Motivation: Large-scale biological experiments provide snapshots into the huge number of processes running in parallel within the organism. These processes depend on a large number of (hidden) (epi)genetic, social, environmental and other factors that are out of experimentalists' control. This makes it extremely difficult to identify the dominant processes and the elements involved in them based on a single experiment. It is therefore desirable to use multiple sets of experiments targeting the same phenomena while differing in some experimental parameters (hidden or controllable). Although such datasets are becoming increasingly common, their analysis is complicated by the fact that the various biological elements could be influenced by different sets of factors. Results: The central hypothesis of this article is that biologically related elements and processes are affected by changes in similar ways while unrelated ones are affected differently. Thus, the relations between related elements are more consistent across experiments. The method outlined here looks for groups of elements with robust intra-group relationships in the expectation that they are related. The major groups of elements may be identified in this way. The strengths of relationships per se are not valued, just their consistency. This represents a completely novel and unutilized source of information. In the analysis of time course microarray experiments, I found cell cycle- and ribosome-related genes to be the major groups. Despite not looking for these groups in particular, the identification of these genes rivals that of methods designed specifically for this purpose.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics