Reproducing selected models/Palma2021/Palma2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproduce the Palma 2021 model\n",
    "\n",
    ">Palma A, Iannuccelli M, Rozzo I, Licata L, Perfetto L, Massacci G, Castagnoli L, Cesareni G, Sacco F. Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia. J Pers Med. 2021 Feb 10;11(2):117. doi: 10.3390/jpm11020117. PMID: 33578936; PMCID: PMC7916657.  \n",
    "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916657/ \n",
    "\n",
    "**Goal:**\n",
    "   \n",
    "Develop strategies to exploit large-scale patient genomic information to infer clinical outcomes and design effective personalized therapy.\n",
    "\n",
    "**Knowledge source:**\n",
    "  \n",
    "1. Genes frequently mutated in AML as listed by four international sequencing consortia or online resources: \n",
    "The TCGA AML dataset, consisting of genomic data of 200 clinically annotated adult cases of de novo AML patients. Fifty cases were characterized by whole-genome sequencing while for the remaining 150 only the exomes were sequenced [ref].   \n",
    "2. Papaemmanuil et al., NEJM 2016, reported the mutational profile of 111 cancer driver genes in 1540 patients enrolled in three clinical trials. For each patient cytogenetic and clinical data are also available [ref].\n",
    "Cancer Gene Census, a continuously updated resource storing a manually expert-annotated catalogue of genes containing mutations that have been causally implicated in cancer onset or progression [ref].\n",
    "3. DisGeNET, a resource containing one of the largest collections of gene lists associated with human diseases [ref]. The list of genes that this resource links to AML is very large (102 genes). We only considered the 33 genes significantly associated to AML (Score_dga > 0.02).\n",
    "4. Literature review: expert consensus as extrapolated from highly cited reviews\n",
    "The causal relationships annotated in the SIGNOR database\n",
    "\n",
    "**Representation:**\n",
    "  \n",
    "Boolean network\n",
    "https://signor.uniroma2.it/CancerGeneNet/    \n",
    "AML drivers are connected in a signed directed logic network to the three hallmark phenotypes. Each node and edge is color-coded as described in the legend.\n",
    "\n",
    "**Tools:**\n",
    "  \n",
    "CancerGeneNet, a tool implemented in SIGNOR to connect the disease genes to hallmark disease phenotypes by causal relationships, obtaining a naïve network of cause–effect interactions\n",
    "R package Boolnet to assemble the model and to compute its steady states\n",
    "\n",
    "**in silico validation:**\n",
    "  \n",
    "Simulations were performed for different genomic profiles, to evaluate whether these models infer clinical outcomes of patients. To this end they made use of the clinical information provided by Papaemmanuil et al. reporting the impact of somatic mutations on overall survival and compared the mutation-specific hazard ratios with the predictions of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "This notebook has been executed using the docker image `colomoto/colomoto-docker:2024-03-01`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\" id=\"colomoto-setup-4111321\" class=\"to-be-removed\">\n",
       "        if (typeof Jupyter != 'undefined') {\n",
       "            \n",
       "function detect_import(cell, module) {\n",
       "    var code = cell.get_text();\n",
       "    code = code.replace(/\\\\\\n/g, \"\");\n",
       "    var lines = code.split(\"\\n\");\n",
       "    var r_simple = new RegExp(\"^(\"+module+\")$\");\n",
       "    var r_alias = new RegExp(\"^\"+module+\"\\\\s+as\\\\s+(\\\\w+)$\");\n",
       "    for (var i = 0; i < lines.length; ++i) {\n",
       "        if (/^import\\s/.test(lines[i])) {\n",
       "            code = lines[i].substr(7);\n",
       "            var parts = code.split(\",\")\n",
       "            for (var j = 0; j < parts.length; ++j) {\n",
       "                code = parts[j].trim();\n",
       "                var m = code.match(r_simple);\n",
       "                if (!m) {\n",
       "                    m = code.match(r_alias);\n",
       "                }\n",
       "                if (m) {\n",
       "                    return m[1];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "    return module;\n",
       "}\n",
       "\n",
       "function colomoto_replace_call(cell, orig, dest, args, comment=false) {\n",
       "    var call_regexp = new RegExp(\"\\\\.\"+orig.replace(\".\",\"\\\\.\")+\"\\\\(\");\n",
       "    var call_replacer = new RegExp(\"\\\\.\"+orig.replace(\".\",\"\\\\.\")\n",
       "                    + \"\\\\(\\\\s*([^\\\\)]*)?\\\\)\");\n",
       "    var code = cell.get_text();\n",
       "    var lines = code.split(\"\\n\");\n",
       "    if (args) {\n",
       "        var strargs = \", \"+args.join(\", \");\n",
       "    } else {\n",
       "        var strargs = \"\";\n",
       "    }\n",
       "    for (var i = 0; i < lines.length; ++i) {\n",
       "        if (call_regexp.test(lines[i])) {\n",
       "            var code = \"\"\n",
       "            if (comment) {\n",
       "                code += \"#\"+lines[i]+\"\\n\";\n",
       "            }\n",
       "            code += lines[i].replace(call_replacer, \".\"+dest+\"($1\"+strargs+\")\")\n",
       "            lines[i] = code;\n",
       "        }\n",
       "    }\n",
       "    cell.set_text(code)\n",
       "}\n",
       "\n",
       "function colomoto_upload(Jupyter, ssid, input, py_callback_name, orig, dest) {\n",
       "\n",
       "    function callback(out_data) {\n",
       "        var cell_element = $(\"#\"+ssid).parents('.cell');\n",
       "        var cell_idx = Jupyter.notebook.get_cell_elements().index(cell_element);\n",
       "        var cell = Jupyter.notebook.get_cell(cell_idx);\n",
       "\n",
       "        var filename = out_data.content.text;\n",
       "\n",
       "        var code = cell.get_text();\n",
       "        code = code.replace(new RegExp(\"\\\\b\" + orig.replace('.', '\\\\.')\n",
       "                    + \"\\\\(\\\\s*((\\\\w+)=[^\\\\)]*)?\\\\)\"),\n",
       "                dest+\"(\\\"\"+filename+\"\\\",$1)\");\n",
       "        code = code.replace('\",)', '\")')\n",
       "        cell.set_text(code);\n",
       "\n",
       "        Jupyter.notebook.select(cell_idx);\n",
       "        Jupyter.notebook.execute_cell_and_select_below();\n",
       "    }\n",
       "\n",
       "    if (! (window.File && window.FileReader && window.FileList && window.Blob)) {\n",
       "        alert(\"Interactive file upload is not supported by your browser.\");\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    input.disabled = true;\n",
       "    input.style.cursor = \"wait\";\n",
       "    input.parentElement.style.cursor = \"wait\";\n",
       "\n",
       "    var f = input.files[0];\n",
       "    var reader = new FileReader();\n",
       "    reader.onload = (function(f) {\n",
       "        return function (e) {\n",
       "            var obj = {\n",
       "                content: e.target.result,\n",
       "                name: f.name\n",
       "            };\n",
       "\n",
       "            //var pycb = py_callback_name+\"(\"+JSON.stringify(obj)+\")\"\n",
       "            // hack/workaround:\n",
       "            // it seems that Jupyter does not like very long lines\n",
       "            // so we split the data in chunks\n",
       "            var chunk_length = 100;\n",
       "            var pycb = \"__colomoto_upload_name = \" + JSON.stringify(obj.name) + \"\\n\";\n",
       "            pycb += \"__colomoto_upload_content = \\\\\\n\";\n",
       "            for (var i = 0; i < obj.content.length; i += chunk_length) {\n",
       "                pycb += \"\\\"\" + obj.content.substr(i, chunk_length)+\"\\\"\\\\\\n\"\n",
       "            }\n",
       "            pycb += \"\\n\"\n",
       "            pycb += py_callback_name+\"({'name':__colomoto_upload_name, 'content': __colomoto_upload_content})\\n\";\n",
       "            pycb += \"del __colomoto_upload_name, __colomoto_upload_content\"\n",
       "\n",
       "            IPython.notebook.kernel.execute(pycb, {iopub: {output: callback}});\n",
       "        };\n",
       "    })(f);\n",
       "    reader.readAsDataURL(f);\n",
       "}\n",
       "\n",
       "function resolve_function(tool_api, funcname) {\n",
       "    if (tool_api.hasOwnProperty(funcname)) {\n",
       "        return tool_api[funcname];\n",
       "    } else {\n",
       "        return window[funcname];\n",
       "    }\n",
       "}\n",
       "\n",
       "function colomoto_extension(Jupyter, ssid, name, menu, toolbar, tool_api) {\n",
       "\n",
       "    function insert_snippet_code(snippet) {\n",
       "        var cell = Jupyter.notebook.get_selected_cell();\n",
       "        Jupyter.notebook.edit_mode();\n",
       "        cell.code_mirror.replaceSelection(snippet, 'around');\n",
       "        //cell.focus_editor();\n",
       "    }\n",
       "\n",
       "    /**\n",
       "        from https://github.com/moble/jupyter_boilerplate/blob/master/main.js\n",
       "    */\n",
       "    function callback_insert_snippet (evt) {\n",
       "        // this (or event.currentTarget, see below) always refers to the DOM\n",
       "        // element the listener was attached to - see\n",
       "        // http://stackoverflow.com/questions/12077859\n",
       "        insert_snippet_code($(evt.currentTarget).data('snippet-code'));\n",
       "    }\n",
       "    function build_menu_element (menu_item_spec, direction) {\n",
       "        // Create the menu item html element\n",
       "        var element = $('<li/>');\n",
       "\n",
       "        if (typeof menu_item_spec == 'string') {\n",
       "            if (menu_item_spec != '---') {\n",
       "                return element.html(menu_item_spec)\n",
       "                        .addClass('ui-state-disabled')\n",
       "                        .attr({\"style\": \"padding:2px .4em\"})\n",
       "                       ;\n",
       "            }\n",
       "            return element.addClass('divider');\n",
       "        }\n",
       "\n",
       "        var a = $('<a/>')\n",
       "            .attr('href', '#')\n",
       "            .html(menu_item_spec.name)\n",
       "            .appendTo(element);\n",
       "        if (menu_item_spec.hasOwnProperty('snippet')) {\n",
       "            var snippet = menu_item_spec.snippet;\n",
       "            if (typeof snippet == 'string' || snippet instanceof String) {\n",
       "                snippet = [snippet];\n",
       "            }\n",
       "            a.attr({\n",
       "                'title' : \"\", // Do not remove this, even though it's empty!\n",
       "                'data-snippet-code' : snippet.join('\\n'),\n",
       "            })\n",
       "            .on('click', callback_insert_snippet)\n",
       "            .addClass('snippet');\n",
       "        }\n",
       "        else if (menu_item_spec.hasOwnProperty('internal-link')) {\n",
       "            a.attr('href', menu_item_spec['internal-link']);\n",
       "        }\n",
       "        else if (menu_item_spec.hasOwnProperty('external-link')) {\n",
       "            a.empty();\n",
       "            a.attr('href', menu_item_spec['external-link']);\n",
       "            a.attr({\n",
       "                'target' : '_blank',\n",
       "                'title' : 'Opens in a new window',\n",
       "            });\n",
       "            $('<i class=\"fa fa-external-link menu-icon pull-right\"/>').appendTo(a);\n",
       "            $('<span/>').html(menu_item_spec.name).appendTo(a);\n",
       "        }\n",
       "\n",
       "        if (menu_item_spec.hasOwnProperty('sub-menu')) {\n",
       "            element\n",
       "                .addClass('dropdown-submenu')\n",
       "                .toggleClass('dropdown-submenu-left', direction === 'left');\n",
       "            var sub_element = $('<ul class=\"dropdown-menu\"/>')\n",
       "                .toggleClass('dropdown-menu-compact', menu_item_spec.overlay === true) // For space-saving menus\n",
       "                .appendTo(element);\n",
       "\n",
       "            var new_direction = (menu_item_spec['sub-menu-direction'] === 'left') ? 'left' : 'right';\n",
       "            for (var j=0; j<menu_item_spec['sub-menu'].length; ++j) {\n",
       "                var sub_menu_item_spec = build_menu_element(menu_item_spec['sub-menu'][j], new_direction);\n",
       "                if(sub_menu_item_spec !== null) {\n",
       "                    sub_menu_item_spec.appendTo(sub_element);\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return element;\n",
       "    }\n",
       "\n",
       "    function menu_setup (menu_item_specs, sibling, insert_before_sibling) {\n",
       "        for (var i=0; i<menu_item_specs.length; ++i) {\n",
       "            var menu_item_spec;\n",
       "            if (insert_before_sibling) {\n",
       "                menu_item_spec = menu_item_specs[i];\n",
       "            } else {\n",
       "                menu_item_spec = menu_item_specs[menu_item_specs.length-1-i];\n",
       "            }\n",
       "            var direction = (menu_item_spec['menu-direction'] == 'left') ? 'left' : 'right';\n",
       "            var menu_element = build_menu_element(menu_item_spec, direction);\n",
       "            // We need special properties if this item is in the navbar\n",
       "            if ($(sibling).parent().is('ul.nav.navbar-nav')) {\n",
       "                menu_element\n",
       "                    .addClass('dropdown')\n",
       "                    .removeClass('dropdown-submenu dropdown-submenu-left');\n",
       "                menu_element.children('a')\n",
       "                    .addClass('dropdown-toggle')\n",
       "                    .attr({\n",
       "                        'id': name+'_menu',\n",
       "                        'data-toggle' : 'dropdown',\n",
       "                        'aria-expanded' : 'false'\n",
       "                    });\n",
       "            }\n",
       "\n",
       "            // Insert the menu element into DOM\n",
       "            menu_element[insert_before_sibling ? 'insertBefore': 'insertAfter'](sibling);\n",
       "        }\n",
       "    }\n",
       "    /** end from */\n",
       "\n",
       "\n",
       "    function self_cleanup() {\n",
       "        var cell_element = $(\"script[class='to-be-removed']\").parents('.cell');\n",
       "        var cell_idx = Jupyter.notebook.get_cell_elements().index(cell_element);\n",
       "        var cell = Jupyter.notebook.get_cell(cell_idx);\n",
       "        var to_remove = -1;\n",
       "        for (var i = 0; i < cell.output_area.outputs.length; ++i) {\n",
       "            var oa = cell.output_area.outputs[i];\n",
       "            if (oa.output_type == \"display_data\"\n",
       "                && typeof oa.data[\"text/html\"] != 'undefined'\n",
       "                && oa.data[\"text/html\"].indexOf(' class=\"to-be-removed\"') >= 0) {\n",
       "                to_remove = i;\n",
       "                break;\n",
       "            }\n",
       "        }\n",
       "        if (to_remove == -1) {\n",
       "            console.log(\"cannot find toberemoved\");\n",
       "        } else {\n",
       "            cell.output_area.outputs.splice(to_remove, 1);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    function toolbar_setup(actions) {\n",
       "        var buttons = [];\n",
       "        for (var i = 0; i < actions.length; ++i) {\n",
       "            var setup = actions[i].setup;\n",
       "            if (typeof setup.handler == 'string') {\n",
       "                setup.handler = resolve_function(tool_api, setup.handler);\n",
       "            }\n",
       "            buttons.push(Jupyter.actions.register(actions[i].setup,\n",
       "                actions[i].name, name));\n",
       "        }\n",
       "        $(\"#\"+name+\"-toolbar\").remove();\n",
       "        Jupyter.toolbar.add_buttons_group(buttons, name+\"-toolbar\");\n",
       "    }\n",
       "\n",
       "    function replace_menu_snippets(menu_spec, orig, dest) {\n",
       "        if (menu_spec.hasOwnProperty(\"snippet\")) {\n",
       "            var snippet = menu_spec.snippet;\n",
       "            if (typeof snippet == \"string\" || snippet instanceof String) {\n",
       "                menu_spec[\"snippet\"] = snippet.replace(orig, dest);\n",
       "            } else {\n",
       "                for (var i = 0; i < snippet.length; ++i) {\n",
       "                    menu_spec[\"snippet\"][i] = snippet[i].replace(orig, dest);\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "        if (menu_spec.hasOwnProperty(\"sub-menu\")) {\n",
       "            for (var i = 0; i < menu_spec[\"sub-menu\"].length; ++i) {\n",
       "                replace_menu_snippets(menu_spec[\"sub-menu\"][i], orig, dest);\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    function load_ipython_extension() {\n",
       "\n",
       "        var mycellelt = $(\"#\"+ssid).parents('.cell');\n",
       "        var myidx = Jupyter.notebook.get_cell_elements().index(mycellelt);\n",
       "        var import_cell = Jupyter.notebook.get_cell(myidx);\n",
       "\n",
       "        var alias = detect_import(import_cell, name);\n",
       "        tool_api.module_alias = alias;\n",
       "        if (alias && alias != name) {\n",
       "            var orig = new RegExp(\"\\\\b\"+name+\"\\\\b\", \"g\");\n",
       "            replace_menu_snippets(menu, orig, alias);\n",
       "        }\n",
       "\n",
       "        if (toolbar) {\n",
       "            toolbar_setup(toolbar);\n",
       "        }\n",
       "\n",
       "        $(\"#\"+name+\"_menu\").parent().remove();\n",
       "        if (menu) {\n",
       "            menu_setup([menu], $(\"#help_menu\").parent(), true);\n",
       "        }\n",
       "\n",
       "        if (tool_api.hasOwnProperty(\"post_install_callback\")) {\n",
       "            tool_api.post_install_callback();\n",
       "        }\n",
       "\n",
       "        setTimeout(self_cleanup, 5000);\n",
       "    };\n",
       "\n",
       "    load_ipython_extension();\n",
       "}\n",
       "\n",
       "function resolve_toolbar_handlers(tool_api, toolbar_spec) {\n",
       "    for (var i = 0; i < toolbar_spec.length; ++i) {\n",
       "        func = resolve_function(tool_api, toolbar_spec[i][\"setup\"][\"handler\"]);\n",
       "        toolbar_spec[i][\"setup\"][\"handler\"] = func;\n",
       "    }\n",
       "    return toolbar_spec\n",
       "}\n",
       "\n",
       "\n",
       "    var biolqm_jsapi = { action_upload_model: function() {\n",
       "        var cell = Jupyter.notebook.get_selected_cell();\n",
       "        cell.set_text('lqm = '+biolqm_jsapi.module_alias+'.upload()');\n",
       "        cell.focus_editor();\n",
       "    } };\n",
       "    colomoto_extension(Jupyter, \"colomoto-setup-4111321\", \"biolqm\", {\"name\": \"bioLQM\", \"sub-menu\": [{\"name\": \"Upload model\", \"snippet\": [\"lqm = biolqm.upload()\"]}, {\"name\": \"Load model\", \"snippet\": [\"lqm = biolqm.load(\\\"model.sbml\\\")\"]}, \"---\", {\"name\": \"Export to file\", \"sub-menu\": [{\"name\": \"SBML-qual v1.0 (.sbml)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.sbml\\\", \\\"sbml\\\")\"]}, \"Functions formats\", {\"name\": \"BoolNet (.bnet)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.bnet\\\", \\\"bnet\\\")\"]}, {\"name\": \"BooleanNet (.booleannet)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.booleannet\\\", \\\"booleannet\\\")\"]}, {\"name\": \"BoolSim (.boolsim)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.boolsim\\\", \\\"boolsim\\\")\"]}, {\"name\": \"Raw Boolean functions (.boolfunctions)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.boolfunctions\\\", \\\"boolfunctions\\\")\"]}, {\"name\": \"Truth table (.tt)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.tt\\\", \\\"tt\\\")\"]}, \"Petri net formats\", {\"name\": \"APNN (.apnn)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.apnn\\\", \\\"apnn\\\")\"]}, {\"name\": \"INA (.ina)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.ina\\\", \\\"ina\\\")\"]}, {\"name\": \"PNML (.pnml)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.pnml\\\", \\\"pnml\\\")\"]}, \"Dedicated formats\", {\"name\": \"GINML (.ginml)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.ginml\\\", \\\"ginml\\\")\"]}, {\"name\": \"GNA non-xml (.gna)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.gna\\\", \\\"gna\\\")\"]}, {\"name\": \"MaBoSS (.bnd)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.bnd\\\", \\\"bnd\\\")\"]}, {\"name\": \"Pint (.an)\", \"snippet\": [\"biolqm.save(lqm, \\\"mymodel.an\\\", \\\"an\\\")\"]}]}, {\"name\": \"Convert to tool\", \"sub-menu\": [{\"name\": \"GINsim\", \"snippet\": [\"lrg = biolqm.to_ginsim(lqm)\"]}, {\"name\": \"MaBoSS\", \"snippet\": [\"masim = biolqm.to_maboss(lqm)\"]}, {\"name\": \"Pint\", \"snippet\": [\"an = biolqm.to_pint(lqm)\"]}]}, \"---\", {\"name\": \"Compute fixpoints\", \"snippet\": [\"fps = biolqm.fixpoints(lqm)\"]}, {\"name\": \"Compute trap spaces\", \"snippet\": [\"traps = biolqm.trapspace(lqm)\"]}, \"---\", {\"name\": \"Model modifications\", \"sub-menu\": [{\"name\": \"Perturbation\", \"snippet\": [\"lqm_mod = biolqm.perturbation(lqm, \\\"node%0\\\")\"]}, {\"name\": \"Booleanization\", \"snippet\": [\"lqm_bool = biolqm.booleanize(lqm)\"]}, {\"name\": \"Reduction\", \"snippet\": [\"lqm_red = biolqm.reduce(lqm, \\\"fixed,output,duplicate\\\")\"]}, {\"name\": \"Reversal\", \"snippet\": [\"lqm_rev = biolqm.reverse(lqm)\"]}, {\"name\": \"Sanitize\", \"snippet\": [\"lqm_san = biolqm.sanitize(lqm)\"]}]}, \"---\", {\"name\": \"Documentation\", \"external-link\": \"http://ginsim.naldi.info/biolqm/site/doc/index.html\"}]}, [{\"name\": \"upload\", \"setup\": {\"icon\": \"fa-upload\", \"help\": \"Upload model\", \"handler\": \"action_upload_model\"}}], biolqm_jsapi);\n",
       "     }</script><style type=\"text/css\">\n",
       "    #biolqm_menu {\n",
       "        color: #00007f;\n",
       "        background-color: white;\n",
       "    }\n",
       "    \n",
       "    div#biolqm-toolbar:before {\n",
       "        content: \"bioLQM\";\n",
       "        float: left;\n",
       "        margin-left: 1em;\n",
       "        margin-right: 1em;\n",
       "        margin-top: 0.2em;\n",
       "    }\n",
       "    div#biolqm-toolbar,\n",
       "    div#biolqm-toolbar button {\n",
       "        color: #00007f;\n",
       "    }\n",
       "    </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\" id=\"colomoto-setup-3416226\" class=\"to-be-removed\">\n",
       "        if (typeof Jupyter != 'undefined') {\n",
       "            \n",
       "    var ginsim_jsapi = { action_upload_model: function() {\n",
       "        var cell = Jupyter.notebook.get_selected_cell();\n",
       "        cell.set_text('lrg = '+ginsim_jsapi.module_alias+'.upload()');\n",
       "        cell.focus_editor();\n",
       "    } };\n",
       "    colomoto_extension(Jupyter, \"colomoto-setup-3416226\", \"ginsim\", {\"name\": \"GINsim\", \"sub-menu\": [{\"name\": \"Upload model\", \"snippet\": [\"lrg = ginsim.upload()\"]}, {\"name\": \"Load model\", \"snippet\": [\"lrg = ginsim.load(\\\"model.zginml\\\")\"]}, \"---\", {\"name\": \"Export to file\", \"sub-menu\": [{\"name\": \"MaBoSS (.bnd, .cfg)\", \"snippet\": [\"ginsim.service(\\\"maboss\\\").export(lrg, \\\"mymodel.bnd\\\")\"]}]}, {\"name\": \"Convert to tool\", \"sub-menu\": [{\"name\": \"bioLQM\", \"snippet\": [\"lqm = ginsim.to_biolqm(lrg)\"]}, {\"name\": \"MaBoSS\", \"snippet\": [\"masim = ginsim.to_maboss(lrg)\"]}, {\"name\": \"NuSMV\", \"snippet\": [\"smv = ginsim.to_nusmv(lrg)\"]}, {\"name\": \"Pint\", \"snippet\": [\"an = ginsim.to_pint(lrg)\"]}]}, \"---\", {\"name\": \" Display regulatory graph\", \"snippet\": [\"ginsim.show(lrg)\"]}]}, [{\"name\": \"upload\", \"setup\": {\"icon\": \"fa-upload\", \"help\": \"Upload model\", \"handler\": \"action_upload_model\"}}], ginsim_jsapi);\n",
       "     }</script><style type=\"text/css\">\n",
       "    #ginsim_menu {\n",
       "        color: blue;\n",
       "        background-color: white;\n",
       "    }\n",
       "    \n",
       "    div#ginsim-toolbar:before {\n",
       "        content: \"GINsim\";\n",
       "        float: left;\n",
       "        margin-left: 1em;\n",
       "        margin-right: 1em;\n",
       "        margin-top: 0.2em;\n",
       "    }\n",
       "    div#ginsim-toolbar,\n",
       "    div#ginsim-toolbar button {\n",
       "        color: blue;\n",
       "    }\n",
       "    </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\" id=\"colomoto-setup-9321531\" class=\"to-be-removed\">\n",
       "        if (typeof Jupyter != 'undefined') {\n",
       "            \n",
       "    var minibn_jsapi = {  };\n",
       "    colomoto_extension(Jupyter, \"colomoto-setup-9321531\", \"minibn\", null, null, minibn_jsapi);\n",
       "     }</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\" id=\"colomoto-setup-1412747\" class=\"to-be-removed\">\n",
       "        if (typeof Jupyter != 'undefined') {\n",
       "            \n",
       "    var mymodule_jsapi = {  };\n",
       "    colomoto_extension(Jupyter, \"colomoto-setup-1412747\", \"mymodule\", {\"name\": \"MaBoSS\", \"sub-menu\": [{\"name\": \"Load MaBoSS file\", \"snippet\": [\"masim = maboss.load(\\\"filename.bnd\\\", \\\"filename.cfg\\\")\"]}, {\"name\": \"Network\", \"sub-menu\": [{\"name\": \"Set istate\", \"snippet\": [\"maboss.wg_set_istate(masim)\"]}, {\"name\": \"Get initial state\", \"snippet\": [\"masim.get_initial_state()\"]}]}, {\"name\": \"Simulation\", \"sub-menu\": [{\"name\": \"Create mutant\", \"snippet\": [\"maboss.wg_make_mutant(masim)\"]}, {\"name\": \"Run\", \"snippet\": [\"simres = masim.run()\"]}, {\"name\": \"Set initial states\", \"snippet\": [\"masim.network.set_istate([\\\"mygenelist\\\"],{})\"]}, {\"name\": \"Set output\", \"snippet\": [\"maboss.wg_set_output(masim)\"]}]}, {\"name\": \"Results\", \"sub-menu\": [{\"name\": \"Save results\", \"snippet\": [\"simres.save(\\\"filename\\\")\"]}, {\"name\": \"Plot piechart\", \"snippet\": [\"simres.plot_piechart()\"]}, {\"name\": \"Plot trajectory\", \"snippet\": [\"simres.plot_trajectory()\"]}]}, {\"name\": \"Conversion\", \"sub-menu\": [{\"name\": \"Convert to biolqm\", \"snippet\": [\"biolqm_model = maboss.to_biolqm(masim)\"]}, {\"name\": \"Convert to minibn\", \"snippet\": [\"minibn_model = maboss.to_minibn(masim)\"]}]}, \"---\", {\"name\": \"Documentation\", \"external-link\": \"http://pymaboss.readthedocs.io\"}]}, null, mymodule_jsapi);\n",
       "     }</script><style type=\"text/css\">\n",
       "    #mymodule_menu {\n",
       "        color: green;\n",
       "        background-color: white;\n",
       "    }\n",
       "    </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\" id=\"colomoto-setup-7714888\" class=\"to-be-removed\">\n",
       "        if (typeof Jupyter != 'undefined') {\n",
       "            \n",
       "    var pypint_jsapi = { action_upload_model: function() {\n",
       "        var cell = Jupyter.notebook.get_selected_cell();\n",
       "        cell.set_text('an = '+pypint_jsapi.module_alias+'.load()');\n",
       "        cell.focus_editor();\n",
       "    },\n",
       "action_enable_debug: function() {\n",
       "        IPython.notebook.kernel.execute(pypint_jsapi.module_alias+\".enable_dbg()\");\n",
       "        pypint_jsapi.debug_enabled(true);\n",
       "    },\n",
       "action_disable_debug: function() {\n",
       "        IPython.notebook.kernel.execute(pypint_jsapi.module_alias+\".disable_dbg()\");\n",
       "        pypint_jsapi.debug_enabled(false);\n",
       "    },\n",
       "btn_enable_debug: null,\n",
       "btn_disable_debug: null,\n",
       "debug_enabled: function(enabled) {\n",
       "        if (enabled) {\n",
       "            this.btn_enable_debug.hide();\n",
       "            this.btn_disable_debug.show();\n",
       "        } else {\n",
       "            this.btn_enable_debug.show();\n",
       "            this.btn_disable_debug.hide();\n",
       "        }\n",
       "    },\n",
       "post_install_callback: function() {\n",
       "        this.btn_enable_debug = $(\"#pypint-toolbar > button[data-jupyter-action='pypint:enable-debug']\");\n",
       "        this.btn_disable_debug = $(\"#pypint-toolbar > button[data-jupyter-action='pypint:disable-debug']\");\n",
       "        this.btn_enable_debug[0].innerHTML = \"enable debug\";\n",
       "        this.btn_disable_debug[0].innerHTML = \"disable debug\";\n",
       "        this.debug_enabled(0);\n",
       "    } };\n",
       "    colomoto_extension(Jupyter, \"colomoto-setup-7714888\", \"pypint\", {\"name\": \"Pint\", \"sub-menu\": [{\"name\": \"Load model\", \"snippet\": [\"an = pypint.load(\\\"filename_or_URL\\\")\"]}, {\"name\": \"Upload model\", \"snippet\": [\"an = pypint.load()\"]}, \"---\", {\"name\": \"Model description\", \"sub-menu\": [{\"name\": \"Dependency graph\", \"snippet\": [\"an.dependency_graph()\"]}, {\"name\": \"Model summary\", \"snippet\": [\"an.summary()\"]}]}, {\"name\": \"Export to file\", \"sub-menu\": [{\"name\": \"Pint native format (.an)\", \"snippet\": [\"an.export(\\\"an\\\")\"]}, {\"name\": \"NuSMV file (.smv)\", \"snippet\": [\"an.export(\\\"nusmv\\\")\"]}, {\"name\": \"Safe Petri net in PEP format (.ll)\", \"snippet\": [\"an.export(\\\"pep\\\")\"]}, {\"name\": \"Safe Petri net in ROMEO format (.xml)\", \"snippet\": [\"an.export(\\\"romeo\\\")\"]}]}, {\"name\": \"Convert to tool\", \"sub-menu\": [{\"name\": \"ITS-Tools\", \"snippet\": [\"itsm = an.to_its()\"]}, {\"name\": \"NuSMV\", \"snippet\": [\"smv = an.to_nusmv()\"]}]}, \"---\", {\"name\": \"Model transformation\", \"sub-menu\": [{\"name\": \"Change initial state\", \"snippet\": [\"an.having(a=1,b=1)\"]}, {\"name\": \"Lock automata (mutations)\", \"snippet\": [\"an.lock({\\\"a\\\":1,\\\"b\\\":0})\"]}, {\"name\": \"Disable local states\", \"snippet\": [\"an.disable({\\\"a\\\":1,\\\"b\\\":1})\"]}, {\"name\": \"Goal-oriented reduction\", \"snippet\": [\"an.reduce_for_goal(\\\"a=1\\\")\"]}]}, \"---\", {\"name\": \"Compute mutations for cutting goal reachability\", \"snippet\": [\"an.oneshot_mutations_for_cut(\\\"a=1\\\")\"]}, {\"name\": \"Compute cut sets of paths to goal\", \"snippet\": [\"an.cutsets(\\\"a=1\\\")\"]}, {\"name\": \"Compute bifurcation transitions from goal\", \"snippet\": [\"an.bifurcations(\\\"a=1\\\")\"]}, {\"name\": \"Verify reachability of goal\", \"snippet\": [\"an.reachability(\\\"a=1\\\")\"]}, \"---\", {\"name\": \"Local Causality Graph\", \"sub-menu\": [{\"name\": \"Full LCG\", \"snippet\": [\"an.full_lcg()\"]}, {\"name\": \"Simple LCG for goal reachability over-approximation\", \"snippet\": [\"an.simple_lcg(\\\"a=1\\\")\"]}, {\"name\": \"Saturated LCG for goal reachability under-approximation\", \"snippet\": [\"an.saturated_lcg(\\\"a=1\\\")\"]}, {\"name\": \"Worth LCG for goal-oriented model reduction\", \"snippet\": [\"an.worth_lcg(\\\"a=1\\\")\"]}]}, \"---\", {\"name\": \"State graph analysis\", \"sub-menu\": [{\"name\": \"Count reachable states\", \"snippet\": [\"an.count_reachable_states()\"]}, {\"name\": \"Reachable state graph\", \"snippet\": [\"an.reachable_stategraph()\"]}, {\"name\": \"Reachable attractors\", \"snippet\": [\"an.reachable_attractors()\"]}, {\"name\": \"Fixpoints\", \"snippet\": [\"an.fixpoints()\"]}]}, \"---\", {\"name\": \"Goal specification\", \"sub-menu\": [{\"name\": \"Simple goal\", \"snippet\": [\"\\\"a=1\\\"\"]}, {\"name\": \"Sub-state goal\", \"snippet\": [\"\\\"a=1,b=1\\\"\"]}, {\"name\": \"Sequence of simple goals\", \"snippet\": [\"\\\"a=1\\\",\\\"b=1\\\"\"]}, {\"name\": \"Sequence of sub-state goals\", \"snippet\": [\"\\\"a=1,c=1\\\",\\\"b=1,d=0\\\"\"]}, {\"name\": \"Alternative goals\", \"snippet\": [\"pypint.Goal(\\\"a=1\\\")|pypint.Goal(\\\"b=1\\\")\"]}]}, \"---\", {\"name\": \"Documentation\", \"external-link\": \"https://loicpauleve.name/pint/doc/api.html\"}]}, [{\"name\": \"upload\", \"setup\": {\"icon\": \"fa-upload\", \"help\": \"Upload model\", \"handler\": \"action_upload_model\"}}, {\"name\": \"enable-debug\", \"setup\": {\"help\": \"Enable debug\", \"handler\": \"action_enable_debug\"}}, {\"name\": \"disable-debug\", \"setup\": {\"help\": \"Disable debug\", \"handler\": \"action_disable_debug\"}}], pypint_jsapi);\n",
       "     }</script><style type=\"text/css\">\n",
       "    #pypint_menu {\n",
       "        color: red;\n",
       "        background-color: white;\n",
       "    }\n",
       "    \n",
       "    div#pypint-toolbar:before {\n",
       "        content: \"Pint\";\n",
       "        float: left;\n",
       "        margin-left: 1em;\n",
       "        margin-right: 1em;\n",
       "        margin-top: 0.2em;\n",
       "    }\n",
       "    div#pypint-toolbar,\n",
       "    div#pypint-toolbar button {\n",
       "        color: red;\n",
       "    }\n",
       "    </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ginsim\n",
    "import biolqm\n",
    "import maboss\n",
    "import pypint\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from colomoto_jupyter import tabulate # for fixpoint table display\n",
    "from itertools import combinations  # for iterating over sets\n",
    "import matplotlib.pyplot as plt # for modifying plots\n",
    "from scipy.stats import pearsonr # for calculating the pearson correlation\n",
    "#import pyboolnet # for reproduce the original results\n",
    "from rpy2.robjects.packages import importr\n",
    "from rpy2.robjects import IntVector\n",
    "from rpy2.robjects.vectors import ListVector\n",
    "from rpy2 import robjects\n",
    "from rpy2.robjects import conversion\n",
    "from rpy2.robjects.conversion import localconverter\n",
    "from rpy2.robjects import pandas2ri\n",
    "boolnet = importr(\"BoolNet\")\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "home = \"persistent/\" # home path for Logic Model Merger\n",
    "model_name = \"Palma2021\"\n",
    "txt_file = home + \"LogicModelMerger/Models/\" + model_name + \".txt\"\n",
    "sbml_file = home + \"LogicModelMerger/Models/\" + model_name + \".sbml\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Palma et al. model\n",
    "\n",
    "The model is downloaded from the [Biodivine Boolean Models (BBM) Benchmark Dataset](https://github.com/sybila/biodivine-boolean-models/tree/main/models/%5Bid-100%5D__%5Bvar-18%5D__%5Bin-3%5D__%5BACUTE-MYELOID-LEUKEMIA%5D), which is taken directly from the text in the paper.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean network simulations of the model\n",
    "\n",
    "As described in the paper:  \n",
    ">In silico simulations were performed for different genomic profiles, including loss of function of NPM1 and/or DNMT3A and/or gain of function of FLT3. In our Boolean model simulation, oncogenes and TSG were set to fixed values of 1 and 0, respectively. Each node of the network is associated to a Boolean expression, describing how the value of the node changes depending on the activities of the upstream regulatory nodes. Nonmutated genes were considered active or inactive depending on the activities of the upstream regulatory no\n",
    "des. This approach enabled to generate eight “mutation-specific” models. To estimate the level of activation of each phenotype, we assumed that the activities of the nodes directly linked to a phenotype have an additive effect on the value of the phenotype. Hence, we estimated phenotype activation by adding up the contribution of the upstream activators and subtracting that of the upstream inhibitors. Thus, a phenotype is considered “most active” whenever all activator nodes are on and inhibitors off at a steady state (Figure 5).\n",
    "\n",
    "\n",
    "We are going to reproduce these results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img class=\"unconfined\" width=\"585px\" src=\"data:image/svg+xml;base64,<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 20001102//EN" "http://www.w3.org/TR/2000/CR-SVG-20001102/DTD/svg-20001102.dtd">
<svg width="585.0" height="170.0" xmlns="http://www.w3.org/2000/svg" version="1.1">
  <style type="text/css">
text {
  font-family: 'SansSerif';
  font-size: 12px;
}
.node .shape {
fill: #ffffff;
stroke: #000000;
}
.node text {
fill: #000000;
}
.edge {
stroke: #000000;
fill: none;
stroke-width: 1;
}
.edge_POSITIVE {
stroke: #00c800;
}
.edge_NEGATIVE {
stroke: #c80000;
}
.edge_DUAL {
stroke: #0000c8;
}
</style>
  <defs>
    <marker id="Marker_NEGATIVE_c80000" viewBox="-7 -7 12 15" orient="auto" markerHeight="9" markerWidth="9">
      <path stroke="#c80000" fill="#c80000" d="M -1 -4 L -1 4 L 1 4 L 1 -4 z"/>
    </marker>
  </defs>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 55.0 87.0 L 384.0 87.0"/>
  <defs>
    <marker id="Marker_POSITIVE_00c800" viewBox="-7 -7 12 15" orient="auto" markerHeight="9" markerWidth="9">
      <path stroke="#00c800" fill="#00c800" d="M -4 -5 L -2 0 L -4 5 L 2 0 z"/>
    </marker>
  </defs>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 167.0 37.0 L 123.0 71.0"/>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 207.0 32.0 L 306.0 77.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 207.0 27.0 L 456.0 82.0"/>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 458.0 93.0 L 283.0 145.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 383.0 92.0 L 133.0 147.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 392.0 102.0 L 348.0 136.0"/>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 130.0 87.0 L 534.0 87.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 207.0 93.0 L 381.0 145.0"/>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 233.0 93.0 L 58.0 145.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 242.0 102.0 L 198.0 136.0"/>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 271.0 102.0 L 316.0 136.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 39.0 37.0 L 99.0 136.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 54.0 37.0 L 234.0 137.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 533.0 90.0 L 58.0 149.0"/>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 92.0 37.0 L 48.0 71.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 121.0 37.0 L 166.0 71.0"/>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 132.0 32.0 L 231.0 77.0"/>
  <path class="edge edge_POSITIVE" marker-end="url(#Marker_POSITIVE_00c800)" d="M 132.0 30.0 L 456.0 144.0"/>
  <path class="edge edge_NEGATIVE" marker-end="url(#Marker_NEGATIVE_c80000)" d="M 332.0 100.0 L 332.0 139.0"/>
  <g id="AKT1" class="node">
    <ellipse id="AKT1_shape" class="shape" cx="32.5" cy="87.5" rx="22.5" ry="12.5"/>
    <text  id="AKT1_text" x="32" y="90" text-anchor="middle">AKT1</text>
  </g>
  <g id="CDKN2A" class="node">
    <ellipse id="CDKN2A_shape" class="shape" cx="107.5" cy="87.5" rx="22.5" ry="12.5"/>
    <text  id="CDKN2A_text" x="107" y="90" text-anchor="middle">CDKN2A</text>
  </g>
  <g id="BCL2" class="node">
    <ellipse id="BCL2_shape" class="shape" cx="32.5" cy="152.5" rx="22.5" ry="12.5"/>
    <text  id="BCL2_text" x="32" y="155" text-anchor="middle">BCL2</text>
  </g>
  <g id="CCND1" class="node">
    <ellipse id="CCND1_shape" class="shape" cx="107.5" cy="152.5" rx="22.5" ry="12.5"/>
    <text  id="CCND1_text" x="107" y="155" text-anchor="middle">CCND1</text>
  </g>
  <g id="CEBPA" class="node">
    <ellipse id="CEBPA_shape" class="shape" cx="182.5" cy="87.5" rx="22.5" ry="12.5"/>
    <text  id="CEBPA_text" x="182" y="90" text-anchor="middle">CEBPA</text>
  </g>
  <g id="DNMT3A" class="node">
    <ellipse id="DNMT3A_shape" class="shape" cx="32.5" cy="22.5" rx="22.5" ry="12.5"/>
    <text  id="DNMT3A_text" x="32" y="25" text-anchor="middle">DNMT3A</text>
  </g>
  <g id="MAPK1" class="node">
    <ellipse id="MAPK1_shape" class="shape" cx="257.5" cy="87.5" rx="22.5" ry="12.5"/>
    <text  id="MAPK1_text" x="257" y="90" text-anchor="middle">MAPK1</text>
  </g>
  <g id="ETV6" class="node">
    <ellipse id="ETV6_shape" class="shape" cx="182.5" cy="152.5" rx="22.5" ry="12.5"/>
    <text  id="ETV6_text" x="182" y="155" text-anchor="middle">ETV6</text>
  </g>
  <g id="FBXW7" class="node">
    <ellipse id="FBXW7_shape" class="shape" cx="332.5" cy="87.5" rx="22.5" ry="12.5"/>
    <text  id="FBXW7_text" x="332" y="90" text-anchor="middle">FBXW7</text>
  </g>
  <g id="FLT3" class="node">
    <ellipse id="FLT3_shape" class="shape" cx="107.5" cy="22.5" rx="22.5" ry="12.5"/>
    <text  id="FLT3_text" x="107" y="25" text-anchor="middle">FLT3</text>
  </g>
  <g id="GSK3B" class="node">
    <ellipse id="GSK3B_shape" class="shape" cx="407.5" cy="87.5" rx="22.5" ry="12.5"/>
    <text  id="GSK3B_text" x="407" y="90" text-anchor="middle">GSK3B</text>
  </g>
  <g id="HOXA9" class="node">
    <ellipse id="HOXA9_shape" class="shape" cx="482.5" cy="87.5" rx="22.5" ry="12.5"/>
    <text  id="HOXA9_text" x="482" y="90" text-anchor="middle">HOXA9</text>
  </g>
  <g id="MEIS1" class="node">
    <ellipse id="MEIS1_shape" class="shape" cx="257.5" cy="152.5" rx="22.5" ry="12.5"/>
    <text  id="MEIS1_text" x="257" y="155" text-anchor="middle">MEIS1</text>
  </g>
  <g id="MYC" class="node">
    <ellipse id="MYC_shape" class="shape" cx="332.5" cy="152.5" rx="22.5" ry="12.5"/>
    <text  id="MYC_text" x="332" y="155" text-anchor="middle">MYC</text>
  </g>
  <g id="NPM1" class="node">
    <ellipse id="NPM1_shape" class="shape" cx="182.5" cy="22.5" rx="22.5" ry="12.5"/>
    <text  id="NPM1_text" x="182" y="25" text-anchor="middle">NPM1</text>
  </g>
  <g id="SOX4" class="node">
    <ellipse id="SOX4_shape" class="shape" cx="407.5" cy="152.5" rx="22.5" ry="12.5"/>
    <text  id="SOX4_text" x="407" y="155" text-anchor="middle">SOX4</text>
  </g>
  <g id="STAT5A" class="node">
    <ellipse id="STAT5A_shape" class="shape" cx="482.5" cy="152.5" rx="22.5" ry="12.5"/>
    <text  id="STAT5A_text" x="482" y="155" text-anchor="middle">STAT5A</text>
  </g>
  <g id="TP53" class="node">
    <ellipse id="TP53_shape" class="shape" cx="557.5" cy="87.5" rx="22.5" ry="12.5"/>
    <text  id="TP53_text" x="557" y="90" text-anchor="middle">TP53</text>
  </g>
</svg>
\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the model with no phenotype node (to calculate later)\n",
    "palma_lqm = biolqm.load(sbml_file)\n",
    "\n",
    "# visualize\n",
    "palma_lrg = biolqm.to_ginsim(palma_lqm)\n",
    "ginsim.show(palma_lrg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment set-up\n",
    "According to the paper, we are going to set up the simulations on each conditions: \n",
    ">loss of function of NPM1 and/or DNMT3A and/or gain of function of FLT3.  \n",
    ">(oncogenes and TSG were set to fixed values of 1 and 0, respectively.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions\n",
    "In the original paper, R package Boolnet is used to assemble the model and to compute its steady states.   \n",
    "Here we adopt a different approach using a Python library. The `biolqm.fixpoints` function could be used to compute the complete list of logical stable states (or fixpoints) of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the fix value for the simulations\n",
    "wt = biolqm.perturbation(palma_lqm, \"FLT3%0 DNMT3A%1 NPM1%1\")\n",
    "flt3 = biolqm.perturbation(palma_lqm, \"FLT3%1 DNMT3A%1 NPM1%1\")\n",
    "npm1 = biolqm.perturbation(palma_lqm, \"FLT3%0 DNMT3A%1 NPM1%0\")\n",
    "dnmt3a = biolqm.perturbation(palma_lqm, \"FLT3%0 DNMT3A%0 NPM1%1\")\n",
    "npm1_dnmt3a = biolqm.perturbation(palma_lqm,\"FLT3%0 DNMT3A%0 NPM1%0\")\n",
    "flt3_dnmt3a = biolqm.perturbation(palma_lqm, \"FLT3%1 DNMT3A%0 NPM1%1\")\n",
    "flt3_npm1 = biolqm.perturbation(palma_lqm, \"FLT3%1 DNMT3A%1 NPM1%0\")\n",
    "flt3_npm1_dnmt3a = biolqm.perturbation(palma_lqm, \"FLT3%1 DNMT3A%0 NPM1%0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stable states calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the stable states\n",
    "networks = [wt,flt3,npm1,dnmt3a,npm1_dnmt3a,flt3_dnmt3a,flt3_npm1,flt3_npm1_dnmt3a]\n",
    "results = pd.DataFrame()\n",
    "for network in networks:\n",
    "    fps = biolqm.fixpoints(network)\n",
    "    results = pd.concat([results, pd.DataFrame(fps)],ignore_index=True)\n",
    "results.index = [\"wt\",\"flt3\",\"npm1\",\"dnmt3a\",\"npm1_dnmt3a\",\"flt3_dnmt3a\",\"flt3_npm1\",\"flt3_npm1_dnmt3a\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phenotype scores\n",
    ">To estimate the level of activation of each phenotype, we assumed that the activities of the nodes directly linked to a phenotype have an additive effect on the value of the phenotype. Hence, we estimated phenotype activation by adding up the contribution of the upstream activators and subtracting that of the upstream inhibitors. Thus, a phenotype is considered “most active” whenever all activator nodes are on and inhibitors off at a steady state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The logical rules of the phenotypes:\n",
    "- Apoptosis = tp53 & !bcl2\n",
    "- Differentiation =\t(cebpa | etv6) & !meis1, scale\n",
    "- Proliferation\t= (myc | ccnd1 | sox4 | meis1 | stat5a), scale\n",
    "\n",
    "Therefore, the phenotype scores would be:\n",
    "- Apoptosis = tp53 - bcl2\n",
    "- Differentiation =\tcebpa + etv6 - meis1\n",
    "- Proliferation\t= myc + ccnd1 + sox4 + meis1 + stat5a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wt</th>\n",
       "      <th>flt3</th>\n",
       "      <th>npm1</th>\n",
       "      <th>dnmt3a</th>\n",
       "      <th>npm1_dnmt3a</th>\n",
       "      <th>flt3_dnmt3a</th>\n",
       "      <th>flt3_npm1</th>\n",
       "      <th>flt3_npm1_dnmt3a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AKT1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APOPTOSIS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCL2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCND1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEBPA</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIFFERENTIATION</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNMT3A</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETV6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FBXW7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLT3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSK3B</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOXA9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPK1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEIS1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYC</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETWORK</th>\n",
       "      <td>-3</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPM1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROLIFERATION</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOX4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STAT5A</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 wt  flt3  npm1  dnmt3a  npm1_dnmt3a  flt3_dnmt3a  flt3_npm1  \\\n",
       "AKT1              0     1     0       0            0            1          1   \n",
       "APOPTOSIS         1     1     0       1            0            1         -1   \n",
       "BCL2              0     0     0       0            0            0          1   \n",
       "CCND1             0     0     0       0            0            1          0   \n",
       "CDKN2A            1     1     0       1            0            1          0   \n",
       "CEBPA             1     0     1       1            1            0          0   \n",
       "DIFFERENTIATION   2     0     2       2            1            0          0   \n",
       "DNMT3A            1     1     1       0            0            0          1   \n",
       "ETV6              1     0     1       1            1            0          0   \n",
       "FBXW7             1     1     0       1            0            1          0   \n",
       "FLT3              0     1     0       0            0            1          1   \n",
       "GSK3B             1     0     1       1            1            0          0   \n",
       "HOXA9             0     0     1       0            1            0          1   \n",
       "MAPK1             0     1     0       0            0            1          1   \n",
       "MEIS1             0     0     0       0            1            0          0   \n",
       "MYC               0     1     0       0            0            1          1   \n",
       "NETWORK          -3     2    -2      -3            0            3          4   \n",
       "NPM1              1     1     0       1            0            1          0   \n",
       "PROLIFERATION     0     3     0       0            1            4          3   \n",
       "SOX4              0     1     0       0            0            1          1   \n",
       "STAT5A            0     1     0       0            0            1          1   \n",
       "TP53              1     1     0       1            0            1          0   \n",
       "\n",
       "                 flt3_npm1_dnmt3a  \n",
       "AKT1                            1  \n",
       "APOPTOSIS                      -1  \n",
       "BCL2                            1  \n",
       "CCND1                           1  \n",
       "CDKN2A                          0  \n",
       "CEBPA                           0  \n",
       "DIFFERENTIATION                -1  \n",
       "DNMT3A                          0  \n",
       "ETV6                            0  \n",
       "FBXW7                           0  \n",
       "FLT3                            1  \n",
       "GSK3B                           0  \n",
       "HOXA9                           1  \n",
       "MAPK1                           1  \n",
       "MEIS1                           1  \n",
       "MYC                             1  \n",
       "NETWORK                         7  \n",
       "NPM1                            0  \n",
       "PROLIFERATION                   5  \n",
       "SOX4                            1  \n",
       "STAT5A                          1  \n",
       "TP53                            0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the phenotype scores\n",
    "results['APOPTOSIS'] = results['TP53'] - results['BCL2']\n",
    "results['DIFFERENTIATION'] = results['CEBPA'] + results['ETV6'] - results['MEIS1']\n",
    "results['PROLIFERATION'] = results['MYC'] + results['CCND1'] + results['SOX4'] + results['MEIS1'] + results['STAT5A']\n",
    "\n",
    "# calculate the network scores\n",
    "results['NETWORK'] = results['PROLIFERATION'] - results['APOPTOSIS'] - results['DIFFERENTIATION']\n",
    "results = results.T.sort_index()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation\n",
    "### Harzard ratio for death\n",
    ">We next evaluated whether we could use these models to infer some clinical outcomes of patients carrying mutations in NPM1 and/or DNMT3A and/or FLT3. To this end we made use of the clinical information provided by Papaemmanuil et al. reporting the impact of somatic mutations on overall survival and we compared the mutation-specific hazard ratios with the predictions of our models. As a proxy for the predictive power of our model, we defined the “integrated network phenotype” score, which is calculated by subtracting from the activation value of the phenotype “proliferation” the values of “apoptosis” and “differentiation”.\n",
    "\n",
    "Hazard ratio of death is given by a clinical study of 1540 AML patients (AMLSG), where survival analysis was performed using Cox proportional-hazards methods. Mutation-specific hazards for FLT3, NPM1, DNMT3A and their combinations are taken from Table 2 in the publication.   \n",
    ">Reference: Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med. 2016 Jun 9;374(23):2209–21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the the mutation-specific hazard ratios provided by Papaemmanuil et al.\n",
    "hazard_ratio = pd.read_csv(home + \"LogicModelMerger/Data/AMLSG/Hazard Ratio for Death.csv\")\n",
    "hazard_ratio.index = ['Hazard_ratio']\n",
    "results = pd.concat([hazard_ratio,results])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting scatterplots\n",
    "phenotypes = ['NETWORK', 'APOPTOSIS', 'DIFFERENTIATION', 'PROLIFERATION']\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "for i, phenotype in enumerate(phenotypes):\n",
    "    ax = axes[i // 2, i % 2]\n",
    "    # Extract hazard ratio and phenotype values\n",
    "    x = results.loc['Hazard_ratio', :]\n",
    "    y = results.loc[phenotype, :]\n",
    "    \n",
    "    # Calculate Pearson Correlation and p-value\n",
    "    pc, p_value = pearsonr(x, y)\n",
    "\n",
    "    # Linear regression (best-fit line)\n",
    "    slope, intercept = np.polyfit(x, y, 1)\n",
    "    ax.plot(x, slope*x + intercept, color='purple')  # Plotting the best-fit line\n",
    "    \n",
    "    # Create scatterplot\n",
    "    ax.scatter(x, y)\n",
    "    ax.set_title(f'{phenotype} vs Hazard Ratio for Death')\n",
    "    ax.set_xlabel('Hazard Ratio')\n",
    "    ax.set_ylabel(phenotype)\n",
    "\n",
    "    # Annotate each data point\n",
    "    for j in range(len(x)):\n",
    "        label = y.index[j]\n",
    "        ax.annotate(label, (x.iloc[j], y.iloc[j]), textcoords=\"offset points\", xytext=(0,7), ha='center')\n",
    "    \n",
    "    # Annotate Pearson Correlation and p-value\n",
    "    ax.text(0.95, 0.05, f'PC: {pc:.2f}\\np-value: {p_value:.2e}',\n",
    "            transform=ax.transAxes, horizontalalignment='right')\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared with Figure 6 from the [Palma et al. manuscript](https://www.mdpi.com/2075-4426/11/2/117):\n",
    "**The phenotype scores correlate significantly with the death hazard ratio except for 'apoptosis', which matches the results from the paper.**   \n",
    "However, there is minor error in the figure. A hazard ratio of 0 here in their figure would imply that the event (death, in this case) never occurs in the group being studied, which is highly unlikely in a clinical context, especially for a condition like AML. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clinical outcomes\n",
    ">We next investigated the prognostic power of our model by comparing it with additional clinical features derived from the AML TGCA dataset. Specifically, we compared the mutation-specific peripheral blood (PB) and bone marrow (BM) blast percentages with the predictions of our models.\n",
    "   \n",
    "Mutation and clinical data are downloaded from the [NIH GDC website](https://gdc.cancer.gov/about-data/publications/laml_2012):\n",
    "1. [Supplemental Table 06: All somatic mutations with annotation and readcounts from DNA and RNA sequencing](https://api.gdc.cancer.gov/data/0d8851d7-1af0-4054-a527-5db763138400)\n",
    "2. [Patient Clinical Data](https://portal.gdc.cancer.gov/files/c07a64a0-7588-4653-95ef-982b41a1a804?aeTable_offset=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          TCGA_id     mutation  \\\n",
      "0    TCGA-AB-2802  npm1_dnmt3a   \n",
      "1    TCGA-AB-2803           wt   \n",
      "2    TCGA-AB-2804           wt   \n",
      "3    TCGA-AB-2805           wt   \n",
      "4    TCGA-AB-2806           wt   \n",
      "..            ...          ...   \n",
      "192  TCGA-AB-3007         flt3   \n",
      "193  TCGA-AB-3008           wt   \n",
      "194  TCGA-AB-3009           wt   \n",
      "195  TCGA-AB-3011         npm1   \n",
      "196  TCGA-AB-3012           wt   \n",
      "\n",
      "     lab_procedure_blast_cell_outcome_percentage_value  \\\n",
      "0                                                   88   \n",
      "1                                                   44   \n",
      "2                                                   82   \n",
      "3                                                   67   \n",
      "4                                                   90   \n",
      "..                                                 ...   \n",
      "192                                                 90   \n",
      "193                                                 71   \n",
      "194                                                 51   \n",
      "195                                                 91   \n",
      "196                                                 42   \n",
      "\n",
      "     lab_procedure_bone_marrow_blast_cell_outcome_percent_value  \n",
      "0                                                   52           \n",
      "1                                                    0           \n",
      "2                                                    6           \n",
      "3                                                   56           \n",
      "4                                                   81           \n",
      "..                                                 ...           \n",
      "192                                                  7           \n",
      "193                                                 90           \n",
      "194                                                 63           \n",
      "195                                                 83           \n",
      "196                                                 29           \n",
      "\n",
      "[197 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Data file path\n",
    "tsv_file_path_genes = 'https://api.gdc.cancer.gov/data/0d8851d7-1af0-4054-a527-5db763138400'\n",
    "tsv_file_path_clinical = 'https://api.gdc.cancer.gov/data/1fd61a91-90d5-405a-bcef-b9df8b396a21'\n",
    "\n",
    "# Mutation data\n",
    "df_genes = pd.read_csv(tsv_file_path_genes, sep='\\t', usecols=['TCGA_id', 'gene_name'])\n",
    "\n",
    "# clinical data \n",
    "df_clinical = pd.read_csv(tsv_file_path_clinical, sep='\\t', usecols=['bcr_patient_barcode', \n",
    "                                                            'lab_procedure_blast_cell_outcome_percentage_value',\n",
    "                                                            'lab_procedure_bone_marrow_blast_cell_outcome_percent_value'])\n",
    "\n",
    "# Function to determine mutation combination\n",
    "def determine_combination(row):\n",
    "    genes = row['gene_name']\n",
    "    has_flt3 = 'FLT3' in genes\n",
    "    has_npm1 = 'NPM1' in genes\n",
    "    has_dnmt3a = 'DNMT3A' in genes\n",
    "\n",
    "    if has_flt3 and has_npm1 and has_dnmt3a:\n",
    "        return \"flt3_npm1_dnmt3a\"\n",
    "    elif has_flt3 and has_dnmt3a:\n",
    "        return \"flt3_dnmt3a\"\n",
    "    elif has_flt3 and has_npm1:\n",
    "        return \"flt3_npm1\"\n",
    "    elif has_npm1 and has_dnmt3a:\n",
    "        return \"npm1_dnmt3a\"\n",
    "    elif has_flt3:\n",
    "        return \"flt3\"\n",
    "    elif has_npm1:\n",
    "        return \"npm1\"\n",
    "    elif has_dnmt3a:\n",
    "        return \"dnmt3a\"\n",
    "    else:\n",
    "        return \"wt\"\n",
    "\n",
    "# Group by TCGA_id and apply the combination function\n",
    "grouped = df_genes.groupby('TCGA_id').agg(list).reset_index()\n",
    "grouped['mutation'] = grouped.apply(determine_combination, axis=1)\n",
    "\n",
    "# Merge the gene information with the clinical data\n",
    "clinical_result = pd.merge(df_clinical, grouped[['TCGA_id', 'mutation']], left_on='bcr_patient_barcode', right_on='TCGA_id')\n",
    "clinical_result = clinical_result[['TCGA_id', 'mutation', \n",
    "                   'lab_procedure_blast_cell_outcome_percentage_value',\n",
    "                   'lab_procedure_bone_marrow_blast_cell_outcome_percent_value']]\n",
    "\n",
    "print(clinical_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PB_BLAST</th>\n",
       "      <th>BM_BLAST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dnmt3a</th>\n",
       "      <td>63.473684</td>\n",
       "      <td>33.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt3</th>\n",
       "      <td>72.090909</td>\n",
       "      <td>44.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt3_dnmt3a</th>\n",
       "      <td>73.600000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt3_npm1</th>\n",
       "      <td>73.750000</td>\n",
       "      <td>46.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flt3_npm1_dnmt3a</th>\n",
       "      <td>76.882353</td>\n",
       "      <td>44.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npm1</th>\n",
       "      <td>76.500000</td>\n",
       "      <td>53.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npm1_dnmt3a</th>\n",
       "      <td>68.909091</td>\n",
       "      <td>31.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt</th>\n",
       "      <td>60.329897</td>\n",
       "      <td>29.061856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PB_BLAST   BM_BLAST\n",
       "mutation                              \n",
       "dnmt3a            63.473684  33.421053\n",
       "flt3              72.090909  44.681818\n",
       "flt3_dnmt3a       73.600000  48.000000\n",
       "flt3_npm1         73.750000  46.166667\n",
       "flt3_npm1_dnmt3a  76.882353  44.176471\n",
       "npm1              76.500000  53.714286\n",
       "npm1_dnmt3a       68.909091  31.636364\n",
       "wt                60.329897  29.061856"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to numeric\n",
    "clinical_result['lab_procedure_blast_cell_outcome_percentage_value'] = pd.to_numeric(clinical_result['lab_procedure_blast_cell_outcome_percentage_value'])\n",
    "clinical_result['lab_procedure_bone_marrow_blast_cell_outcome_percent_value'] = pd.to_numeric(clinical_result['lab_procedure_bone_marrow_blast_cell_outcome_percent_value'])\n",
    "\n",
    "# aggregate the mean PB of each mutation profile\n",
    "clinical_aggr = clinical_result.pivot_table(\n",
    "    index='mutation', \n",
    "    values=['lab_procedure_blast_cell_outcome_percentage_value','lab_procedure_bone_marrow_blast_cell_outcome_percent_value'],\n",
    "    aggfunc='mean'\n",
    ")\n",
    "clinical_aggr.columns = ['PB_BLAST', 'BM_BLAST']\n",
    "clinical_aggr.to_csv('clinical.csv')\n",
    "clinical_aggr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Merge the results table\n",
    "clinical_results = pd.concat([clinical_aggr.T.sort_index(axis=1),results.sort_index(axis=1)])\n",
    "\n",
    "# Plotting scatterplots\n",
    "clinicals = ['PB_BLAST', 'BM_BLAST']\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "for i, clinical in enumerate(clinicals):\n",
    "    ax = axes[i]\n",
    "    # Extract network scores and clinical outcome values\n",
    "    y = clinical_results.loc['NETWORK', :]\n",
    "    x = clinical_results.loc[clinical, :]\n",
    "    \n",
    "    # Calculate Pearson Correlation and p-value\n",
    "    pc, p_value = pearsonr(x, y)\n",
    "\n",
    "    # Linear regression (best-fit line)\n",
    "    slope, intercept = np.polyfit(x, y, 1)\n",
    "    ax.plot(x, slope*x + intercept, color='purple')  # Plotting the best-fit line\n",
    "    \n",
    "    # Create scatterplot\n",
    "    ax.scatter(x, y)\n",
    "    ax.set_title(f'{clinical} vs Network score')\n",
    "    ax.set_ylabel('Network score')\n",
    "    ax.set_xlabel('%' + clinical)\n",
    "\n",
    "        # Annotate each data point\n",
    "    for j in range(len(x)):\n",
    "        label = y.index[j]\n",
    "        ax.annotate(label, (x.iloc[j], y.iloc[j]), textcoords=\"offset points\", xytext=(0,7), ha='center')\n",
    "\n",
    "    \n",
    "    # Annotate Pearson Correlation and p-value\n",
    "    ax.text(0.95, 0.05, f'PC: {pc:.2f}\\np-value: {p_value:.2e}',\n",
    "            transform=ax.transAxes, horizontalalignment='right')\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared with Figure 6 from the paper:  \n",
    "  \n",
    "**There result is not very consistent, Pearson Correlation: 0.71 vs 0.82 for peripheral blood (PB) blast percentages and 0.45 vs 0.83 bone marrow (BM) blast percentages.**  \n",
    "However, it is also unlikely that blast percentages would be 0 for wild type patients. They did not give the link of dataset they used here, and it is thus possible that different version/indicators might be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "**Results from the Palma et al. 2021 are generally reproducible, expect little discrepencies in the paper.**   \n",
    "However, there is a lack of detailed descriptions in the paper, including the data processing steps and version of the dataset used. One needs to contact the authors to obtain those details in order to fully reproduce their results."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}