Tool: pathfindR - Enrichment Analysis using Active Subnetworks
14
gravatar for egeulgen
21 months ago by
egeulgen960
Istanbul
egeulgen960 wrote:

Hello all,

I would like to introduce our group's active-subnetwork-oriented enrichment R package: pathfindR (latest release is v1.4.0).

This tool is designed to improve enrichment analysis by firstly identifying active subnetworks in differential expression/methylation data using a protein-protein interaction network. It then performs enrichment analysis (Over-Representation Analysis). By utilizing the interaction information, pathfindR identifies most of the key involved processes.

  • pathfindR provides the human gene sets KEGG, Reactome, BioCarta, GO-MF, GO-BP, GO-CC and GO-All (BP+MF+CC) and the M.musculus KEGG mmu_KEGG gene set for enrichment analysis. The user can also use any other custom gene sets.
  • pathfindR also creates pathway diagrams for hsa-KEGG with the involved genes colored by change values or visualizations of interactions of genes involved in the pathways and interaction graphs for other gene sets, displaying the interactions of involved genes
  • The package allows for clustering of enriched terms and establishment of representative terms. This allows for further abstraction and reduces the complexity of analysis. The available clustering approaches are hierarchical and fuzzy.
  • pathfindR provides functionality to score terms for individual samples and plot a heat map of the scores. This allows the user to investigate how a gene set is altered in a given sample (or in cases vs. controls).
  • the package also offers a term-gene interaction graph visualizing terms and term-related genes as a graph to determine the degree of overlap between the enriched terms by identifying shared and/or distinct significant genes

You can install the released version of pathfindR from CRANvia:

install.packages("pathfindR")

And the development version from GitHub via:

install.packages("devtools") # if you have not installed this
devtools::install_github("egeulgen/pathfindR")

You can read more about the method, its example applications on 3 different datasets and comparison with other available methods in our 2019 article.

Find out more in the GitHub wiki and check out the pathfindR tutorial.

ADD COMMENTlink modified 28 days ago • written 21 months ago by egeulgen960
3

It's useful.
1. Why not to bioconductor, containing lots of bioinformatics R packages instead of CRAN?
2. no examples of pathmap in the vignette.
3. An enhancement: Visualise the results of run_pathfindR, besides the table show.

Thank you.

ADD REPLYlink modified 18 months ago by zx87548.7k • written 18 months ago by Zhilong Jia1.5k
1

Thank you for the feedback! To answer them:

  1. We did not think it would make any difference
  2. We will try to include an example for pathmap in the next version
  3. The pathways are already visualized and can be viewed through the HTML reports. Would you expand on how we could provide more visualizations?

Thanks again,

-E

ADD REPLYlink modified 18 months ago • written 18 months ago by egeulgen960
  1. Precision advertising. The targeting users of pathfindR are bioinformaticians, which are the users of Bioconductor, while the users of CRAN is R users. Getting more chance to encounter target audiences of pathfindR via Bioconductor. Anyway, not a problem.
  2. I did not check the HTML report as I cannot run the examples after installing pathfindR and filed an issue at Github. I mean the pathway enrichment itself, not visualization for each pathway based on pathview.
ADD REPLYlink written 18 months ago by Zhilong Jia1.5k

We’re looking into your issue on Github. We will try to include a visualization of the enrichment results in the next version (considering a bubble chart but feel free to share any ideas of what you had in mind).

ADD REPLYlink modified 18 months ago • written 18 months ago by egeulgen960

I will second the idea of a bubble chart. This is one of the easier/better GO packages to use that I've tried, but it lacks the visualization options of some others. Keep up the good work.

ADD REPLYlink written 15 months ago by jared.andrews074.1k

Can you please give a tutorial by taking some examples of a gene.

ADD REPLYlink modified 21 months ago • written 21 months ago by kalyanimeha30
2

You can see an example application in the vignette (browseVignettes("pathfindr_vignette")). We also have a wiki.

ADD REPLYlink modified 18 months ago • written 21 months ago by egeulgen960

HI This is a great package. I'm having a little trouble with the pathway scoring part of the tutorial. I'm not sure how to create the RA_exp_mat for my data?

thanks again

ADD REPLYlink written 16 months ago by amgodogma0

The expression matrix for the pathway scores is the same matrix as the one you use for differential expression analysis. Columns are samples, rows are genes and the values are expression values.

ADD REPLYlink written 16 months ago by egeulgen960
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