supersigs is a companion R package to a method proposed by Afsari, et al. (2021, ELife) to generate mutational signatures from single nucleotide variants in the cancer genome. Note: Package is under active development.

More details on the statistical method can be found in this paper:

  • Afsari, B., Kuo, A., Zhang, Y., Li, L., Lahouel, K., Danilova, L., Favorov, A., Rosenquist, T. A., Grollman, A. P., Kinzler, K. W., Cope, L., Vogelstein, B., & Tomasetti, C. (2021). Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer. ELife, 10.


You can install the development version of supersigs from github using the install_github() function from the devtools package.

# Install development version from GitHub

Core functions

In brief, the supersigs package contains two core functions: get_signature and predict_signature.

get_signature trains a supervised signature for a given factor (e.g. smoking).

supersig <- get_signature(dt = data, factor = "smoking", wgs = F)

predict_signature uses the trained supervised signature to obtain predicted probabilities (e.g. probability of smoker) on a new dataset.

pred <- predict_signature(object = supersig, newdata = data, factor = "smoking")


To follow a tutorial on how to use the package, click vignette("supersigs") (or type vignette("supersigs") in R).