ssGSEA2.0/PTM-SEA

Resources for gene-centric single sample Gene Set Enrichment Analysis (ssGSEA) of gene expression data (e.g. mRNAs, proteins) and site-centric PTM Signature Enrichment Analysis (PTM-SEA) [1] of phosphoproteomics data sets using the PTM signatures database (PTMsigDB) [1].

Disclaimer

The primary purpose of this repository is to supplement our manuscript in which we describe PTM-SEA and PTMsigDB. While ssGSEA2.0 presents an updated version of the original ssGSEA R implementation, we want to acknowledge that this is not the primary repository for ssGSEA. The official codebase for ssGSEA can be found here, and the official GenePattern module to perform ssGSEA can be accessed here.

ssGSEA 2.0

This is an updated version of the original ssGSEA [2,3] R-implementation. Depending on the input dataset and chosen database (gene sets or PTM signatures), the software performs either ssGSEA or PTM-SEA, respectively. The Molecular Signatures Database (MSigDB) [4] provides a large collection of curated gene sets. Gene sets are stored as plain text in GMT format. A current version of MSigDB gene set collections can be found in the db/msigdb subfolder. MSigDB gene sets are realeased under Creative Commons Attribution 4.0 International License. The license terms can be found in thedb/msigdb folder.

File formats supported by ssGSEA2.0/PTM-SEA are Gene Cluster Text GCT v1.2 or GCT v1.3 files. Morpheus provides a convenient way to convert your data tables into GCT format.

For more information about the GSEA method and MSigDB please visit http://software.broadinstitute.org/gsea/.

How can I use these tools?

I modified the GUI version to a R script named "main.R" that will generate ssGSEA results.
Remember to modify directory paths in "main.R", MsigDB files and .gct files.

License

License Agreement for MSigDB v6.0 and above can be found here.

References

1. Krug, K., Mertins, P., Zhang, B., Hornbeck, P., Raju, R., Ahmad, R., . Szucs, M., Mundt, F., Forestier, D., Jane-Valbuena, J., Keshishian, H., Gillette, M. A., Tamayo, P., Mesirov, J. P., Jaffe, J. D., Carr, S. A., Mani, D. R. (2019). A curated resource for phosphosite-specific signature analysis, Molecular & Cellular Proteomics (in Press). http://doi.org/10.1074/mcp.TIR118.000943

2. Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Susan, E., Dunn, I. F., . Hahn, W. C. (2010). Systematic RNA interference reveals that oncogenic KRAS- driven cancers require TBK1, Nature, 462(7269), 108-112. https://doi.org/10.1038/nature08460

3. Abazeed, M. E., Adams, D. J., Hurov, K. E., Tamayo, P., Creighton, C. J., Sonkin, D., et al. (2013). Integrative Radiogenomic Profiling of Squamous Cell Lung Cancer. Cancer Research, 73(20), 6289-6298. http://doi.org/10.1158/0008-5472.CAN-13-1616

4. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15545-15550. http://doi.org/10.1073/pnas.0506580102

5. Hornbeck, P. V., Zhang, B., Murray, B., Kornhauser, J. M., Latham, V., & Skrzypek, E. (2015). PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Research, 43(D1), D512-D520. https://doi.org/10.1093/nar/gku1267

6. Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 26(12), 1367-1372. https://doi.org/10.1038/nbt.1511

7. Reich, M., Liefeld, T., Gould, J., Lerner, J., Tamayo, P., & Mesirov, J. P. (2006). GenePattern 2.0. Nature Genetics, 38(5), 500-501. https://doi.org/10.1038/ng0506-500

---