A Bayesian network approach incorporating imputation of missing data enables exploratory analysis of complex causal biological relationships.

Autor: Howey R; Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom., Clark AD; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom., Naamane N; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom., Reynard LN; Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom., Pratt AG; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.; Musculoskeletal Services Directorate, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom., Cordell HJ; Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.
Jazyk: angličtina
Zdroj: PLoS genetics [PLoS Genet] 2021 Sep 29; Vol. 17 (9), pp. e1009811. Date of Electronic Publication: 2021 Sep 29 (Print Publication: 2021).
DOI: 10.1371/journal.pgen.1009811
Abstrakt: Bayesian networks can be used to identify possible causal relationships between variables based on their conditional dependencies and independencies, which can be particularly useful in complex biological scenarios with many measured variables. Here we propose two improvements to an existing method for Bayesian network analysis, designed to increase the power to detect potential causal relationships between variables (including potentially a mixture of both discrete and continuous variables). Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis. This can be wasteful and undesirable when there are many individuals with missing data, perhaps with only one or a few variables missing. This motivates the use of imputation. We present a new imputation method that uses a version of nearest neighbour imputation, whereby missing data from one individual is replaced with data from another individual, their nearest neighbour. For each individual with missing data, the subsets of variables to be used to select the nearest neighbour are chosen by sampling without replacement the complete data and estimating a best fit Bayesian network. We show that this approach leads to marked improvements in the recall and precision of directed edges in the final network identified, and we illustrate the approach through application to data from a recent study investigating the causal relationship between methylation and gene expression in early inflammatory arthritis patients. We also describe a second improvement in the form of a pseudo-Bayesian approach for upweighting certain network edges, which can be useful when there is prior evidence concerning their directions.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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