disCBpath

disCBpath is a computational method used in biological research, specifically for analyzing gene expression data. It focuses on identifying differentially co-expressed gene pairs, which are pairs of genes whose expression levels tend to change together across different biological conditions. This approach helps researchers understand how genes coordinate their activity to perform specific functions within a cell.

The method works by first calculating correlation coefficients between all possible pairs of genes in a dataset. These datasets typically come from experiments that measure the activity of thousands of genes simultaneously, such as RNA sequencing. Next, disCBpath compares these correlation coefficients between two or more conditions, for example, comparing gene expression in healthy tissue versus diseased tissue.

The core idea is to identify gene pairs whose correlation changes significantly between these conditions. A pair that is highly correlated in one condition but not in another might indicate a disruption in their coordinated regulatory mechanism. These disruptions can be clues to underlying biological processes, such as the development of a disease or a response to a treatment.

By pinpointing these differentially co-expressed gene pairs, disCBpath can help in constructing gene regulatory networks or identifying pathways that are altered in specific biological states. This information can then be used to generate hypotheses for further experimental validation and to gain a deeper understanding of complex biological systems. The method is implemented in software that is accessible to researchers in the field.