The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics

Understanding the relationship between two or more variables is a driving motivation of many biological questions. The past several decades has seen a rapid increase in our ability to discern such relationships at multiple levels from molecular to cellular to whole populations. However, our ability to understand the relationships between different scales and different types of data is still limited [1].Here we introduce Cross Pattern Identification Technique (CRIT) as a means of integrating at least three matrices which do not all share the same index. The goal of CRIT is to systematically combine information from multiple tables with different indices allowing one to not only stack features in a single dimension but also to span across multiple ones. Thus, CRIT captures a new type of relationship between different types of data (for example drugs and their protein targets) which we term a 'cross pattern.' What is a cross pattern and how does this differ from the more traditional integration methods? There are two main differences: (1) It preserves the underlying structure of the individual datasets allowing for greater transparency and more importantly (2) it does not rely on a single index for querying. In other words, cross patterns are conceptually related to correlation but are not correlations as there is no obvious way to correlate two differently indexed objects. To better illustrate these differences, in Figure 1, we are given three pieces of information: the properties of a set of drugs, the properties of a set of proteins, and which drugs targeted which proteins. Our goal is to determine if there are any properties of drugs that are related to any property of the protein target. As a test query, in Figure 1b, we narrow our question to Which types of proteins are disrupted by aromatic drugs? Understanding these types of relationships could provide additional details about general mechanisms of drug-protein binding and how to design drugs to disrupt a parti