Investigating the effects of imputation methods for modelling gene networks using a dynamic Bayesian network from gene expression data

Lian En Chai, Chow Kuan Law, Mohd Saberi Mohamad, Chuii Khim Chong, Yee Wen Choon, Safaai Deris, Rosli Md Illias

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes.

Original languageEnglish
Pages (from-to)20-27
Number of pages8
JournalMalaysian Journal of Medical Sciences
Volume21
Issue number2
Publication statusPublished - Mar 2014
Externally publishedYes

Keywords

  • Bayesian method
  • DNA microarrays
  • Gene expression
  • Gene expression regulation
  • Gene regulatory networks

ASJC Scopus subject areas

  • General Medicine

Fingerprint

Dive into the research topics of 'Investigating the effects of imputation methods for modelling gene networks using a dynamic Bayesian network from gene expression data'. Together they form a unique fingerprint.

Cite this