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  "Package": "corto",
  "Type": "Package",
  "Title": "Inference of Gene Regulatory Networks",
  "Version": "1.4.0",
  "Authors@R": "c(\nperson(\"Federico M.\",\"Giorgi\",email = \"federico.giorgi@gmail.com\",role=c(\"aut\",\"cre\")),\nperson(\"Daniele\",\"Mercatelli\",email = \"danielemercatelli@gmail.com\",role=\"ctb\"),\nperson(\"Gonzalo\",\"Lopez-Garcia\",email = \"gonzolgarcia@gmail.com\",role=\"ctb\"),\nperson(\"Hugo\",\"Tovar\",email = \"hatovar@inmegen.gob.mx\",role=\"ctb\"),\nperson(\"Hualin\",\"Wang\",email = \"wanghualin@stu.njmu.edu.cn\",role=\"ctb\"))",
  "Maintainer": "Federico M. Giorgi <federico.giorgi@gmail.com>",
  "Description": "We present 'corto' (Correlation Tool), a simple package to\ninfer gene regulatory networks and visualize master regulators\nfrom gene expression data using DPI (Data Processing\nInequality) and bootstrapping to recover edges. An initial step\nis performed to calculate all significant edges between a list\nof source nodes (centroids) and target genes. Then a DPI step\nremoves edges: by default, for each target only the edge from\nthe centroid with the strongest correlation is kept;\noptionally, the 'ARACNe' rule removes an edge only if it is the\nweakest in a triplet of two centroids and one target. A\nbootstrapping process then calculates the robustness of the\nnetwork, eventually re-adding edges previously removed by DPI.\nThe algorithm has been optimized to run outside a computing\ncluster, using a fast correlation implementation. The package\nfinally provides functions to calculate network enrichment\nanalysis from RNA-Seq and ATAC-Seq signatures as described in\nthe article by Giorgi lab (2020)\n<doi:10.1093/bioinformatics/btaa223>.",
  "License": "LGPL-3",
  "Encoding": "UTF-8",
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  "Repository": "https://federicogiorgi.r-universe.dev",
  "Date/Publication": "2026-10-20 19:02:23 UTC",
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  "Author": "Federico M. Giorgi [aut, cre],\nDaniele Mercatelli [ctb],\nGonzalo Lopez-Garcia [ctb],\nHugo Tovar [ctb],\nHualin Wang [ctb]",
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  "_published": "2026-10-20T20:16:15.774Z",
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    "message": "Vignette: harmonize with the README\n\nThe vignette text is now generated from the README: same introduction\n(corto paper cited first, metabolomics application), the same seven\nalgorithm steps, the mra() and mraplot() section and the funding note,\nwith references numbered identically. The mra() chunks are not evaluated,\nlike the CNV one, to keep the build fast, and the two remotely hosted\nimages are left out so that the build needs no network access.\n",
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  "_mentions": 3,
  "_devurl": "https://github.com/federicogiorgi/corto",
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      "date": "2019-10-30"
    },
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      "date": "2020-01-28"
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    {
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      "date": "2020-01-30"
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    {
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      "date": "2020-03-03"
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      "date": "2020-04-09"
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      "version": "1.1.0",
      "date": "2020-06-27"
    },
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      "date": "2020-10-13"
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    {
      "version": "1.1.3",
      "date": "2020-10-23"
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    {
      "version": "1.1.4",
      "date": "2021-03-05"
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      "date": "2021-05-24"
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      "date": "2021-09-14"
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      "date": "2021-09-29"
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      "date": "2021-10-22"
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      "date": "2022-05-23"
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      "date": "2023-02-23"
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      "date": "2023-12-07"
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    "dpi_edges",
    "filter_regulon",
    "fisherp",
    "getregulon",
    "gsea",
    "gsea2",
    "kmgformat",
    "mra",
    "mraplot",
    "p2r",
    "p2z",
    "plot_gsea",
    "plot_gsea2",
    "r2p",
    "scatter",
    "scinot",
    "slice",
    "ssgsea",
    "stouffer",
    "textrepel",
    "val2col",
    "wstouffer",
    "z2p"
  ],
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    {
      "page": "barplot2",
      "title": "barplot2 - Bar plot with upper error bars",
      "topics": [
        "barplot2"
      ]
    },
    {
      "page": "corto",
      "title": "Calculate a regulon from a data matrix",
      "topics": [
        "corto"
      ]
    },
    {
      "page": "dpi_edges",
      "title": "Apply the Data Processing Inequality to a set of centroid-target edges",
      "topics": [
        "dpi_edges"
      ]
    },
    {
      "page": "fcor",
      "title": "A fast correlation function",
      "topics": [
        "fcor"
      ]
    },
    {
      "page": "filter_regulon",
      "title": "filter_regulon - Filter a Regulon Object",
      "topics": [
        "filter_regulon"
      ]
    },
    {
      "page": "fisherp",
      "title": "Fisher integration of p-values",
      "topics": [
        "fisherp"
      ]
    },
    {
      "page": "getregulon",
      "title": "getregulon - Flatten a Regulon Object into a Table",
      "topics": [
        "getregulon"
      ]
    },
    {
      "page": "gsea",
      "title": "GSEA",
      "topics": [
        "gsea"
      ]
    },
    {
      "page": "gsea2",
      "title": "2-way GSEA GSEA Gene set enrichment analysis of two complementary gene sets using gsea",
      "topics": [
        "gsea2"
      ]
    },
    {
      "page": "kmgformat",
      "title": "kmgformat - Nice Formatting of Numbers",
      "topics": [
        "kmgformat"
      ]
    },
    {
      "page": "mra",
      "title": "Perform Master Regulator Analysis (mra)",
      "topics": [
        "mra"
      ]
    },
    {
      "page": "mraplot",
      "title": "Plot a master regulator analysis",
      "topics": [
        "mraplot"
      ]
    },
    {
      "page": "p2r",
      "title": "p2r Convert a P-value to the corresponding Correlation Coefficient",
      "topics": [
        "p2r"
      ]
    },
    {
      "page": "p2z",
      "title": "p2z",
      "topics": [
        "p2z"
      ]
    },
    {
      "page": "plot_gsea",
      "title": "Plot GSEA results",
      "topics": [
        "plot_gsea"
      ]
    },
    {
      "page": "plot_gsea2",
      "title": "Plot 2-way GSEA results",
      "topics": [
        "plot_gsea2"
      ]
    },
    {
      "page": "r2p",
      "title": "r2p Convert Correlation Coefficient to P-value",
      "topics": [
        "r2p"
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    },
    {
      "page": "scatter",
      "title": "scatter - XY scatter plot with extra information",
      "topics": [
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      ]
    },
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      "page": "scinot",
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      "topics": [
        "scinot"
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    },
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      "filename": "corto_vignette.html",
      "title": "A short introduction to the corto Package",
      "author": "| Daniele Mercatelli^1^, Gonzalo Lopez-Garcia^2^, Federico M. Giorgi^1^ | ^1^Department of Pharmacy and Biotechnology, University of Bologna, Italy | ^2^Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, USA",
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        "The corto Gene Network Inference Algorithm",
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        "Sample-by-sample centroid prediction",
        "Contrast centroid prediction",
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        "References"
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