31:Tbfe,table_data = { let for_table = [] let cclass = "" //let busted_sce = fdr_or_no for (const key in results_json) { //if (busted_sce === 'BUSTED-PH_' + scenario){ cclass = "C" + (results_json[key]['BUSTED-PH-E']['FG p-val'] < 0.05 ? 1 : 0) + (results_json[key]['BUSTED-PH-E']['BG p-val'] >= 0.05 ? 0 : 1) + (results_json[key]['BUSTED-PH-E']['DF p-val'] < 0.05 ? 1: 0) //} //else { //cclass = "C" + (results_json[key][busted_sce]['fdr significance']['FG'] ? 1 : 0) + (results_json[key][busted_sce]['fdr significance']['BG'] ? 0 : 1) + (results_json[key][busted_sce]['fdr significance']['DIFF'] ? 1: 0) //} const obj = { "ID" : key, //'LRT' : results_json[key]['BUSTED_E']['test for error']['LRT'], //'error P-value' : results_json[key]['BUSTED_E']['test for error']['p-value'], //'filter?' : results_json[key][busted_sce]['filtered?'], "Sequences" : results_json[key]['BUSTED-BOTH-E']['input']['number of sequences'], "Sites" : results_json[key]['BUSTED-BOTH-E']['input']['number of sites'], //"Num_FG" : _.filter(results_json[key]['BUSTED-FG-E']['tested'][0], d=> d ==='test').length, "FG_pval" : results_json[key]['BUSTED-PH-E']['FG p-val'], "FG_LRT" : results_json[key]['BUSTED-FG-E']['test results']['LRT'], "BG_pval" : results_json[key]['BUSTED-PH-E']['BG p-val'], "BG_LRT" : results_json[key]['BUSTED-BG-E']['test results']['LRT'], "DF_pval" : results_json[key]['BUSTED-PH-E']['DF p-val'], "FG_omegas" : String(results_json[key]['BUSTED-FG-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][0]['omega']) +',' + String(results_json[key]['BUSTED-FG-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][1]['omega']) + ',' + String(results_json[key]['BUSTED-FG-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][2]['omega']) + ',' + String(results_json[key]['BUSTED-FG-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][3]['omega']), "BG_omegas" : String(results_json[key]['BUSTED-BG-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][0]['omega']) +',' + String(results_json[key]['BUSTED-BG-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][1]['omega']) + ',' + String (results_json[key]['BUSTED-BG-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][2]['omega']) + ',' + String(results_json[key]['BUSTED-BG-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][3]['omega']), "DF_omegas" : String(results_json[key]['BUSTED-BOTH-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][0]['omega']) +',' + String(results_json[key]['BUSTED-BOTH-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][1]['omega']) + String(results_json[key]['BUSTED-BOTH-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][2]['omega']) + String(results_json[key]['BUSTED-BOTH-E']['fits']['Unconstrained model']['Rate Distributions']['Test'][3]['omega']), "Class" : cclass } for_table.push(obj) } return for_table }32:T8f6,displayed_tree = { let tree = tree_object[0]; var t = tree.render("#tree_container", { //height:height, width:800, 'show-scale' : true, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', //'internal-names' : true, 'is-radial' : false, 'node_circle_size' : constant(0) }); const bs = (n) => { if (tree.is_leafnode (n)) { return 3; } return 1; }; let nodeStates = null; t.node_bubble_size = bs; t.phylotree.node_bubble_size = bs; //t.options["draw-size-bubbles"] = true; t.font_size = 10; t.spacing_x (10); t.phylotree.branch_length_accessor = n=> { //console.log (summary_json[segment_id]); let bl = tree_object[2][n.data.name]; /*if (bl > 0.25) { n.data.censored_bl = true; return 0.25; } else { n.data.censored_bl = false; }*/ return bl; } t.node_label ((n)=>{ return n.data.name.split ("_").slice (0,3).join (" "); //return ""; }); function sort_nodes (asc) { tree.traverse_and_compute (function (n) { var d = 1; if (n.children && n.children.length) { d += d3.max (n.children, function (d) { return d["count_depth"];}); } n["count_depth"] = d; }); tree.resort_children (function (a,b) { return (a["count_depth"] - b["count_depth"]) * (asc ? 1 : -1); }); } sort_nodes (true); t.style_nodes ((e,n) => { e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); if (n.data.name in tree_object[1]) { let binfo = tree_object[1][n.data.name]; e.selectAll ("circle").style ('fill', species_colors (binfo)); e.selectAll ("text").style ('fill', species_colors (binfo)); } //} }); t.style_edges ((e,d) => { if (d.target.data.name in tree_object[1]) { let binfo = tree_object[1][d.target.data.name]; console.log (e); e.style ('stroke', species_colors (binfo)); e.style ('stroke-width', 2.5); } }); t.placenodes(); t.update(); return t; }33:T524,interpretation = { let inter = "" if (chosen_json['BUSTED-PH-E']['FG p-val'] > 0.05){ inter = "There is no evidence of this gene experiencing positive selection associated with the foreground (test) branches." } else { if (chosen_json['BUSTED-PH-E']['BG p-val'] > 0.05) { if (chosen_json['BUSTED-PH-E']['DF p-val'] > 0.05) { inter = "There is evidence of this gene experiencing positive selection associated with the foreground (test) branches, but there is no statistically significant difference between the test and background branches in terms of selective pressure." } else { inter = "There is strong evidence of this gene experiencing positive selection associated with the foreground (test) branches." } } else { inter = "For this gene, selection is acting on the foreground (test) branches, but selection is also acting on background branches. " if (chosen_json['BUSTED-PH-E']['DF p-val'] > 0.05) { inter = inter + "There is no statistically significant difference between the test and background branches in terms of selective pressure." } else { inter = inter + "There is a statistically significant difference between the test and background branches in terms of selective pressure." } } } return inter }34:[] 2f:{"title":"Empirical data analysis using BUSTED-PH+E","cells":[{"id":0,"value":"# Empirical data analysis using BUSTED-PH+E","pinned":false,"hidden":false,"mode":"md"},{"id":1171,"value":"viewof dataset = Inputs.select (_.sortBy (_.keys (urls)), {value: \"Marine mammals\", label: \"Choose a trait to study\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":290,"value":"viewof search = Inputs.search(table_data)","pinned":false,"hidden":false,"mode":"ojs"},{"id":294,"value":"table1 = Inputs.table(search, {\n rows : 15\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":298,"value":"\n** Table 1. **\nColumn definitions are as follows: \n1. **ID** the candidate gene/alignment under study\n2. **Sequences** the number of sequences in the alignment\n3. **Sites** the number of codons in the alignment \n4. **Num_FG** the number of foreground (i.e. Test) species and ancestral nodes in the alignment\n5. **FG_pval** The BUSTED-PH LRT p-value for the test for positive selection in the foreground species\n6. **BG_pval** The BUSTED-PH LRT p-value for the test for positive selection in the background species\n7. **DF_pval** The BUSTED-PH LRT p-value for the test for the difference between the two selective regimes\n8. **Class** the significance of BUSTED-PH p values (FG_pval, BG_pval, and DF_pval respectively) where 1 indicates significance and 0 indicates non-significance at a threshold of 0.05. (e.g., C101 implies Selection on FG, no selection on BG, and statistically significant difference between FG and BG selective regimes)\n","pinned":false,"hidden":false,"mode":"md"},{"id":1168,"value":"C101_genes = _.map(_.filter(table_data, d=> d.Class === \"C101\"), d=> d.ID)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1238,"value":"C111_genes = _.map(_.filter(table_data, d=> d.Class === \"C101\" || d.Class === \"C111\"), d=> d.ID)","pinned":false,"hidden":false,"mode":"ojs"},{"id":308,"value":"## Exploring individual genes","pinned":false,"hidden":false,"mode":"md"},{"id":223,"value":"viewof gene_id = Inputs.select(only_search_results, {label: \"Show this gene:\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"trendLegend = swatches({\n color: species_colors\n //tickFormat: \"%\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1270,"value":"color1 = '#45503B'","pinned":true,"hidden":false,"mode":"ojs"},{"id":1267,"value":"color2 = '#D84797'","pinned":true,"hidden":false,"mode":"ojs"},{"id":48,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":178,"value":"\n** Figure 1. **\nGene tree for ${gene_id} generated by BUSTED-PH. Colors indicate foreground, background, and nuisance branches (if using).\n","pinned":false,"hidden":false,"mode":"md"},{"id":101,"value":"## Interpretation of test for positive selection","pinned":false,"hidden":false,"mode":"md"},{"id":104,"value":"1. The p-value for the test for positive selection in the *test* (foreground) branches is: ** ${chosen_json['BUSTED-PH-E']['FG p-val'].toFixed(3)} **\n\n2. The p-value for the test for positive selection in the *background* branches is: ** ${chosen_json['BUSTED-PH-E']['BG p-val'].toFixed(3)} **\n\n3. The p-value for the test for the *difference between* the test (foreground) and background branch sets is: ** ${chosen_json['BUSTED-PH-E']['DF p-val'].toFixed(3)} **","pinned":false,"hidden":false,"mode":"md"},{"id":116,"value":"** ${interpretation} **","pinned":false,"hidden":false,"mode":"md"},{"id":121,"value":"## Omega values and their proportions","pinned":false,"hidden":false,"mode":"md"},{"id":126,"value":"omega_plot.legend(\"color\", {\n label: \"Omega values →\",\n tickSize: 3,\n ticks: 10,\n //tickLabelRotate: 45,\n //tickFormat: r.tickFormat,\n //round: r.round,\n width: 600,\n height: 45\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":129,"value":"omega_plot = Plot.plot({\n color: {\n type: \"sqrt\",\n //pivot: 4,\n scheme: \"brbg\",\n reverse: true,\n //legend: true\n }, \n y: {\n domain: ['FG', 'BG', 'ALL']\n },\n marks: [\n Plot.barX(stacked_omegas, {y: \"dist\",x: \"proportion\", fill: 'omega', title: (d) =>\n `${d.dist} omega bin #${d.omega_bin}\\n Omega value: ${d.omega},\\nProportion: ${d.proportion}` }),\n Plot.ruleX([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":170,"value":"\n** Figure 2. **\nOmega distribution for ${gene_id}. Sites were categorized into three omega values for each of the three distributions. The three distributions include: foreground branches, background branches, and all branches (in both FG and BG). Prop indicates proportion of sites that fall into the omega category. The largest omega value category for each of the three distributions are: ${stacked_omegas[3]['omega'].toFixed(3)} for the FG, ${stacked_omegas[7]['omega'].toFixed(3)} for the BG, and ${stacked_omegas[11]['omega'].toFixed(3)} for all branches. \n","pinned":false,"hidden":false,"mode":"md"},{"id":44,"value":"### Parsing Data","pinned":false,"hidden":false,"mode":"md"},{"id":239,"value":"chosen_json = results_json[gene_id]//['BUSTED-PH_' + scenario]","pinned":false,"hidden":false,"mode":"ojs"},{"id":371,"value":"results_json = Object.fromEntries(\n Object.entries(full_json).filter(([key, value]) =>\n 'BUSTED-FG-E' in value &&\n 'BUSTED-BG-E' in value &&\n 'BUSTED-BOTH-E' in value &&\n 'BUSTED-PH-E' in value\n )\n)\n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":32,"value":"full_json = await d3.json (urls[dataset] );","pinned":false,"hidden":false,"mode":"ojs"},{"id":270,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1175,"value":"urls = ({'Nectar birds' : \"https://data.hyphy.org/web/busted-ph/wrapper/combined_birds.json\",\n 'Marine mammals' : \"https://data.hyphy.org/web/busted-ph/wrapper/combined_marine.json\",\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":53,"value":"species_colors = d3.scaleOrdinal(d3.schemeCategory10).domain([...species]).range([color1, color2, color3]);","pinned":false,"hidden":false,"mode":"ojs"},{"id":1273,"value":"color3 = '#0A724C'","pinned":false,"hidden":false,"mode":"ojs"},{"id":1250,"value":"species = _.uniq(_.map(chosen_json['BUSTED-FG-E']['tested'][0])).sort()","pinned":false,"hidden":false,"mode":"ojs"},{"id":68,"value":"tree_object = [new phylotree.phylotree(chosen_json['BUSTED-BOTH-E']['input']['trees'][0] || \"()\"), chosen_json['BUSTED-FG-E']['tested'][0] || [], chosen_json['BUSTED-FG-E']['lengths']]","pinned":false,"hidden":false,"mode":"ojs"},{"id":97,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":108,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"stacked_omegas = {\n\n let test = chosen_json['BUSTED-FG-E']['fits']['Unconstrained model']['Rate Distributions']['Test']\n //return test\n let count = 1\n for (const key in test) {\n test[key]['dist'] = 'FG';\n test[key]['omega_bin'] = count;\n count++\n}\n\n//return test\n let bg = chosen_json['BUSTED-BG-E']['fits']['Unconstrained model']['Rate Distributions']['Test']\n count = 1\n for (const key in bg) {\n bg[key]['dist'] = 'BG';\n bg[key]['omega_bin'] = count;\n count++\n}\n let shared = chosen_json['BUSTED-BOTH-E']['fits']['Unconstrained model']['Rate Distributions']['Test']\n count = 1\n for (const key in shared) {\n shared[key]['dist'] = 'ALL';\n shared[key]['omega_bin'] = count;\n count++\n}\n \nconst stacked = [\n ...Object.values(test),\n ...Object.values(bg),\n ...Object.values(shared)\n]; \n return stacked\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":431,"value":"only_search_results = {\n var data = []\n for(var i = 0;i< search.length;i++){\n data.push(search[i][\"ID\"])\n }\n return data\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":38,"value":"### Imports and functions","pinned":false,"hidden":false,"mode":"md"},{"id":726,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"import {file} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"import {localFileInput} from \"@mbostock/localfile\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"params = new URLSearchParams(location.search)","pinned":false,"hidden":false,"mode":"ojs"},{"id":62,"value":"import { Legend, swatches } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":91,"value":"phylotree=require (\"https://dl.dropboxusercontent.com/s/i528j2lqnyzknit/phylotree.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":153,"value":"import {Plot} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":323,"value":"import { py, pyodide } from '@gnestor/pyodide'","pinned":false,"hidden":false,"mode":"ojs"},{"id":845,"value":"import {upsetApp} from \"@trebor/upset-demonstrator\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1261,"value":" function constant(x) {\n return function() { \n return x;\n };\n }","pinned":false,"hidden":false,"mode":"ojs"},{"id":340,"value":"chisqrprob = function chisqrprob ($n,$x) {\n if (($n <= 0) || ((Math.abs($n) - (Math.abs(Math.floor($n)))) != 0)) {\n throw(\"Invalid n: $n\\n\"); /* degree of freedom */\n }\n return _subchisqrprob($n-0, $x-0);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":346,"value":"function _subuprob ($x) {\n var $p = 0; /* if ($absx > 100) */\n var $absx = Math.abs($x);\n\n if ($absx < 1.9) {\n $p = Math.pow((1 +\n $absx * (.049867347\n + $absx * (.0211410061\n + $absx * (.0032776263\n + $absx * (.0000380036\n + $absx * (.0000488906\n + $absx * .000005383)))))), -16)/2;\n } else if ($absx <= 100) {\n for (var $i = 18; $i >= 1; $i--) {\n $p = $i / ($absx + $p);\n }\n $p = Math.exp(-.5 * $absx * $absx) \n / Math.sqrt(2 * Math.PI) / ($absx + $p);\n }\n\n if ($x<0)\n $p = 1 - $p;\n return $p;\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":342,"value":"function _subchisqrprob ($n,$x) {\n var $p;\n\n if ($x <= 0) {\n $p = 1;\n } else if ($n > 100) {\n $p = _subuprob((Math.pow(($x / $n), 1/3)\n - (1 - 2/9/$n)) / Math.sqrt(2/9/$n));\n } else if ($x > 400) {\n $p = 0;\n } else { \n var $a;\n var $i;\n var $i1;\n if (($n % 2) != 0) {\n $p = 2 * _subuprob(Math.sqrt($x));\n $a = Math.sqrt(2/Math.PI) * Math.exp(-$x/2) / Math.sqrt($x);\n $i1 = 1;\n } else {\n $p = $a = Math.exp(-$x/2);\n $i1 = 2;\n }\n\n for ($i = $i1; $i <= ($n-2); $i += 2) {\n $a *= $x / $i;\n $p += $a;\n }\n }\n return $p;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1063,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":1190,"value":"C1XX = _.filter(table_data, d=> d.FG_pval\t < 0.05)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1198,"value":"C0XX = _.filter(table_data, d=> d.FG_pval\t > 0.05)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1193,"value":"CX1X = _.filter(table_data, d=> d.BG_pval\t < 0.05)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1202,"value":"CX0X = _.filter(table_data, d=> d.BG_pval\t > 0.05)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1196,"value":"CXX1 = _.filter(table_data, d=> d.DF_pval\t < 0.05)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1204,"value":"CXX0 = _.filter(table_data, d=> d.DF_pval\t > 0.05)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1206,"value":"all_classes = {\nlet all_psg = [];\n\nfunction addToAllPsg(id, scenario) {\n let foundIndex = all_psg.findIndex(obj => obj.name === id);\n if (foundIndex !== -1) {\n all_psg[foundIndex].sets.push(scenario);\n } else {\n all_psg.push({ name: id, sets: [scenario] }); \n }\n}\n\nC1XX.forEach(id => addToAllPsg(id, \"C1**\"));\nC0XX.forEach(id => addToAllPsg(id, \"C0**\"));\nCX1X.forEach(id => addToAllPsg(id, \"C*1*\"));\nCX0X.forEach(id => addToAllPsg(id, \"C*0*\"));\nCXX1.forEach(id => addToAllPsg(id, \"C**1\"));\nCXX0.forEach(id => addToAllPsg(id, \"C**0\"));\n//_.each(all_psg, function(item) {item.Count = item.Scenario.length;});\nlet temp = all_psg[9];\nall_psg[9] = all_psg[1]\nall_psg[1] = temp\nreturn all_psg\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":850,"value":"viewof appState = upsetApp({ set: all_classes })","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}