In biology, researchers often seek to understand the factors that underpin a particular cellular process or diseased state. Thus, this line of thinking necessarily result in experiment design that seek to correlate molecular events with cellular processes, which could be readout experimentally, either through microscopy techniques or enzymatic biochemical assays. More generally, co-expression analysis is a form of correlation analysis that seeks to identify two parameters or events that occur together, and are connected sequentially.
With the advent of molecular tools, the idea to seek correlation at the molecular level between genes and proteins, and between genes have drove the development of various analysis and experimental tools useful for understanding how expression of one gene or protein is related to another at the genome or proteome level. This is thus the essence of co-expression analysis, a powerful molecular tool for correlating expression levels of different genes and proteins in different scenarios in health and disease.
Originally performed in a microarray format but which utilizes gene sequencing now, co-expression analysis affords a glimpse of how particular genes may be differentially co-expressed with other genes in different cellular processes or disease states. Given that co-expression of genes in biology portend possible physiological and mechanistic significance, extraction of such pairs or sets of co-expressed genes provide the incision points for unlocking a problem or subsequent analysis.
However, co-expression of genes or proteins could also be due to chance, and without biological significance. Thus, other analysis tools are used for screening the biologically relevant pairs of proteins or genes obtained from co-expression analysis. But, in using logic to understand the relevance of each pair of genes or proteins uncovered by co-expression analysis, care must also be taken not to discount possible hitherto unknown relationship between proteins and genes distantly related in metabolic or signalling processes.
Collectively, co-expression analysis finds utility in uncovering gene or protein sets differentially co-expressed with each other in various cellular processes and diseased states. Such information provides the incision points for further research seeking to understand the molecular logic surrounding a cellular process or diseased state. However, cases also exist where co-expression of genes and proteins occur due to their roles as housekeeping genes or proteins. Thus, similar to other branches of science, correlation does not suggest causation.
Category: biochemistry, molecular biology, cell biology, biotechnology, bioengineering, genetics, genomics,
Tags: co-expression analysis, microarray, genome, proteome, cellular processes, diseased states, gene sequencing,