30:T7f9,html`

${rer_results_json.input["number of sequences"]} sequences in the alignment	${rer_results_json.input["number of sites"]} sites in the alignment	${floatFmt(tree_length_by_model[rer_best_model])} Tree Length (subs/site)
${rer_best_model} Best model	${_.size (tested_branches)} Tested branches	${_.size (background_branches)} Background branches
${floatFmt(getTestResult("Proportional","Proportional Partitioned"))} p test set different	${floatFmt(getTestResult("Proportional Partitioned","Unconstrained Test"))} p test set heterogenous	${floatFmt(getTestResult("Unconstrained Test","Unconstrained"))} p full tree heterogenous

`31:T490,summary=md`

Analysis description

Model comparison was run on \`${rer_results_json["input"]["file name"]}\`. The best-fitting model (based on AIC-c) is **${rer_best_model}**. **\`${rer_results_json["analysis"]["model"]}\`** substitution model was used, and internal branches were labeled using the **\`${rer_results_json["analysis"]["labeling strategy"]}\`** strategy.
Based on the comparison of the _Proportional_ and the _Proportional Partitioned_ models, there ${report_result(getTestResult ("Proportional","Proportional Partitioned"))} for the differences in average relative rates between test and background branches.
Based on the comparison of the _Proportional Partitioned_ and the _Unconstrained Test_ models, there ${report_result(getTestResult ("Proportional Partitioned","Unconstrained Test"))} for the heterogenity of rates in the test set of branches.
Based on the comparison of the _Unconstrained Test_ and the _Unconstrained_ models, there ${report_result(getTestResult ("Unconstrained Test","Unconstrained"))} for the heterogenity of rates in all tree branches.

`32:T1373,{ const max_bl = Math.max (max_length_by_model[fig1x],max_length_by_model[fig1y]); const doFG = fig1Options.indexOf ("Labeled") >= 0; const doBG = fig1Options.indexOf ("Background") >= 0; const doSqrt = fig1Options.indexOf ("sqrt") >= 0; _.each (nrm_branch_lengths, (d)=> { if (d.name in branch_level_analysis) { if (branch_level_analysis[d.name] == '++') { d["node-stroke"] = "Informative"; return; } if (branch_level_analysis[d.name] == '+-') { d["node-stroke"] = "Outlier"; return; } } d["node-stroke"] = "Uninformative"; }); let vg_options = { "data": {"values": _.filter (nrm_branch_lengths, (d)=>doFG && tested_branches[d.name] ||doBG && background_branches[d.name]) }, "width" : 300, "height" : 300, "layer" : [{ "mark": {type: "circle", "opacity" : 0.5, tooltip : {"content": "data"}, filled : true, clip : true}, "encoding": { "x": {"field": fig1x, "type": "quantitative", "title" : fig1x, "axis" : {"grid" : false, titleFontSize: 18 }, "scale" : {"type" : doSqrt ? "sqrt" : "linear"}, }, "y": {"field": fig1y , "title" : fig1y, "axis" : {"grid" : false, titleFontSize: 18}, "type": "quantitative", "scale" : {"type" : doSqrt ? "sqrt" : "linear"} }, "size" : {"field": "node-stroke", "scale" : {"domain" : ["Uninformative","Informative","Outlier"], "range" : [32,128,256]}, "legend" : {"orient" : "bottom-right", "title" : "Outlier test", "labelFontSize" : 12, "titleFontSize" : 14}}, "fill" : {"field" : "group", "scale": { "domain" : ["Labeled", "Background"], "range": [crameri_bilbao[0], crameri_bilbao[crameri_bilbao.length-1]],}, "legend" : {"orient" : "top", "title" : "Branch type", "labelFontSize" : 12, "titleFontSize" : 14}} } }, {"mark": { "type": "rule", "color": "grey", "strokeWidth": "1", "strokeDash" : [8, 8] }, "encoding": { "x": { "datum": {"expr": "0"}, "type": "quantitative", "scale": {"domain": [0, max_bl]} }, "y": { "datum": {"expr": "0"}, "type": "quantitative", "scale": {"domain": [0, max_bl]} }, "x2": {"datum": {"expr": "" + max_bl}}, "y2": {"datum": {"expr": "" + max_bl}} } }, /*{ "transform": [ { "regression": fig1y, "on": fig1x, "method" : "linear", "params": true }, {"calculate": "'y=' + format(datum.coef[0], '.4f') + '+' + format(datum.coef[1], '.4f') + 'x; R²: '+format(datum.rSquared, '.2f')", "as": "R2"} ], "mark": { "type": "text", "color": "firebrick", "x": "width", "size" : 16, "align": "right", "y": -5 }, "encoding": { "text": {"type": "nominal", "field": "R2"} } }*/ ] }; let do_bg_regression = new Set (['Proportional', 'Proportional Partitioned', 'Unconstrained Test']); let do_fg_regression = new Set (['Proportional Partitioned']); if (do_bg_regression.has (fig1x) || do_bg_regression.has (fig1y)) { vg_options["layer"].push ({ "mark": { "type": "line", "color": crameri_bilbao[crameri_bilbao.length-1], "clip": true, "opacity" : 0.5 }, "data": {"values": _.filter (nrm_branch_lengths, (d)=> background_branches[d.name]) }, "transform": [ { "regression": fig1y, "on": fig1x, "method" : "linear" } //{"calculate" : "datum.GTR_dsn*1.284", "as" : "rgr"} ], "encoding": { "x": { "field": fig1x, "type": "quantitative" }, "y": { "field": fig1y, "type": "quantitative" } } }); } if (do_fg_regression.has (fig1x) || do_fg_regression.has (fig1y)) { vg_options["layer"].push ({ "mark": { "type": "line", "color": crameri_bilbao[0], "clip": true, "opacity" : 0.5 }, "data": {"values": _.filter (nrm_branch_lengths, (d)=> tested_branches[d.name]) }, "transform": [ { "regression": fig1y, "on": fig1x, "method" : "linear" } //{"calculate" : "datum.GTR_dsn*1.284", "as" : "rgr"} ], "encoding": { "x": { "field": fig1x, "type": "quantitative" }, "y": { "field": fig1y, "type": "quantitative" } } }); } return vegalite (vg_options); }33:T7ad,modelTableData = { let ref = tree_length_by_model_by_group["Reference"]; let ratio_domain = [1e100, 0]; let ref_lengths = _.fromPairs(_.map (["test", "background"], (t)=> { return [t, d3.sum (_.map (tree_length_by_model_by_group["Reference"][t]))]; })); let table_data = _.chain (tree_length_by_model).map((i,d)=> { let lengths = _.fromPairs(_.map (["test", "background"], (t)=> { return [t, d3.sum (_.map (tree_length_by_model_by_group[d][t]))]; })); if (d in rer_results_json["fits"]) { return { 'Model' : d, 'AIC-c' : rer_results_json["fits"][d]["AIC-c"], 'Tree length' : tree_length_by_model[d], 'L (test)' : lengths["test"], 'L (background)' : lengths["background"], 'Ratio (test)' : {'branches' : _.mapValues (tree_length_by_model_by_group[d]["test"], (d,k)=> d / ref["test"][k]), 'ratio' : lengths["test"] / ref_lengths["test"]}, 'Ratio (background)' : {'branches' : _.mapValues (tree_length_by_model_by_group[d]["background"], (d,k)=> d / ref["background"][k]), 'ratio' : lengths["background"] / ref_lengths["background"]}, }; } else { return { 'Model' : d, 'AIC-c' : null, 'Tree length' : tree_length_by_model[d], 'L (test)' : lengths["test"], 'L (background)' : lengths["background"], 'Ratio (test)' : null, 'Ratio (background)' : null }; } }).value(); _.each (table_data, (d)=> { _.each (["Ratio (test)","Ratio (background)"], (t)=> { if (d[t]) { ratio_domain [0] = Math.min (ratio_domain[0], d3.min (_.map (d[t]['branches']))); ratio_domain [1] = Math.max (ratio_domain[1], d3.max (_.map (d[t]['branches']))); } }); }); return {'table' : table_data ,'ranges' : ratio_domain}; }34:Td1d,display_tree = (i) => { let dim = treeDim.length ? _.map (treeDim.split ("x"), (d)=>+d) : null; let T = tree_objects[i]; var t = T.render({ height:dim && dim[0] || 1024, width:dim && dim[1] || 600, 'align-tips' : treeLabels.indexOf ("align tips") >= 0, 'show-scale' : true, 'is-radial' : false, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', 'node_circle_size' : (n)=> 0, 'draw-size-bubbles' : false, 'internal-names' : treeLabels.indexOf ("show internal") >= 0 } ); let showSeqNames = treeLabels.indexOf ("sequence names") >= 0; let showOutlierClass = treeLabels.indexOf ("outlier class") >= 0; let showTested = treeLabels.indexOf ("tested branches") >= 0; let logRatio = treeLabels.indexOf ("log scale") >= 0; let range = null; t.rd = _.find (modelTableData.table, (d)=>d.Model == modelForTree); try { t.rd = _.extend (t.rd["Ratio (test)"].branches, t.rd["Ratio (background)"].branches); range = d3.extent (_.map (t.rd)); } catch { t.rd = {} } t.nodeLabel ((n)=> { let label = ""; if (showSeqNames) label += n.data.name; if (showOutlierClass) label += branch_level_analysis[n.data.name] || ""; return label; }); function sort_nodes (asc) { T.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; }); T.resortChildren (function (a,b) { return (a["count_depth"] - b["count_depth"]) * (asc ? 1 : -1); }); } sort_nodes (true); t.style_nodes ((e,n) => { if (n.children && n.children.length) return; e.style ("font-family", "courier"); e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); }); if (range) { if (range[1] - range[0] < 1e-5) { range[0] = (range[0] + range[1])*0.5 - 1e-4; range[1] = (range[0]) + 1e-4; } if (logRatio) { if (range[0] < 1e-15) { range[0] = 1e-8; } if (range[1] > 1 && range[0] < 1) { range[2] = range[1]; range[1] = 1; } } } t.color_scale = range && logRatio ? d3.scaleSymlog(range, ["blue", "grey", "red"]).nice() : d3.scaleSequential (range ? range : modelTableData.ranges,d3.interpolateRgbBasis(crameri_bilbao)).nice(); //treeMatrixColorScale.domain ([0,js_max]); t.style_edges ((e,n) => { e.style ("stroke", null); if (true) { if (showTested && tested_branches[n.target.data.name]) { e.style ("stroke-width", 3); } else { e.style ("stroke-width", null); } e.style ("stroke", t.color_scale (t.rd[n.target.data.name])); } }); t.placenodes(); t.update(); return t; }35:Tc29,html` `36:[] 2e:{"title":"MoleRate (molecular relative evolutionary rates) analysis result visualization","cells":[{"id":0,"value":"header1 = md`# MoleRate (molecular relative evolutionary rates) analysis result visualization`","pinned":false,"hidden":false,"mode":"ojs"},{"id":660,"value":"load_prompt = md`Load a MoleRate JSON result file `","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"viewof results_file = params.get (\"url\") ? localFileInput({accept: [\".json\",\".gz\"], value : params.get (\"url\"), disable: true}) : localFileInput({accept: [\".gz\",\".json\"]}) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":592,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":1141,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":538,"value":"table1caption=md`**Table 1**. Summary of model fit, tree lengths (subs/site), and branch lengths relative to the reference.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1177,"value":"viewof table1 = Inputs.table (modelTableData.table, {\n sort: \"AIC-c\",\n rows : 6,\n width: {\n 'Model' : 145,\n 'Ratio (test)': 200,\n 'Ratio (background)': 200,\n },\n format : {\n 'Ratio (test)' : (d)=>html`${floatFmt(d.ratio)}${plotRatios(_.map (d.branches), modelTableData.ranges)}`,\n 'Ratio (background)' : (d)=>html`${floatFmt(d.ratio)}${plotRatios(_.map (d.branches), modelTableData.ranges)}`\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1856,"value":"table2caption=md`**Table 2**. Summary of likelihood ratio tests.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1870,"value":"viewof table2 = Inputs.table (_.map (rer_results_json[\"test results\"], (d,k)=>{\n return {\n 'Null' : k.split (':')[0],\n 'Alternative' : k.split (':')[1],\n 'p' : d['Corrected P-value'],\n 'p (uncorrected)' : d['Uncorrected P-value']\n }\n}), {\n sort: \"p\",\n format: {\n 'p' : (d)=>floatFmt(d),\n 'p (uncorrected)' : (d)=>floatFmt(d)\n },\n rows : 6\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1274,"value":"viewof fig1x = Inputs.select(_.map (rer_model_summary, (d)=>d[0]), {value: \"Proportional\", label: \"x\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1278,"value":"viewof fig1y = Inputs.select(_.filter (_.map (rer_model_summary, (d)=>d[0]), (d)=>d!=fig1x), {value: \"Proportional Partitioned\", label: \"y\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1682,"value":"viewof fig1Options = Inputs.checkbox(\n [\"Labeled\",\"Background\",\"sqrt\"],{\"value\" : [\"Labeled\",\"Background\"], label: html`Plot options` })","pinned":false,"hidden":false,"mode":"ojs"},{"id":577,"value":"fig1_label=md`**Figure 1**. Compare branch length estimates between the two selected models.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1288,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1531,"value":"fig2label = md`**Figure 2.** Phylogenetic tree renderings show branch lengths under the selected model.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1318,"value":"viewof treeDim = text({placeholder : \"1024 x 800\", description: \"Tree dimension (height x width in pixels), leave blank to auto-scale\", submit: \"Resize\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1346,"value":"viewof treeLabels = Inputs.checkbox(\n [\"show internal\",\"sequence names\",\"align tips\",\"tested branches\",\"log scale\",\"outlier class\"],{\"value\" : [\"sequence names\",\"show internal\"], label: html`Tree labels` })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1332,"value":"viewof modelForTree = Inputs.select(_.map (rer_model_summary, (d)=>d[0]), {value: \"Proportional Partitioned\", label: \"Substitution model\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1391,"value":"treeMatrixColorLegend = Legend(treeMatrixColorScale, {\n title: \"Ratio/reference\",\n tickFormat : \"g\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":486,"value":"figure2 = dT.show()","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"md`---`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2052,"value":"crameri_bilbao = crameri [0].schemes[3].scheme[12]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1098,"value":"rer_model_summary = _.chain (rer_results_json[\"fits\"]).map ((v,m)=>[m,v [\"AIC-c\"]]).sortBy((d)=>d[1]).value().concat ([[\"Reference\",null]])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1118,"value":"rer_best_model = rer_model_summary [0][0]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1292,"value":"nrm_branch_lengths = {\n let bl = [];\n _.each (rer_results_json[\"branch attributes\"][\"0\"], (d,b)=> {\n let r = {'name' : b};\n _.each (rer_model_summary, (m)=>r[m[0]] = d[m[0]]);\n r.group = rer_results_json.tested[b] == \"test\" ? \"Labeled\" : \"Background\" ;\n bl.push (r);\n \n });\n return bl;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1104,"value":"tree_length_by_model = _.chain (_.map (rer_model_summary,(d)=>d[0])).map ((d)=>{return [d,d3.sum (_.map (rer_results_json[\"branch attributes\"][\"0\"], (bv)=>bv[d]))]}).fromPairs().value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1716,"value":"tree_length_by_model_by_group = _.chain (_.map (rer_model_summary,(d)=>d[0])).map ((d)=>{return [d,_.map (rer_results_json[\"branch attributes\"][\"0\"], (bv,b)=>[b,bv[d],rer_results_json[\"tested\"][b]])]}).map (d=>[d[0], _.groupBy (d[1], (c)=>c[2])]).map (d=>[d[0],_.mapValues(d[1],(c)=>_.fromPairs (c))]).fromPairs().value() ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1654,"value":"max_length_by_model = _.chain (_.map (rer_model_summary,(d)=>d[0])).map ((d)=>{return [d,d3.max (_.map (rer_results_json[\"branch attributes\"][\"0\"], (bv)=>bv[d]))]}).fromPairs().value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":638,"value":"floatFormat = d3.format (\".2g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1131,"value":"getTestResult = (m1,m2)=> {\n if ((m1 + \":\" + m2) in rer_results_json[\"test results\"])\n return rer_results_json[\"test results\"][m1 + \":\" + m2][\"Corrected P-value\"];\n return \"Test not run\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1180,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1394,"value":"treeMatrixColorScale = dT.color_scale","pinned":false,"hidden":false,"mode":"ojs"},{"id":1582,"value":"tested_branches = _.fromPairs(_.filter (_.toPairs(rer_results_json[\"branch attributes\"][\"0\"]), (d)=>rer_results_json.tested[d[0]]== \"test\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1600,"value":"background_branches = _.fromPairs(_.filter (_.toPairs(rer_results_json[\"branch attributes\"][\"0\"]), (d)=>rer_results_json.tested[d[0]]!= \"test\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2181,"value":"branch_level_analysis = {\n const pv = 0.05;\n return _.mapValues (rer_results_json[\"branch level analysis\"],d=>{\n return (d[\"null\"][\"Corrected P-value\"] <= pv ? \"+\" : \"-\") + (d[\"alternative\"][\"Corrected P-value\"] <= pv ? \"+\" : \"-\");\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"rer_results_json = await get_json (params.get (\"url\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"d3 = require (\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1471,"value":"dT = display_tree(0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"floatFmt = d3.format (\".2g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":841,"value":"seqNames = (tree)=> {\n let seq_names = [];\n tree.traverse_and_compute (n=>{\n if (n.children && n.children.length) return;\n seq_names.push (n.data.name);\n });\n return seq_names;\n};","pinned":false,"hidden":false,"mode":"ojs"},{"id":1159,"value":"report_result = (p)=> {\n let sup = \"\";\n if (p <= 0.05) {\n sup = \"**is** evidence\";\n } else {\n sup = \"**is no** evidence\";\n }\n return sup + \" (p=\" + floatFmt(p) + \")\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"_=require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":692,"value":"tree_objects = _.map (rer_results_json.input.trees, (tree,i)=> {\n let T = new phylotree.phylotree (tree);\n T.branch_length_accessor = (n)=>rer_results_json[\"branch attributes\"][i][n.data.name][modelForTree];\n return T;\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":711,"value":"totalTreeLength = (tree)=> {\n let L = 0;\n tree.traverse_and_compute ( (n)=> {\n if (tree.branch_length (n)) {\n L += +tree.branch_length (n);\n }\n });\n return L;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":350,"value":"import {Legend, Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":78,"value":"phylotree=require (\"phylotree\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":432,"value":"vegalite = require (\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1805,"value":"plotRatios = (d, dmn)=> {\n return ticks(\n d,\n {\n \"width\" : 120,\n \"height\" : 15,\n \"color\" : \"firebrick\",\n \"domain\" : dmn,\n \"datum\" : null,\n \"scaling\" : \"zero\",\n \"margin\" : {\"horizontal\" : .04, \"vertical\" : .125}\n }\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":174,"value":"import {select, text, input, autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1342,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2010,"value":"get_json = async (json_source)=> {\n if (json_source) {\n if (json_source.substr (json_source.length-3) == \".gz\") {\n return JSON.parse (pako.inflate(await d3.buffer(json_source), { to: 'string' }));\n }\n return d3.json (json_source);\n try {\n return d3.json (json_source);\n } catch {}\n //results_file.json();\n }\n try {\n return JSON.parse (pako.inflate(await results_file.arrayBuffer(), { to: 'string' }));//results_file.json();\n } catch {}\n return results_file.json();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2013,"value":"pako = require('pako@2.1.0/dist/pako.min.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1363,"value":"params = new URLSearchParams(location.search)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1370,"value":"import {localFileInput} from \"@mbostock/localfile\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1795,"value":"import {histogram, ticks} from \"@hamzaamjad/tiny-charts\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2037,"value":"import {schemes_by_tag as crameri} from \"@nitaku/fabio-crameris-color-schemes\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":598,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}