Analysis of Nonlinear Relations between Expression Profiles by the Principal Curves of Oriented-Points Approach

摘要

DNA microarray technology enables high-throughput gene expression analysis and allows researchers to test the activity of thousands of genes at one time in multiple cellular conditions. This approach is based on principal curves of oriented points (PCOP) analysis and minimum spanning trees to analyze temporal and nontemporal series data to relate the genes. PCOP is a very suitable method, non–hypothesis-driven, for nonlinear relationship recognition between multivariable sets of data. Initially, a gene-relations tree is generated from the correlation between each pair of genes, calculated by PCOP analysis. Next, the researcher can introduce the query genes to be studied into the zoom-in operation, and the system selects the genes which connect the previously provided ones, beyond the activation pathways, using the minimum spanning tree. Thus, this zoom-in operation generates the nonlinear pattern of the intraset expression behavior for the new gene set. This inner expression pattern relatesthe query and selected genes to study their mutual interdependence in detail. This detailed information is especially useful in the biomedical environment, where such information is not possible to obtain by applying the current analytical methods.