Analysis and correction of crosstalk effects in pathway analysis

Abstract

Identifying the pathways that are significantly impacted in a given condition is a crucial step in understanding the underlying biological phenomena. All approaches currently available for this purpose calculate a P-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These P-values were previously thought to be independent. Here we show that this is not the case, and that many pathways can considerably affect each other's P-values through a ''crosstalk'' phenomenon. Although it is intuitive that various pathways could influence each other, the presence and extent of this phenomenon have not been rigorously studied and, most importantly, there is no currently available technique able to quantify the amount of such crosstalk. Here, we show that all three major categories of pathway analysis methods (enrichment analysis, functional class scoring, and topology-based methods) are severely influenced by crosstalk phenomena. Using real pathways and data, we show that in some cases pathways with significant P-values are not biologically meaningful, and that some biologically meaningful pathways with nonsignificant P-values become statistically significant when the crosstalk effects of other pathways are removed. We describe a technique able to detect, quantify, and correct crosstalk effects, as well as identify independent functional modules. We assessed this novel approach on data from four experiments involving three phenotypes and two species. This method is expected to allow a better understanding of individual experiment results, as well as a more refined definition of the existing signaling pathways for specific phenotypes.

Crosstalk analysis of pathways in breast cancer using a network model based on overlapping differentially expressed genes

“…Pathways can affect each other through a phenomenon known as crosstalk, rather than acting independently (10). Although it is evident that different pathways could influence each other, particularly when there is an overlap of DE genes having significant changes in expression level, the presence and extent of this phenomenon have not been rigorously studied.…”

Quantitative proteomic analyses of mammary organoids reveals distinct signatures after exposure to environmental chemicals

“…To determine whether a subset of proteins altered by exposure could sensitize mammary epithelium to transformation, differentiation, and other premalignant processes, we took an approach that considers the direction and type of all signals in a pathway and identified the biological processes characteristic of the proteins altered by each chemical (18,19). The Elim pruning method, which iteratively removes the genes mapped to a significant gene ontology (GO) term from more general (higher level) GO terms was used to overcome the limitation of errors introduced by considering genes multiple times (20).…”

Revealing pathway cross-talk related to diabetes mellitus by Monte Carlo Cross-Validation analysis

“…More importantly, identification of cross-talk can provide additional functional information for data interpretation and facilitates a better understanding of the connections between two pathways. However, currently there are no reliable methods utilized to quantify cross-talk for paired pathways [10]. Significantly, the combining of DEGs and pathway data with Monte Carlo cross-validation has been considered to quantify the cross-talk between the paired pathways [11].…”