singscore

Overview

‘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) and Bhuva et al. (2020). 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.

Additional packages we have developed can enhance the singscore workflow:

1. msigdb - A package that provides gene-sets from the molecular signatures database (MSigDB) as a GeneSetCollection object that is compatible with singscore.
2. vissE - A package that can summarise and aid in the interpretation of a list of significant gene-sets identified by singscore (see tutorial).
3. emtdata - The full EMT dataset used in this tutorial (with additional EMT related datasets).

We have also published and made openly available the extensive tutorials below that demonstrate the variety of ways in which singscore can be used to gain a better functional understanding of molecular data:

1. Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures.
2. Gene-set enrichment analysis workshop - available through the Orchestra platform (search “WEHI Masterclass Day 4: Functional Analysis, single sample gene set analysis”).

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))
    install.packages("BiocManager")
BiocManager::install("singscore", version = "3.8")

Documentation

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.

References

Foroutan M, Bhuva D, Lyu R, Horan K, Cursons J, Davis M (2018). “Single sample scoring of molecular phenotypes.” BMC bioinformatics, 19(1), 404. doi: 10.1186/s12859-018-2435-4.

Bhuva D, Cursons J, Davis M (2020). “Stable gene expression for normalisation and single-sample scoring.” Nucleic Acids Research, 48(19), e113. doi: 10.1093/nar/gkaa802.