Motivation: Gene network inference and master regulator analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses but most require a computer cluster or large amounts of RAM to be executed.Results: We developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for copy-number variations and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets.
Mercatelli D., Lopez-Garcia G., Giorgi F.M. (2020). Corto: A lightweight R package for gene network inference and master regulator analysis. BIOINFORMATICS, 36(12), 3916-3917 [10.1093/bioinformatics/btaa223].
Corto: A lightweight R package for gene network inference and master regulator analysis
Mercatelli D.;Giorgi F. M.
2020
Abstract
Motivation: Gene network inference and master regulator analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses but most require a computer cluster or large amounts of RAM to be executed.Results: We developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for copy-number variations and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets.File | Dimensione | Formato | |
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