Analyzing microarray data with transitive directed acyclic graphs
Post hoc assignment of patterns determined by all pairwise comparisons in microarray experiments with multiple treatments has been proven to be useful in assessing treatment effects. We propose the usage of transitive directed acyclic graphs (tDAG) as the representation of these patterns and show that such representation can be useful in clustering treatment effects, annotating existing clustering methods, and analyzing sample sizes. Advantages of this approach include: (1) unique and descriptive meaning of each cluster in terms of how genes respond to all pairs of treatments; (2) insensitivity of the observed patterns to the number of genes analyzed; and (3) a combinatorial perspective to address the sample size problem by observing the rate of contractible tDAG as the number of replicates increases. The advantages and overall utility of the method in elaborating drug structure activity relationships are exemplified in a controlled study with real and simulated data. © 2009 Imperial College Press.
Journal of Bioinformatics and Computational Biology
Phan, V., Olusegun George, E., Tran, Q., & Goodwin, S. (2009). Analyzing microarray data with transitive directed acyclic graphs. Journal of Bioinformatics and Computational Biology, 7 (1), 135-156. https://doi.org/10.1142/S0219720009003972