The Five-Gene-Network Data Analysis with Local Causal Discovery Algorithm Using Causal Bayesian Networks

• Published in: Annals of the New York Academy of Sciences 2009-03, Vol.1158 (1), p.93-101
• Main Authors: Yoo, Changwon, Brilz, Erik M.
• Format: Article
• Language: English
• Subjects: Algorithms; Bayes Theorem; causal Bayesian networks; causal discovery; Gene Expression Profiling; gene networks; Gene Regulatory Networks; high throughput data analysis; microarray data analysis; Models, Biological; Oligonucleotide Array Sequence Analysis; Statistics as Topic - methods
• ISSN: 0077-8923; 1749-6632; 1930-6547
• DOI: 10.1111/j.1749-6632.2008.03749.x

Using microarray experiments, we can model causal relationships of genes measured through mRNA expression levels. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, such as by knock out of some genes, with experiments of the system under no interventions. However, it is expensive and difficult to conduct wet lab experiments of complete interventions of genes in a biological system. Thus, it will be helpful if we can discover promising causal relationships among genes with no interventions or incomplete interventions, such as by applying a treatment that has unknown effects to modeled genes, in order to identify promising genes to perturb in the system that can later be verified in wet laboratories. In this paper we use causal Bayesian networks to implement a causal discovery algorithm—the equivalence local implicit latent variable scoring method (EquLIM)—that identifies promising causal relationships even with a small dataset generated from no or incomplete interventions. We then apply EquLIM to analyze the five‐gene‐network data and compare EquLIM's predictions with true causal pairwise relationships between the genes.