Summary: Accessing the collection of perturbed gene expression profiles, such as the LINCS L1000 connectivity map, is usually performed at the individual dataset level, followed by a summary performed by counting individual hits for each perturbagen. With the metaLINCS R package, we present an alternative approach that combines rank correlation and gene set enrichment analysis to identify meta-level enrichment at the perturbagen level and, in the case of drugs, at the mechanism of action level. This significantly simplifies the interpretation and highlights overarching themes in the data. We demonstrate the functionality of the package and compare its performance against those of three currently used approaches.
Availability and implementation: metaLINCS is released under GPL3 license. Source code and documentation are freely available on GitHub (https://github.com/bigomics/metaLINCS).
Supplementary information: Supplementary data are available at Bioinformatics Advances online.
© The Author(s) 2022. Published by Oxford University Press.