GSNA version 0.1.4.2

Jonathan M. Urbach

Ragon Institute of MGH, MIT, and Harvard

2024-03-18

GSNA stands for Gene Set Network Analysis. GSNA is a toolkit for clustering gene sets based on metrics of similarity and distance such as the Jaccard and Szymkiewicz–Simpson overlap indices, and log Fisher p-values. The intended purpose of the GSNA package to provide a means to simplify data sets generated by pathways analysis methods such as GSEA (Subramanian et al. (2005),Mootha et al. (2003)), and CERNO (Zyla et al. (2019)). GSNA can be used subsequent to pathways analysis methods that generate lists of gene sets with associated significance statistics, e.g. p-values. From such data, groups of similar pathways are inferred, greatly simplifying the task of analyzing complex pathways datasets. On the basis of similarity, networks and clusters (or subnets) can be generated and represented graphically, and statistical parameters can be assessed.

Dependencies

We recommend R version 4.0 and later for GSNA, though it may be installable on some later R version 3 distributions. In addition to base R, the GSNA package requires some other R packages including the following:

• circlize
• DT
• dendextend
• dplyr
• ggplot2
• graphics
• grDevices
• igraph
• Matrix
• methods
• psych
• raster
• stringr
• stringi
• stats
• tibble
• tidyr
• tmod
• utils
• withr
• Rcpp

Several of these packages have cascading dependencies, which become particularly important when installing from source. If binary R packages are available, such as for up-to-date Windows and Mac OS X R installations, we recommend installing those. Compiling these packages from source generally requires a C, C++, and/or Fortran compiler, which on Windows means installing Rtools for Windows, and for Mac OS X, Xcode. Linux installations generally include the required compilers, specifically the GCC compilers.

NOTE: Particular care should be paid to the installation of the R raster package. The raster package requires the terra package, which in turn requires the GEOS C++ computational library, available from https://libgeos.org/ to compile from source. Binary packages for the GEOS library are available for numerous Linux distributions, and may offer a more convenient alternative than installing GEOS from source.

Installation

We intend to make the GSNA package available on CRAN in the near future. If and when it is accepted by CRAN, you will be able to install **GSNA using the following command:

install.packages('GSNA')

In the meantime, if you have the devtools package installed, you can install the development version of GSNA directly from GitHub like so:

devtools::install_github( repo = "https://github.com/JonathanUrbach/GSNA" )

Note: Currently, this method omits installation of the package’s Roxygen2 man pages.

If you have downloaded the source code from GitHub and opened the project in an Rstudio session, the following command can be used to install the package, including documentation:

devtools::install( build_vignettes = TRUE, args = "--no-multiarch --with-keep.source" )

Loading GSNA and Accessing Documentation

To load GSNA, type the following in your R console:

library( GSNA )

To access a vignette containing additional information and usage examples, run:

vignette( "using_the_gsna_package" )

A package description and a list of function documentation can be obtained via:

help( package = "GSNA" )

Citing GSNA

To cite package GSNA in publications, please use:

Collins, R. D, Urbach, M. J, Racenet, J. Z, Arshad, Umar, Power, A. K, Newman, M. R, Mylvaganam, H. G, Ly, L. N, Lian, Xiaodong, Rull, Anna, Rassadkina, Yelizaveta, Yanez, G. A, Peluso, J. M, Deeks, G. S, Vidal, Francesc, Lichterfeld, Mathias, Yu, G. X, Gaiha, D. G, Allen, M. T, Walker, D. B (2021). “Functional impairment of HIV-specific CD8+ T cells precedes aborted spontaneous control of viremia.” Immunity, S107476132100337X. doi:10.1016/j.immuni.2021.08.007 https://doi.org/10.1016/j.immuni.2021.08.007, https://linkinghub.elsevier.com/retrieve/pii/S107476132100337X.

Collins, R. D, Hitschfel, Julia, Urbach, M. J, Mylvaganam, H. G, Ly, L. N, Arshad, Umar, Racenet, J. Z, Yanez, G. A, Diefenbach, J. T, Walker, D. B (2023). “Cytolytic CD8+ T cells infiltrate germinal centers to limit ongoing HIV replication in spontaneous controller lymph nodes.” Science Immunology, 8(83), eade5872. doi:10.1126/sciimmunol.ade5872 https://doi.org/10.1126/sciimmunol.ade5872, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231436.

References

Mootha, Vamsi K., Cecilia M. Lindgren, Karl-Fredrik Eriksson, Aravind Subramanian, Smita Sihag, Joseph Lehar, Pere Puigserver, et al. 2003. “PGC-1α-Responsive Genes Involved in Oxidative Phosphorylation Are Coordinately Downregulated in Human Diabetes.” Nature Genetics 34 (3): 267–73. https://doi.org/10.1038/ng1180.

Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.

Zyla, Joanna, Michal Marczyk, Teresa Domaszewska, Stefan H E Kaufmann, Joanna Polanska, and January Weiner 3rd. 2019. “Gene Set Enrichment for Reproducible Science: Comparison of CERNO and Eight Other Algorithms.” Bioinformatics 35 (24): 5146–54. https://doi.org/10.1093/bioinformatics/btz447.