‘singscore’ is an R/Bioconductor package which implements the simple single-sample gene-set (or gene-signature) scoring method proposed by Foroutan et al. (2018). It uses rank-based statistics to analyze each sample’s gene expression profile and scores the expression activities of gene sets at a single-sample level.

We have written up a new workflow package demonstrating application of singscore to infer mutation status in the TCGA acute myeloid leukemia cohort. Refer to the published workflow at https://f1000research.com/articles/8-776/v2. It is also available as a R/Bioconductor workflow package SingscoreAMLMutations.

Getting Started

These instructions will get you to install the package up and running on your local machine. If you experience any issues, please raise a GitHub issue at https://github.com/DavisLaboratory/singscore/issues.

# build_vignettes = TRUE to build vignettes upon installation
if (!requireNamespace("BiocManager", quietly = TRUE))
BiocManager::install("singscore", version = "3.8")


The package comes with a vignette showing how the different functions in the package can be used to perform a gene-set enrichment analysis on a single sample level. Pre-built vignettes can be accessed via Bioconductor or the GitHub IO page.


Foroutan, Momeneh, Dharmesh D Bhuva, Ruqian Lyu, Kristy Horan, Joseph Cursons, and Melissa J Davis. 2018. “Single Sample Scoring of Molecular Phenotypes.” BMC Bioinformatics 19 (1). BioMed Central: 404. doi: 10.1186/s12859-018-2435-4.