Kontio JAJ, Pyhäjärvi T, Sillanpää MJ (2021) Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways. PLoS Comput Biol 17(5): e1008960. https://doi.org/10.1371/journal.pcbi.1008960
Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways
|Author:||Kontio, Juho A. J.1; Pyhäjärvi, Tanja2,3; Sillanpää, Mikko J.1|
1Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
2Department of Ecology and Genetics, University of Oulu, Oulu, Finland
3Department of Forest Sciences, University of Helsinki, Helsinki, Finland
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021061036463
Public Library of Science,
|Publish Date:|| 2021-06-10
A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method’s high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework’s versatility and broad practical relevance.
PLoS computational biology
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
112 Statistics and probability
1184 Genetics, developmental biology, physiology
JK was supported by the Finnish Academy of Science and Letters, Vilho, Yrjö and Kalle Väisälä Foundation grant nr. 190030 URL https://www.acadsci.fi/apurahat-ja-palkinnot/haettavat-apurahat/vaisalan-rahasto.html and Biocenter Oulu URL https://www.oulu.fi/biocenter/. TP acknowledges Academy of Finland grant nr. 287431 URL https://www.aka.fi/en/. MJS acknowledges Academy of Finland (PROFI5 HiDyn) grant nr. 326291 URL https://www.aka.fi/en/ and is supported by the Infotech Oulu research institute (https://www.oulu.fi/infotech/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
|Academy of Finland Grant Number:||
287431 (Academy of Finland Funding decision)
© 2021 Kontio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.