Pathway analysis stands as an eminent and extensively employed suite of methodologies within the realm of life sciences research, offering researchers a profound avenue to delve into the intricate interplays and functionalities of genes within biological pathways. This multifaceted approach encompasses the discernment and scrutiny of discrete protein functionalities, biological pathways, and tangible interactions that proliferate within a specific assemblage or cohort of genes. By meticulously scrutinizing the intricate web of associations and interactions amongst genes encompassed within a pathway, researchers are equipped to foster a heightened comprehension of the underlying mechanisms and biological processes that govern them. The data requisite for conducting pathway analysis are procured from esteemed databases, prominently including the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and STRING. These information repositories proffer an extensive repertoire of data pertaining to gene functionality, molecular interactions, and the pathways intricately involved in diverse biological processes.
Pathway analysis is based on the term "omics," which refers to the comprehensive analysis of biological molecules such as DNA, RNA, proteins, and metabolites. The field of genomics encompasses several disciplines, including transcriptomics, proteomics, metabolomics, and epigenomics, each of which focuses on specific aspects of molecular analysis.
Systems biology provides a framework for integrating diverse biological information to gain a holistic understanding of biological phenomena. Systems biology aims to develop predictive mathematical models that capture the behavior of biological systems, combining information from different histological disciplines.
Pathways as Functional Biological Units
Pathways are best represented as networks, where nodes represent biological components and edges represent relationships between components. This network-based representation captures the complex connections and interactions between pathway components. Visualizing pathways as networks have become a widely used method for describing regulatory, metabolic, and signaling processes.
Pathways as Informatics Units
The pathway concept has been transferred from a literature scheme to an informatics framework in the form of pathway data files that compile information from different sources into a single model. Pathway data can be broadly categorized into two different types: gene sets and pathway topologies. Gene sets are simple lists of biological components in which the entities listed share a common biological theme. In contrast, pathway topologies not only list the components of a pathway but also describe their interactions, showing who interacts with whom and how these interactions occur, thus turning into a more detailed description of how the molecular composition of the pathway proceeds.
Pathway Databases (PDBs)
Efforts to build knowledge of pathway biology have led to the creation of pathway databases (PDBs), which condense current biological knowledge of molecular interactions in collections of pathway data. PDBs typically retrieve and build data from a variety of sources. The current catalog of pathway databases is indeed rich and diverse, covering differences in species focus, management methods, pathway types, and interactions covered.
Fig. 1. Pathway data types. (García-Campos M A, et al., 2015)
Overrepresentation analysis (ORA) methods aim to identify pathways that are significantly enriched for differentially expressed genes or proteins compared to what would be expected by chance alone. These tools (e.g., GoMiner and WebGestalt) use statistical tests (e.g., hypergeometric distributions and Fisher's exact test) to determine pathway enrichment. They provide researchers with ranked lists of pathways, highlighting those most likely to be biologically relevant.
Functional Class Scoring
Functional Class Scoring (FCS) methods go beyond basic overexpression analysis to consider not only the enrichment of genes within a pathway but also their coordinated changes in expression levels. These methods utilize all available measurements in a high-throughput dataset to calculate pathway-level statistics that capture the overall functional impact of the pathway.
Gene set enrichment analysis (GSEA) is a well-known FCS method that ranks pathways based on enrichment scores. GSEA employs a gene-ranking algorithm to determine whether the distribution of gene sets (pathways) in a gene-ranked list shows a statistically significant difference. By integrating pathway topology information, GSEA can identify subtle but coordinated changes in pathway activity, even though individual genes may not meet strict significance thresholds. Explore with our gene set enrichment analysis service for more information.
Pathway Topology-based Analysis
To further enhance the understanding of complex biological systems, pathway topology-based (PTB) analysis methods consider network interactions within pathways. These methods utilize the rich pathway topology information available in databases such as KEGG and Reactome to investigate causal relationships and interdependencies between pathway components. Pathway-Express and Signaling Pathway Impact Analysis (SPIA) are well-known PTB tools.
Fig. 2. Common Outputs of pathway analysis tools. (García-Campos M A, et al., 2015)
Pathway analysis and gene set analysis are two distinct approaches employed for the analysis and interpretation of gene lists derived from histology experiments. While both methods serve the purpose of scrutinizing such gene lists, they diverge in fundamental ways, as outlined below:
Gene set analysis entails comparing a given gene list to a predefined collection or set of genes, devoid of accounting for the intricate interplay and relationships that exist between genes. The primary objective of this approach is to determine whether a gene set demonstrates significant overrepresentation within an experimental gene list. Although gene set analysis proves valuable in identifying functional themes or biological processes associated with gene lists, it may overlook the intricate mechanisms that govern pathway intricacies.
Conversely, pathway analysis goes beyond mere gene-gene associations and delves into the structural aspects, interactions, and dependencies encompassed within a biological pathway. It encompasses the specific functions, biological processes, and physical interactions captured by pathways, thereby providing a more granular and mechanistic comprehension of gene lists. Equipped with pathway analysis tools, researchers gain the ability to explore intricate relationships and signaling pathways entangled within diverse biological phenomena.
Pathway analysis holds particular significance within various research scenarios that necessitate a comprehensive understanding of gene function and biological processes. Here are notable situations where pathway analysis emerges as a critical tool:
Interpreting High-throughput Data
Pathway analysis assumes a prominent role in the analysis of high-throughput biological data, such as transcriptomics or proteomics data. It aids researchers in comprehending the extensive gene lists generated by these experiments, enabling the identification of enriched biological pathways and shedding light on the underlying biological mechanisms.
In biomarker discovery studies, pathway analysis proves invaluable. By analyzing gene expression data across different disease states or conditions, pathway analysis facilitates the identification of significantly dysregulated pathways, unveiling potential biomarkers, and elucidating the molecular mechanisms underlying observed phenotypes.
Understanding Disease Mechanisms
Pathway analysis serves as a potent tool for unraveling the intricate molecular mechanisms implicated in various diseases. By amalgamating genetic and transcriptomic data with pathway information, researchers can pinpoint key pathways that undergo dysregulation during disease progression, thereby attaining insights into the underlying pathological processes. This knowledge paves the way for the development of targeted therapies and precision medicine approaches.
Drug Discovery and Target Identification
Pathway analysis assumes a pivotal role in drug discovery endeavors by illuminating relevant biological pathways and potential drug targets. Through the analysis of gene expression profiles derived from drug-treated samples, pathway analysis identifies pathways that undergo significant regulation due to drugs, thereby providing valuable information for target identification and an improved understanding of drug modes of action.
In conclusion, pathway analysis constitutes an extensively utilized arsenal of tools within the domain of life sciences research, empowering researchers to glean insights into gene function, biological pathways, and their intricate interactions. As opposed to gene set analysis methods, pathway analysis furnishes a more comprehensive and mechanistic understanding of gene lists. Pathway analysis finds particular utility in scenarios such as interpreting high-throughput data, biomarker discovery, comprehending disease mechanisms, and driving drug discovery efforts. By harnessing the capabilities offered by pathway analysis tools, researchers can unravel the complexity inherent in biological processes and enhance our understanding of diverse biological phenomena.