Data Analysis Modules: panoply_ssgsea

panoply_ssgsea

Description

Performs single sample Gene Set Enrichment Analysis (ssGSEA) or PTM-Signature Enrichment Analysis (PTM-SEA) [1] on each column of the input data matrix. This module is based on the implementation available at the ssGSEA2.0 GitHub repository.

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/.

Input

Required inputs:

• input_ds: (.gct file) input GCT file
• gene_set_database: (.gmt file) gene set database
• yaml_file: (.yaml file) master-parameters.yaml

Optional inputs:

• correl_type: (String) Correlation type: "z.score" (default), "rank", "symm.rank".
• global_fdr: (Boolean) If TRUE global FDR across all data columns is calculated (default: FALSE).
• min_overlap: (Integer) Minimal overlap between signature and data set (default: 10).
• tolerate_min_overlap_err (String) Internal parameter. TRUE/FALSE toggle for tolerating "not-enough-overlap" errors. Recommended value is FALSE.
• nperm: (Integer) Number of permutations (default: 1000).
• output_score_type: (String) Score type: "ES" - enrichment score, "NES" - normalized ES (default).
• sample_norm_type: (String) Sample normalization: "rank"(default), "log", "log.rank"
• statistic: (String) Test statistic: "area.under.RES" (default), "Kolmogorov-Smirnov"
• weight: (Float) When weight=0, all genes have the same weight; if weight>0 actual values matter and can change the resulting score (default: 0.75).
• output_prefix: (String, default="results-ssgsea") File prefix for output files.

Output

• results: (.tar.gz file) tarball including:
  • ${output_prefix}-scores.gct: GCT file with enrichment scores as data matrix (@mat)
  • ${output_prefix}-pvalues.gct: GCT file with nominal p-values as data matrix (@mat)
  • ${output_prefix}-fdr-pvalues.gct: GCT file with FDR-corrected p-values as data matrix (@mat)
  • ${output_prefix}-combined.gct: GCT file with enrichment scores as data matrix (@mat) as well as nominal and FDR-corrected p-values as row-description metadata (@rdesc).
  • ${output_prefix}-parameters.txt: Text file summarizing parameters.
  • ${output_prefix}-ssgse.log.txt: Text file tracking progess.
  • signature_gct: Directory with individual GCT files (one for each gene set) with the original data used as input for ssGSEA/PTM-SEA as data matrix (@mat).
• ssgsea_min_overlap_err: (Boolean) Internal parameter. TRUE if all pathways returned a "not-enough-overlap" error; relevant when tolerate_min_overlap_err=TRUE. Can be used to automatically skip the report module in a workflow context.

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