gsea-web

Intro

This is is a versatile tool for running (preranked) Gene Set Enrichment Analysis (GSEA) across combinations of ranks and gene sets. It mainly consists of a series of wrapper functions to perform the analysis and sift through the results.

clone the repository:

git clone https://github.com/asalt/bcm-gsea

Features

R Analysis: The main analysis is performed using R, with fgsea for fast gene set enrichment. Python Integration: Python manages the database, hosts APIs, and stores results. This component is still under development. Testing: Comprehensive testing using pytest for Python and testthat for R. Visualization: Utilizes ggplot2 for high-quality plots.

Script main entry points are driven by python command line parsing and variable dispatch.

## bcm-gsea example help

~/t/bcm-gsea> bcm-gsea run --help
Usage: bcm-gsea run [OPTIONS]

Options:
  -c, --config FILE               .toml file with additional parameters for
                                  report
  -i, --interactive               run in interactive session within python
                                  rpy2
  -v, --verbose                   verbose output
  -l, --log_level [DEBUG|INFO|WARNING|ERROR|CRITICAL]
                                  log level
  --help                          Show this message and exit.

command line interface is here:

./python/cli.py

R:

data loading
analysis
results routing

python :

database management
API hosting
results storage in db

test everything with test.sh (no .bat file yet) python testing with pytest ./python/tests/ R tests located in ./R/tests/ using testthat, mostly test coverage << 100%

Modules

There are a variety of modules to facilitate data loading, geneset retrievement, formatting/wrangling, and more. The following are descriptions of the available modules loosly in order for running a new analysis

io.R

Currently there are two options to input data. First is a tsv file containing statistics regarding a comparison - e.g. t test between two groups.

The function that handles this datasource is:

create_rnkfiles_from_volcano

After the identifier and value are properly loaded, a rankfile is saved locally for future use. Identifier currently is expected to be Entrez GeneID. Value currently expected to be a zero centered quantitative value about the comparison. Good choices of value are signedlogp value or logFoldChange

Second, for the purpose of ssGSEA, takes gct file format as input. Each sample (cid) is separated into a separate rank file. An option for scaling by zscore is available if data are not zero centered. Generally expected for data to be approximately normally distributed. Note: setting the GSEA paramater p to 0 should allow for proper analysis of non-normalized data, though explicit control over this parameter is not yet available.

geneset_utils.R

Facitiates fetching of publically available genesets using msigdbr. Caches fetched genesets to a local file. Support for multiple species is available through msigdbr.

fgsea.R

Execution performed by run_one wrapper around fgsea::fgsea. Also has an option to "collapse" pathways to reduce redundancy. This is performed with fgsea::collapsePathways. No filtering is performed at this stage; if collapse == TRUE redundant pathways will be calculated and indicated.

plot.R

Citations

To cite package ‘fgsea’ in publications use:

G. Korotkevich, V. Sukhov, A. Sergushichev. Fast gene set enrichment analysis. bioRxiv (2019), doi:10.1101/060012

  @Article{,
    author = {Gennady Korotkevich and Vladimir Sukhov and Alexey Sergushichev},
    title = {Fast gene set enrichment analysis},
    year = {2019},
    doi = {10.1101/060012},
    publisher = {Cold Spring Harbor Labs Journals},
    url = {http://biorxiv.org/content/early/2016/06/20/060012},
    journal = {bioRxiv},
  }

To cite package ‘msigdbr’ in publications use:

Dolgalev I (2022). msigdbr: MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format. R package version 7.5.1, https://CRAN.R-project.org/package=msigdbr.


  @Manual{,
    title = {msigdbr: MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format},
    author = {Igor Dolgalev},
    year = {2022},
    note = {R package version 7.5.1},
    url = {https://CRAN.R-project.org/package=msigdbr},
  }

To cite ggplot2 in publications, please use

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

  @Book{,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org},
  }

To cite package ‘purrr’ in publications use:

Wickham H, Henry L (2023). purrr: Functional Programming Tools. R package version 1.0.2, https://CRAN.R-project.org/package=purrr.


@Manual{,
    title = {purrr: Functional Programming Tools},
    author = {Hadley Wickham and Lionel Henry},
    year = {2023},
    note = {R package version 1.0.2},
    url = {https://CRAN.R-project.org/package=purrr},
  }

To cite package ‘ComplexHeatmap’ in publications use:


  @Article{,
    title = {Complex heatmaps reveal patterns and correlations in multidimensional genomic data},
    author = {Zuguang Gu and Roland Eils and Matthias Schlesner},
    journal = {Bioinformatics},
    doi = {10.1093/bioinformatics/btw313},
    year = {2016},
  }

  @Article{,
    title = {Complex Heatmap Visualization},
    author = {Zuguang Gu},
    doi = {10.1002/imt2.43},
    journal = {iMeta},
    year = {2022},
  }

asalt / bcm-gsea

readme