Plaid (Pathway Level Average Intensity Detection) is an ultra-fast method to compute single-sample enrichment scores for gene expression or proteomics data. For each sample, plaid computes the gene set score as the average intensity of the genes/proteins in the gene set. The output is a gene set score matrix suitable for further analyses.
A distinctive feature of PLAID is that it can simulate few of the most widely used gene set scoring algorithms (GSVA, ssGSEA, scSE, ucell, sing), enabling researchers to replace those functions and gaining much improved runtime efficiency and memory requirement. Typically, PLAID can be more than 100 times faster than the original algorithm.
Plaid is freely available on GitHub. It's a main gene sets scoring algorithm in OmicsPlayground, our Bioinformatics platform at BigOmics Analytics. In OmicsPlayground, you can perform Plaid without coding needs.
You can install plaid from Bioconductor:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("plaid")You can also install the development version from GitHub:
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("bigomics/plaid")For detailed usage examples and tutorials, please see our vignettes:
Key features:
- Ultra-fast single-sample gene set enrichment scoring
- Automatically detects and handles Bioconductor objects (
SummarizedExperiment,SingleCellExperiment,BiocSet) - Works with regular matrices, sparse matrices, and Bioconductor data structures
- Includes multiple scoring methods (plaid, sing, ssgsea, scSE, ucell, gsva)
- Built-in differential enrichment testing
- Zito A., et al. PLAID: ultrafast single-sample gene set enrichment scoring. BioRxiv preprint. June 2025.
For support feel free to reach our Bioinformatics Data Science Team at BigOmics Analytics: help@bigomics.ch