Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges

By Purvesh Khatri, Marina Sirota, Atul J. Butte. 2012

1,495 citations recorded by the source corpus.

Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis.

Subfields: Molecular Biology

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Citations in the corpus

The Gene Ontology Resource: 20 years and still GOing strong (2018, methodology): “The most obvious example is its use in GO enrichment analysis, also often called pathway analysis (5).”
Systematic Functional Annotation and Visualization of Biological Networks (2016, methodology): “While functional regions have been discovered on a case-by-case basis (Khatri et al., 2012; Mitra et al., 2013), no method currently exists to identify them exhaustively.”
WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs (2019, methodology): “The size and color of the dot is proportional to the number of overlapping (for ORA) or leading edge genes (for GSEA) of the category.”
Constraint-based models predict metabolic and associated cellular functions (2014, background): “…the bases of thermodynamic feasibility 72 , the theoretical yield of BDO (which was determined using FBA), the number of known enzymes for the biochemical transformations (which was determined using pathway databases 73 ) and the topological distance of the pathway from central carbon metabolism.”
Detecting gene-gene interactions using a permutation-based random forest method (2016, methodology): “…to solve such problems, which include applying filter algorithms to reduce the number of SNPs in the analysis by removing redundant SNPs based on the needs, such as Spatially Uniform ReliefF (SURF), and doing pathway analysis to subset the SNP dataset based on similar biological functions [12, 13].”
KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis (2021, methodology): “It has been developed to utilize the additional topology information in a given network of pathway, which is ignored by the ORA and FCS methods (1).”
WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit (2017, methodology): “Neither ORA nor GSEA accounts for systems-level dependencies and interactions between genes.”
WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics (2024, methodology): “For users who are new to these methods, we recommend referring to previous publications from our team and other researchers ( 3 ,5 ).”
PathVisio 3: An Extendable Pathway Analysis Toolbox (2015, background): “While over-representation analysis and functional class scoring only consider the number of genes in the pathways, topology-based methods also look at the interactions between the elements in the pathways [30].”
Trials and tribulations of 'omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine. (2013, neutral): “22 Maximizing the statistical power, rather than minimizing the false positive rate, may be desirable for downstream analyses ( e.g. , pathway mapping, enrichment analysis 23 ).”
Big Data Analytics in Healthcare (2015, methodology): “Pathway analysis approaches do not attempt to make sense of high-throughput big data in biology as arising from the integrated operation of a dynamical system [25].”
GSVA: gene set variation analysis for microarray and RNA-Seq data (2013, methodology): “Also, rank-based methods ignore relative changes of genes in a pathway resulting in equal treatment of the genes, although theymight have different fold changes [50].”
STRING v9.1: protein-protein interaction networks, with increased coverage and integration (2012, background): “To address this question, a variety of dedicated online resources are already available (49,50), most notably the DAVID resource (51).”