1 uses
1.1 pathways databases
1.2 methods , software
1.2.1 over-representation analysis or enrichment analysis (ora)
1.2.2 functional class scoring (fcs)
1.2.3 pathway topology (pt)
uses
the data pathway analysis come high throughput biology. includes high throughput sequencing data , microarray data. before pathway analysis can done, omics data should normalized, , genes should ranked differential expression of student s t-test, anova or other statistics. in general, list of statistical ranked genes can analyzed pathway analysis. example, functional activity of proteins can inferred using network enrichment analysis of genes deferentially expressed in experiment. such functional activity scores can used pathway analysis find pathways responsible observed differential expression. in case when ranking not available list of genes can analyzed. possible integrate multiple microarray data sets different research groups meta-analysis , cross-platform normalization. using pathway analysis software, researchers can determine gene groups such pathways, cell processes or diseases enriched on , under expressed in experimental data genes. can infer associated upstream , downstream regulators, proteins, small molecules, drugs, etc. example, pathway analysis of several independent microarray experiments (meta-analysis) helped discover potential biomarkers in single pathway important fast-to-slow switch fiber type transition in duchenne muscular dystrophy. in other study meta-analysis identified 2 biomarkers in blood of patients parkinson s disease, can useful monitoring disease.
pathways databases
pathway analysis needs knowledge base pathway collection , interaction networks. pathway collections content, structure , functionality vary in different sources. examples of popular free public pathway collections kegg , reactome. there commercial pathways collections such pathway studio pathways , ipa pathways.
methods , software
pathway analysis software can divided web-based applications, desktop programs , programming packages. programming packages coded in r , python languages, , shared openly through bioconductor , github projects. different methods of pathway analysis evolve fast, classification of these methods still discussable. there 3 main groups of methods in pathway analysis according to: ora, fsc , pt.
over-representation analysis or enrichment analysis (ora)
this method measures percentage of genes in pathway or gene group (gene ontology (go) groups, protein families, pathways) have differential expression. aim of ora list of relevant pathways, ordered in accordance p-value. basic hypothesis in ora relevant pathways can identified number of genes differently expressed in experiment pathways contain. statistical significance of overlap between genes pathway , list of differently expressed genes determined such statistical tests fisher s exact test, hypergeometric distribution test or jaccard index.
functional class scoring (fcs)
this method analyzes expression change of overall genes in list (not ranking statistical significance or else) of differently expressed in experiment genes. fcs discards ora cut-off threshold limitation. aim of fcs evaluate differently expressed genes enrichment scores (see gene set enrichment) using pathways gene sets perform computations. 1 of first , popular methods deploying fcs approach gene set enrichment analysis (gsea).
pathway topology (pt)
pathway topology same fcs, except pt uses gene-level statistics through different databases integration. critical difference leveraging information role, position, , direction of interaction pathway database, pt able re-score significance of pathway linkages change, whereas fcs provide same score. examples pt approaches include signaling pathway impact analysis (spia), enrichnet, ggea, , topogsa.
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