Whether you’re comparing disease to normal tissue, tumor samples with and without drug treatment, cultured cells before and after exposure to environmental stimuli, or any number of other experimental conditions, measured changes in the pattern of gene expression between experimental and control samples can provide a view into what is happening in the cell. But how do you make sense of the data, translating measured expression values into evidence for the biological mechanism at work?
There are two ways to approach analysis of differential gene expression: through traditional downstream analysis approaches and through the more recently described upstream analysis approach.
Since 2008, BIOBASE has been spreading the word about the added value offered by upstream analysis compared to traditional downstream analysis alone, but why is upstream analysis so important?
Traditional downstream analysis looks at enrichment of functional categories within differentially expressed gene sets—categories that include Gene Ontology’s molecule function, biological process and cellular component, disease-associated genes, biomarkers and therapeutic targets, and signaling pathways. Further methods search for network modules or cluster co-regulated genes over a time course.
Downstream methods rely solely on the subset of genes that are differentially expressed—genes that provide evidence of the effect, much like ripples in a lake provide evidence of the effect of the stone that penetrated the surface—but which do not themselves necessarily identify the cause of the differential gene expression.
What if the causal molecule, the stone, is not differentially expressed? What if, for example, increased activity of a growth factor sets off a signaling cascade, but the gene expression of the growth factor itself, or of components of its pathway, do not change? In such situations the causal signal can be completely lost when looking at differentially expressed genes only.