ssGSEA2.0/PTM-SEA

This repository provides an R package implementation of ssGSEA2.0. See the upstream repository for more details about publications, ssGSEA 2.0, PTM-SEA, MSigDB, and PTMsigDB.

Installation

R >= 4.0

if (!require("devtools", quietly = TRUE)){
  install.packages("devtools")
}
devtools::install_github("nicolerg/ssGSEA2")

R 3.6

You must install the GitHub version of cmapR first:

if (!require("devtools", quietly = TRUE)){
  install.packages("devtools")
}
install_github("cmap/cmapR", ref="R-3.6")

Then install this package:

devtools::install_github("nicolerg/ssGSEA2")

Example

In this example, we perform PTM-SEA with example input provided in examples/.

library(ssGSEA2)

# Download example input
download.file(url = "https://raw.githubusercontent.com/nicolerg/ssGSEA2/master/example/PI3K_pert_logP_n2x23936.gct",
              destfile = "/tmp/PI3K_pert_logP_n2x23936.gct")

# Download gene set database 
download.file(url = "https://raw.githubusercontent.com/nicolerg/ssGSEA2/master/example/ptm.sig.db.all.flanking.human.v1.8.1.gmt"),
              destfile = "/tmp/ptm.sig.db.all.flanking.human.v1.8.1.gmt")

res = run_ssGSEA2("/tmp/PI3K_pert_logP_n2x23936.gct",
                  output.prefix = "example",
                  gene.set.databases = "/tmp/ptm.sig.db.all.flanking.human.v1.8.1.gmt",
                  output.directory = "/tmp",
                  sample.norm.type = "none", 
                  weight = 0.75, 
                  correl.type = "rank", 
                  statistic = "area.under.RES",
                  output.score.type = "NES", 
                  nperm = 1000, 
                  min.overlap = 5, 
                  extended.output = TRUE, 
                  global.fdr = FALSE,
                  log.file = "/tmp/run.log")

Notes

We are aware that the following warnings are seen upon attaching this package:

Warning messages:
1: multiple methods tables found for ‘aperm’ 
2: replacing previous import ‘BiocGenerics::aperm’ by ‘DelayedArray::aperm’ when loading ‘SummarizedExperiment’

This could be avoided by requiring DelayedArray >= 0.24.0 and R >= 4.2. However, to make this package accessible to users using older versions of R, we opted not to do this. See more details here.

Citing this work

Citing ssGSEA2.0

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, 18(3), 576-593. http://doi.org/10.1074/mcp.TIR118.000943

Citing ssGSEA

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

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