Package: corto Type: Package Title: Inference of Gene Regulatory Networks Version: 1.4.0 Authors@R: c( person("Federico M.","Giorgi",email = "federico.giorgi@gmail.com",role=c("aut","cre")), person("Daniele","Mercatelli",email = "danielemercatelli@gmail.com",role="ctb"), person("Gonzalo","Lopez-Garcia",email = "gonzolgarcia@gmail.com",role="ctb"), person("Hugo","Tovar",email = "hatovar@inmegen.gob.mx",role="ctb"), person("Hualin","Wang",email = "wanghualin@stu.njmu.edu.cn",role="ctb")) Maintainer: Federico M. Giorgi Description: We present 'corto' (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then a DPI step removes edges: by default, for each target only the edge from the centroid with the strongest correlation is kept; optionally, the 'ARACNe' rule removes an edge only if it is the weakest in a triplet of two centroids and one target. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) . License: LGPL-3 Encoding: UTF-8 Depends: R (>= 3.6) NeedsCompilation: no Imports: grDevices, graphics, methods, parallel, pbapply, plotrix, stats, utils Suggests: knitr, rmarkdown VignetteBuilder: knitr Config/roxygen2/version: 8.1.0 Repository: https://federicogiorgi.r-universe.dev Date/Publication: 2026-10-20 19:02:23 UTC RemoteUrl: https://github.com/federicogiorgi/corto RemoteRef: HEAD RemoteSha: 132dcc6cfc571301ecdd3d3af50c9bab83fc3ca4 Packaged: 2026-10-20 20:13:16 UTC; root Author: Federico M. Giorgi [aut, cre], Daniele Mercatelli [ctb], Gonzalo Lopez-Garcia [ctb], Hugo Tovar [ctb], Hualin Wang [ctb]