networks obtained from Pearson correlation. Moreover, we show that signed distance correlation networks capture more biological information than unweighted networks based on the same metric. While we use biological data sets to illustrate the method, the approach is general and can be used to construct networks in other domains.
- Publication status:
- Published
- Peer review status:
- Not peer reviewed
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- Files:
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(Preview, Pre-print, pdf, 5.6MB, Terms of use)
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- Preprint server copy:
- 10.1101/2021.11.15.468627
Authors
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/T018445/1
- Funder identifier:
- https://ror.org/00cwqg982
- Grant:
- BB/T001801/1
- Funder identifier:
- https://ror.org/01bstzn19
- Preprint server:
- bioRxiv
- Publication date:
- 2021-11-16
- DOI:
- EISSN:
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2692-8205
- Language:
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English
- Keywords:
- Pubs id:
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1210686
- Local pid:
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pubs:1210686
- Deposit date:
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2026-04-20
- ARK identifier:
Terms of use
- Copyright holder:
- Pardo-Diaz et al
- Copyright date:
- 2021
- Rights statement:
- ©2021 The Authors. The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
- Licence:
- CC Attribution (CC BY)
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