FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods

Abstract

Background Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways. Results In this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes. Conclusions The results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of “Renin-angiotensin system” pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far. Electronic supplementary material The online version of this article (10.1186/s12859-019-2635-6) contains supplementary material, which is available to authorized users.

BNrich: A Bayesian network approach to the pathway enrichment analysis

“…It is worth mentioning that BLMA is able to analyze several datasets simultaneously. Finally, the signaling pathway impact analysis (SPIA) method combines the features of the classical enrichment analysis with measurements of the actual perturbation on a given pathway under a given condition [12];although this method is not new, it is still considered as a benchmark to examine new PEA methods [16,51,52].…”

“…Consistency measures the number of overlapping nodes and edges among independent datasets under similar studying conditions, at a given rank threshold (top 10, 20… 70 significant pathways) [19,52]. We conduct consistency analysis on the last four datasets shown in Table1, related to colon cancer.…”

“…We conduct consistency analysis on the last four datasets shown in Table1, related to colon cancer. This analysis has been performed in two phases: consistency analysis among four datasets and consistency analysis along dataset GSE44076 [52] by resampling 70% of data without replacement.…”

Analysing the meta-interaction between pathways by gene set topological impact analysis

“…As a result, these algorithms are able to reduce the false negative rate, but not the false positive rate, of the pathway analysis results. Additionally, along with other crosstalk analysis software such as BinoX [ 13 ] and FoPA [ 14 ], they are unable to assess the upstream/downstream relationships..…”