How to cite the Bioconductor package fgsea

fgsea is a popular Bioconductor package that is available at https://bioconductor.org/packages/fgsea. By citing R packages in your paper you lay the grounds for others to be able to reproduce your analysis and secondly you are acknowledging the time and work people have spent creating the package.

APA citation

Formatted according to the APA Publication Manual 7^th edition. Simply copy it to the References page as is.

APA

The minimal requirement is to cite the R package in text along with the version number. Additionally, you can include the reference list entry the authors of the fgsea package have suggested.

Example of an in-text citation

Analysis of the data was done using the fgsea package (v1.16.0; Korotkevich et al., 2016).

Reference list entry

Korotkevich, G., Sukhov, V., Budin, N., Shpak, B., Artyomov, M. N., & Sergushichev, A. (2016). Fast gene set enrichment analysis. bioRxiv. https://doi.org/10.1101/060012

Vancouver citation

Formatted according to Vancouver style. Simply copy it to the references section as is.

Vancouver

Example of an in-text citation

Analysis of the data was done using the fgsea package v1.16.0 (1).

Reference list entry

1.
Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis [Internet]. bioRxiv; 2016. Available from: http://dx.doi.org/10.1101/060012

BibTeX

Reference entry in BibTeX format. Simply copy it to your favorite citation manager.

BibTeX

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@UNPUBLISHED{Korotkevich2016-ks,
  title       = "Fast gene set enrichment analysis",
  author      = "Korotkevich, Gennady and Sukhov, Vladimir and Budin, Nikolay
                 and Shpak, Boris and Artyomov, Maxim N and Sergushichev,
                 Alexey",
  abstract    = "AbstractGene set enrichment analysis (GSEA) is an ubiquitously
                 used tool for evaluating pathway enrichment in transcriptional
                 data. Typical experimental design consists in comparing two
                 conditions with several replicates using a differential gene
                 expression test followed by preranked GSEA performed against a
                 collection of hundreds and thousands of pathways. However, the
                 reference implementation of this method cannot accurately
                 estimate small P-values, which significantly limits its
                 sensitivity due to multiple hypotheses correction
                 procedure.Here we present FGSEA (Fast Gene Set Enrichment
                 Analysis) method that is able to estimate arbitrarily low GSEA
                 P-values with a high accuracy in a matter of minutes or even
                 seconds. To confirm the accuracy of the method, we also
                 developed an exact algorithm for GSEA P-values calculation for
                 integer gene-level statistics. Using the exact algorithm as a
                 reference we show that FGSEA is able to routinely estimate
                 P-values up to 10−100 with a small and predictable estimation
                 error. We systematically evaluate FGSEA on a collection of 605
                 datasets and show that FGSEA recovers much more statistically
                 significant pathways compared to other implementations.FGSEA
                 is open source and available as an R package in Bioconductor
                 (http://bioconductor.org/packages/fgsea/) and on GitHub
                 (https://github.com/ctlab/fgsea/).",
  institution = "bioRxiv",
  month       =  jun,
  year        =  2016,
  url         = "http://dx.doi.org/10.1101/060012",
  doi         = "10.1101/060012"
}

RIS

Reference entry in RIS format. Simply copy it to your favorite citation manager.

RIS

TY  - INPR
AU  - Korotkevich, Gennady
AU  - Sukhov, Vladimir
AU  - Budin, Nikolay
AU  - Shpak, Boris
AU  - Artyomov, Maxim N
AU  - Sergushichev, Alexey
TI  - Fast gene set enrichment analysis
PY  - 2016
DA  - 2016/6/20
AB  - AbstractGene set enrichment analysis (GSEA) is an ubiquitously used tool
      for evaluating pathway enrichment in transcriptional data. Typical
      experimental design consists in comparing two conditions with several
      replicates using a differential gene expression test followed by preranked
      GSEA performed against a collection of hundreds and thousands of pathways.
      However, the reference implementation of this method cannot accurately
      estimate small P-values, which significantly limits its sensitivity due to
      multiple hypotheses correction procedure.Here we present FGSEA (Fast Gene
      Set Enrichment Analysis) method that is able to estimate arbitrarily low
      GSEA P-values with a high accuracy in a matter of minutes or even seconds.
      To confirm the accuracy of the method, we also developed an exact
      algorithm for GSEA P-values calculation for integer gene-level statistics.
      Using the exact algorithm as a reference we show that FGSEA is able to
      routinely estimate P-values up to 10−100 with a small and predictable
      estimation error. We systematically evaluate FGSEA on a collection of 605
      datasets and show that FGSEA recovers much more statistically significant
      pathways compared to other implementations.FGSEA is open source and
      available as an R package in Bioconductor
      (http://bioconductor.org/packages/fgsea/) and on GitHub
      (https://github.com/ctlab/fgsea/).
DO  - 10.1101/060012
UR  - http://dx.doi.org/10.1101/060012
ER  -

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