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Pathways

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Kevin S edited this page Aug 31, 2017 · 2 revisions

Pathway analyses are done using fold-change values returned by limma or DESeq2. Since there are fold-change values for each comparison, so pathway analysis can be conducted on each comparison. Note that pathway analysis uses fold-change values of all genes and hence is independent of the selected DEGs.

Pathway analysis can be performed using several methods. GSEA (Gene Set Enrichment Analysis) (Subramanian et al., 2005) is conducted in the pre-ranked mode using a recent faster algorithm based on the fgsea package (Sergushichev, 2016). PAGE (Parametric Analysis of Gene Set Enrichment) (Kim and Volsky, 2005) is used as implemented in PGSEA package(Furge and Dykema, 2012). For PGSEA there are two versions one only analyzes the selected comparisons and another option (“PGSEA w/ all samples”) enables the user to analyze all sample groups.

Unlike all of these methods rely on the built-in gene set databases, ReactomePA (Reactome Pathway Analysis) (Yu and He, 2016) retrieves gene sets from Reactome (Fabregat et al., 2016; Yu and He, 2016)

Gene expression data can be visualized on KEGG pathway diagrams (Kanehisa et al., 2017) using Pathview (Luo and Brouwer, 2013). Note that Pathview download pathway diagrams directly from KEGG website and thus is slow.

On the lower left side of the screen, there is check box named “Use absolute values of fold changes for GSEA and GAGE”. This is useful as some molecular pathways can be modulated by up-regulating some genes while down- regulating others. This is especially useful when using KEGG pathways. For others genes sets such as TF target genes, microRNA target genes where we know the regulation is one-directional, we should not check this box.

R code used for GAGE using the gage package:

paths <- gage(fold, gsets = gmt, ref = NULL, samp = NULL)

R code for GSEA via fgsea pckage:

paths <- fgsea(pathways = gmt,
stats = fold,
minSize=input$minSetSize,
maxSize=input$maxSetSize,
nperm=5000)

R code for PAGE using the PGSEA package:

pg= PGSEA (convertedData – rowMeans(convertedData), cl=gmt, range=myrange, p.value=TRUE, weighted=FALSE)

R code for ReactomePA:

paths <- gsePathway(fold, nPerm=5000, organism = ReactomePASpecies[ix],
minGSSize= input$minSetSize,
maxGSSize= input$maxSetSize,
pvalueCutoff=0.5,
pAdjustMethod=”BH”, verbose=FALSE)

References:

  • Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016). The Reactome pathway Knowledgebase. Nucleic Acids Res 44, D481-487.

  • Furge, K., and Dykema, K. (2012). PGSEA: Parametric Gene Set Enrichment Analysis. R package version 1480.

  • Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., and Morishima, K. (2017). KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45, D353-D361.

  • Kim, S.Y., and Volsky, D.J. (2005). PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 6, 144.

  • Luo, W., and Brouwer, C. (2013). Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29, 1830-1831.

  • Sergushichev, A. (2016). An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv http://biorxiv.org/content/early/2016/06/20/060012.

  • Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550.

  • Yu, G., and He, Q.Y. (2016). ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol Biosyst 12, 477-479.

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