2b:null 2c:["$","title",null,{"children":"BUSTED analysis result visualization | HyPhy | Observable"}] 31:T915,summary_table = html`

${results_json.input["number of sequences"]} sequences in the alignment	${results_json.input["number of sites"]} codon sites in the alignment	${results_json.input["partition count"]} partitions
${tested_branch_count} median branches/partition used for testing	${omega_rate_classes} classes non-synonymous rate variation	${srv_rate_classes ? "" + srv_rate_classes + " classes" : "None"}${srv_hmm ? " HMM" : ""} synonymous rate variation
${floatFmt(results_json["test results"]["p-value"])} p-value for episodic diversityfing selection	${results_json["Evidence Ratios"]["constrained"] ? _.filter (results_json["Evidence Ratios"]["constrained"][0], (d)=>d >= pv).length : 0} Sites with ER≥${pv} for positive selection	${!_.isUndefined(mh_rates['DH']) ? floatFmt (mh_rates['DH']) : "N/A"}:${!_.isUndefined(mh_rates['TH']) ? floatFmt (mh_rates['TH']) : "N/A"} Multiple hit rates (2H:3H)

`32:T91c,plot_legends = ({ "Evidence ratio for ω>1 (constrained)" : "Evidence ratios (site level likelihood ratios) for ω>1, comparing the unrestricted model with the model where max(ω) := 1, and all other parameters are kept at their maximum likelihood values. Solid line = user selected significance threshold. Values capped at " + dyn_range_cap + " for readability", "Evidence ratio for ω>1 (optimized)" : "Evidence ratios (site level likelihood ratios) for ω>1, comparing the unrestricted model with the optimized constrained model and all other parameters are kept at their maximum likelihood values. Solid line = user selected significance threshold. Values capped at " + dyn_range_cap + " for readability", "Synonymous rates" : "Posterior means for synonymous site-level substitution rates (α). " + (srv_hmm ? "The most likely synonymous rate value, based on the Viterbi HMM path inference, is shown in dark red." : "") + "Dots are colored by the evidence ratio (constrained) in favor of positive selection acting on the corresponding site", "Support for positive selection" : "Empirical Bayes Factors for ω>1 at a particular branch and site (only tested branches are included).", "Support for 2H" : "Evidence ratios for having a non-zero 2-nucleotide substitution rate (δ), comparing the unrestricted model with the model where this rate is 0 at a particular branch/site, and all other parameters are kept at their maximum likelihood values. Values less than one are **not** reported.", "Support for 3H" : "Evidence ratios for having a non-zero 3-nucleotide substitution rate (ψ), comparing the unrestricted model with the model where this rate is 0 at a particular branch/site, and all other parameters are kept at their maximum likelihood values. Values less than one are **not** reported.", "Support for 2H+3H" : "Evidence ratios for having non-zero 2 or 3-nucleotide substitution rate (δ or ψ), comparing the unrestricted model with the model where these rates are both 0 at a particular branch/site, and all other parameters are kept at their maximum likelihood values. Values less than one are **not** reported.", "Site-level LR support" : "Cumulative distribution of the likelihood ratio test for the BUSTED test broken down by the contributions of individual sites" })33:T610,plot_specs = ( { "Evidence ratio for ω>1 (constrained)" : { "width": 800, "height": 150, "vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> { return ERPlot (fig1data, d, 70, "ER (constrained)") })}, "Evidence ratio for ω>1 (optimized)" : { "width": 800, "height": 150, "vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> { return ERPlot (fig1data, d, 70, "ER (optimized null)") })}, "Synonymous rates" : { "width": 800, "height": 150, "vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> { return SRVPlot (fig1data, d, 70, "SRV posterior mean", srv_hmm ? "SRV viterbi" : null) })}, "Support for positive selection" : { "vconcat" : _.map (_.range (1, results_json.input["number of sites"], er_step_size()), (d)=> { return BSPosteriorPlot (bsPositiveSelection, d, er_step_size()) })}, "Support for 2H" : { "vconcat" : _.map (_.range (1, results_json.input["number of sites"], er_step_size()), (d)=> { return BSPosteriorPlot (twoHBranchSite, d, er_step_size()) })}, "Support for 3H" : { "vconcat" : _.map (_.range (1, results_json.input["number of sites"], er_step_size()), (d)=> { return BSPosteriorPlot (threeHBranchSite, d, er_step_size()) })}, "Support for 2H+3H" : { "vconcat" : _.map (_.range (1, results_json.input["number of sites"], er_step_size()), (d)=> { return BSPosteriorPlot (multiHBranchSite, d, er_step_size()) })}, "Site-level LR support" : cdsQuant (fig1data, "LR", "Site LR") } )34:T9b3,siteTableData = { let site_info = []; let index = 0; _.each (results_json["data partitions"], (pinfo, partition)=> { _.each (pinfo["coverage"][0], (ignore, i)=> { let site_record = { 'Partition' : siteIndexPartitionCodon[index][0], 'Codon' : siteIndexPartitionCodon[index][1], }; if (results_json["Evidence Ratios"]['optimized null']) { site_record['ER (constrained)'] = results_json["Evidence Ratios"]['constrained'][0][index]; site_record['ER (optimized null)'] = results_json["Evidence Ratios"]['optimized null'][0][index]; }; site_record['LogL'] = results_json["Site Log Likelihood"]['unconstrained'][0][index]; if (srv_rate_classes) { let site_srv = []; _.each (srv_distribution, (d,i)=> { site_srv.push ({'value' : d.value, 'weight' : results_json["Synonymous site-posteriors"][i][index]}); }); site_record['SRV posterior mean'] = distMean (site_srv); if (srv_hmm) { site_record['SRV viterbi'] = srv_distribution[results_json["Viterbi synonymous rate path"][0][index]].value; } } if (results_json["Evidence Ratios"]['optimized null']) { site_record['LR'] = 2*Math.log (results_json["Evidence Ratios"]['optimized null'][0][index]); } site_info.push (site_record); index++; }) }); return [site_info, { 'Partition' : html`Part.`, 'Codon' : html`Codon`, 'ER (constrained)' : html`ER (ω>1, constr.)`, 'ER (optimized null)' : html`ER (ω>1, optim.)`, 'SRV posterior mean' : html`E_post[α]`, 'SRV viterbi' : html`α`, 'logL' : html`log(L)`, 'LR' : html`LR` }]; }35:T537,distributionTable = { let table = []; _.each (["Unconstrained model", "Constrained model"], (m)=> { if (!_.get (results_json,["fits",m])) return; let record = {'Model' : m}; record['LogL'] = _.get (results_json,["fits",m,"Log Likelihood"]); record['AICc'] = _.get (results_json,["fits",m,"AIC-c"]); record['p'] = _.get (results_json,["fits",m,"estimated parameters"]); record['dist'] = ["Tested", getRateDistribution (["fits",m,"Rate Distributions","Test"]),"ω",m + " ω tested"]; record['plot'] = ["ω",record['dist'][1]]; table.push (record); if (has_background) { record = {'Model' : m}; record['dist'] = ["Background", getRateDistribution (["fits",m,"Rate Distributions","Background"]),"ω",m + " ω background" ]; record['plot'] = ["ω",record['dist'][1]]; table.push (record); } if (srv_rate_classes) { record = {'Model' : m}; record['dist'] = ["Synonymous rates", getRateDistribution (["fits",m,"Rate Distributions","Synonymous site-to-site rates"], ["rate", "proportion"]),"",m + " syn rates" ]; record['plot'] = ["",record['dist'][1]]; table.push (record); } }); return table; //getRateDistribution (["fits","Unconstrained model","Rate Distributions","Test"]) }36:T8ee,cdsQuant = (data, key1, title)=> { return { "config": { "mark": {"invalid": null} }, "vconcat" : [ { "width" : 400, "height" : 200, "data" : {"values" : data}, "transform": [{ "sort": [{"field": key1}], "window": [{"op": "sum", "field": key1, "as": "CC"}], "frame": [null, 0] }], "layer" : [{ "mark": {"type" : "area", "opacity" : 0.5, "color" : "grey", "tooltip" : true, "interpolate" : "step"}, "encoding": { "x": { "field": key1, "type": "quantitative", "axis" : {"title" : null}, }, "y": { "field": "CC", "type": "quantitative", "scale" : {"type" : "linear"}, "axis" : {"title" : "Cumulative LRT", titleFontSize : 14} } }}, { "mark": {"type" : "line", "opacity" : 1.0, "color" : "black", "interpolate" : "step"}, "encoding": { "x": { "field": key1, // "sort" : "descending", "type": "quantitative", }, "y": { "field": "CC", "type": "quantitative", "scale" : {"type" : "linear"}, //"title" : "Cumulative count" }, }} ] }, { "width" : 400, "height" : 100, "data" : {"values" : data}, "mark": {"type" : "bar", "color" : "#ccc", "stroke" : "black", "tooltip" : true}, "encoding": { "x": { "field": key1, "bin": {"maxbins":200}, "type": "quantitative", "axis" : {"title" : title ? title : key1, titleFontSize : 14}, }, "y": { "aggregate": "count", "type": "quantitative", "scale" : {"type" : "sqrt"}, "axis" : {"title" : "Count", titleFontSize : 14}, //"title" : "Cumulative count" } } }]} }37:T859,rate_density = (data)=> { let rate_options = [["alpha","α"],["beta","β"], ["omega", "dN/dS"]]; return { "data" : {"values" : _.map (data, (d)=> { let dd = _.clone (d); _.each (["alpha","beta","dN/dS MLE"], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "transform" : [{"calculate" : "min(" + dyn_range_cap + ",datum.alpha > 0 ? datum.beta/datum.alpha : datum.beta > 0 ? 10000 : 0)", "as" : "omega"}], "vconcat" : _.map (rate_options, (rt)=>({"layer" : [{ "width": 800, "height": 100, "transform":[{ "density": rt[0], "bandwidth": 0.2 }], "mark": {type: "area", "opacity" : 0.5, tooltip : true, line : true}, "encoding": { "x": { "field": "value", "grid" : null, "title": rt[1], "type": "quantitative", "scale" : {"domain" : [0, dyn_range_cap]}, "axis": {"grid": false} }, "y": { "field": "density", "type": "quantitative", "title" : "", "axis": {"grid": false} }, "color" : {"value" : "grey"} }}, { "mark": "rule", "encoding": { "x": {"aggregate": "mean", "field": rt[0]}, "color": {"value": "firebrick"}, "size": {"value": 5}, } }, { "transform": [ { "aggregate": [{"op": "mean", "field": rt[0], "as": "rate_mean_" + rt[0]}] }, {"calculate": "format(datum['rate_mean_"+rt[0]+"'], '.2f')", "as": "fm1"} ], "mark": { "type": "text", "color": "gray", "size" : 12, "align": "left", "y": -5, "x": 2 }, "encoding": { "x" : {"field" : "rate_mean_" + rt[0], "type": "quantitative"}, "text": {"type": "nominal", "field": "fm1"} } }]}) ) } }38:T44a,qq_plot = (data, title)=> { return { "data": {"values": data}, "title" : title, "layer" : [{ "mark": {"type" : "line", "color" : "firebrick", "clip" : true}, "width" : 100, "height" : 100, "encoding": { "x": { "field": "bs", "type" : "quantitative", "scale" : {"domain" : [0,0.25]}, "axis" : {"grid" : false, "title" : "Bootstrap p-value", "labelFontSize" : 12, "titleFontSize" : 14} }, "y": {"field": "c2", "type" : "quantitative","axis" : {"grid" : false, "title" : "Asymptotic p-value", "labelFontSize" : 12, "titleFontSize" : 14}, "scale" : {"domain" : [0,0.25]}} }},{ "mark": { "type": "rule", "color": "grey", "strokeWidth": 1, "opacity" : 0.5, "clip" : true }, "encoding": { "x": { "datum": {"expr": "0"}, "type": "quantitative", }, "y": { "datum": {"expr": "0"}, "type": "quantitative", }, "x2": {"datum": {"expr": "1"}}, "y2": {"datum": {"expr": "1"}} }}] }}39:T75a,pv_plot = (data)=> { let color_d = []; let color_r = []; _.each (table_colors, (v,c)=> {color_d.push (c); color_r.push (v);}); return { "width": 500, "height": 500, "data" : {"values" : data}, "transform" : [{"calculate" : "(datum['p-value'] -" + pv + ")* (datum['p-asmp'] -" + pv + ") >= 0 ? 'Yes' : 'No'", "as": "agree"}], "layer" : [ { "mark" : {"opacity": 0.1, "type": "rect", "color": "#DDD"}, "encoding": { "x": { "datum": {"expr": "0"}, "type": "quantitative", }, "y": { "datum": {"expr": "0"}, "type": "quantitative", }, "x2": {"datum": {"expr": pv}}, "y2": {"datum": {"expr": pv}} } }, { "mark" : {"type" : "point", "size" : 96, "tooltip" : {"content" : "data"}, "filled" : true, "opacity" : 0.4}, "encoding" : { "x": { "field": "p-value", "type" : "quantitative", "scale" : {"type" : "sqrt"}, "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Bootstrap p-value"} }, "y": { "field": "p-asmp", "type" : "quantitative", "scale" : {"type" : "sqrt"}, "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Asymptotic p-value"} }, "color" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"}, "shape": { "field" : "agree", "title" : "Both p-values agree", "scale" : {"domain" : ["Yes","No"], "range" : ["circle", "cross"]} } } } ] } }3a:T7aa,ERPlot = (data, from, step, key)=> { let scale = d3.extent (data, (d)=>d[key]); scale[1] = Math.min (dyn_range_cap,Math.max (scale[1], pv)); scale = d3.nice (scale[0], scale[1], 10); return { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i< from + step - 1), (d)=> { let dd = _.clone (d); _.each ([key], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark": {"stroke": "black", "type": "line", "size" : 2, "interpolate" : "step", "color" : "lightgrey", "opacity" : 0.5}, "encoding": { "y": { "field": key, "type" : "quantitative", "scale" : {"type" : "symlog", "domain" : scale}, "axis" : {"grid" : false} } } }, { "mark": { "stroke": "black", "type": "point", "size" : 100, "filled" : true, "color" : "lightgrey", "tooltip" : {"contents" : "data"}, "opacity" : 1.}, "encoding": { "y": { "field": key, "type" : "quantitative", }, "color" : {"condition": {"test": "datum['" + key + "'] > " + pv, "value": "firebrick"}, "value": "lightgrey" } } }, { "mark" : {"opacity": 0.5, "type": "line", "color": "steelblue"}, "encoding" : { "y": { "datum": {"expr": "" + pv}, "type": "quantitative", "scale" : {"domain" : scale} }, "size": {"value": 2}, } } ] }; }3b:T72c,SRVPlot = (data, from, step, key, key2)=> { let spec = { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i<= from + step), (d)=> { let dd = _.clone (d); _.each ([key], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark": {"type": "line", "size" : 2, "color" : "lightgrey", "opacity" : 0.5, "interpolate" : "step"}, "encoding": { "y": { "field": key, "type" : "quantitative", } } }, { "mark": {"stroke": null, "type": "point", "size" : 100, "filled" : true, "color" : "lightgrey", "tooltip" : {"contents" : "data"}, "opacity" : 1}, "encoding": { "y": { "field": key, "type" : "quantitative", "scale" : {"type" : "symlog"}, "axis" : {"grid" : false} }, "color" : results_json["Evidence Ratios"]["constrained"] ? {"field" : "ER (constrained)", "type" : "quantitative", "scale" : {"type" : "log", "scheme": "turbo"}, "legend" : {"orient" : "top"}} : null } } ] }; if (key2) { spec.layer.push ( { "mark": {"type": "line", "size" : 4, "color" : "lightgrey", "opacity" : 0.5, "interpolate" : "step", "color" : "firebrick"}, "encoding": { "y": { "field": key2, "type" : "quantitative", } } } ); } return spec; }3c:T5f5,BSPosteriorPlot = (data, from, step)=> { const branch_order = treeNodeOrdering (0); let N = results_json.input["number of sequences"]; let box_size = 10; let font_size = 8; if (N > 50) { if (N <= 100) {box_size = 8; font_size = 6;} else if (N <= 200) {box_size = 5; font_size = 5;} else {box_size = 2; font_size = 0;} } let spec = { "width": {"step": box_size}, "height" : {"step" : box_size}, "data" : {"values" : _.filter (data, (d,i)=> d.Codon >= from && d.Codon < from + step), }, "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Codon", "labelFontSize" : font_size} : null }, "y": { "field": "Branch", "scale" : {"domain" : branch_order}, "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Branch", "labelFontSize" : font_size} : null } }, "layer": [ { "mark": {"type": "rect", "size" : 2, "color" : "lightgrey", "opacity" : 1.0, "tooltip" : true}, "encoding": { "color": { "field": "ER", "type" : "quantitative", "legend" : {"orient" : "top"}, "sort": "descending", "scale" : {"type" : "log", "scheme" : "redblue", "domainMid" : 1} } } } ] }; return spec; }3d:T90c,dNdS_with_ci = (data, from, step)=> { let color_d = []; let color_r = []; _.each (table_colors, (v,c)=> {color_d.push (c); color_r.push (v);}); return { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i<= from + step), (d)=> { let dd = _.clone (d); _.each (["dN/dS LB","dN/dS UB", "dN/dS MLE"], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "transform" :[{"calculate": "1", "as": "neutral"}], "encoding": { "x": { "field": "codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark": {"opacity": 1., "type": "line", "color": "steelblue"}, "encoding": { "y": { "field": "dN/dS LB", "scale" : {"type" : "sqrt"}, "type" : "quantitative", "axis": {"titleColor": "black", "grid" : false, "titleFontSize" : 14, "title" : "dN/dS"} }, "y2": { "field": "dN/dS UB", "type" : "quantitative" } } }, { "mark": {"stroke": "black", "type": "point", "filled" : true, "size" : 64, "stroke" : null, "tooltip" : {"contents" : "data"}}, "encoding": { "y": { "field": "dN/dS MLE", "type" : "quantitative", }, "color" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, /*{ "mark": {"stroke": "lightgrey", "type": "line", "size" : 1, "opacity" : 1.}, "encoding": { "y": { "field": "dN/dS MLE", "type" : "quantitative", }, } }*/ { "mark" : {"opacity": 0.5, "type": "line", "color": "gray"}, "encoding" : { "y": { "field": "neutral", "type" : "quantitative", }, "x": { "field": "codon", "type" : "nominal"}, "size": {"value": 2}, } } ] }; }3e:Ta3f,alpha_beta_plot = (data, from, step)=> { let color_d = []; let color_r = []; _.each (table_colors, (v,c)=> {color_d.push (c); color_r.push (v);}); return { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i<= from + step), (d)=> { let dd = _.clone (d); _.each (["alpha","beta", "alpha=beta"], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); dd.alpha = -dd.alpha; return dd; })}, "transform" :[{"calculate": "0", "as": "neutral"}, {"calculate": '-datum["alpha=beta"]', "as": "amb2"}], "encoding": { "x": { "field": "codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark" : {"opacity": 0.5, "type": "line", "color": "gray"}, "encoding" : { "y": { "field": "neutral", "type" : "quantitative", }, "x": { "field": "codon", "type" : "nominal"}, "size": {"value": 2}, } }, { "mark": {"type": "bar", "filled" : true, "stroke" : null, "opacity" : 0.5, "tooltip" : {"contents" : "data"}}, "encoding": { "y": { "field": "alpha", "type" : "quantitative", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Rate estimate", "labelExpr": "datum.label > 0 ? 'β = ' + datum.label: (toNumber(datum.label) == '0' ? '0' : 'α = ' + replace (datum.label, /[^0-9\.]/,''))"} }, "fill" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, { "mark": { "type": "bar", "filled" : true, "opacity" : 0.5, "stroke" : null, "tooltip" : {"contents" : "data"}}, "encoding": { "y": { "field": "beta", "type" : "quantitative", }, "fill" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, { "mark": {"opacity": 1., "type": "line", "color": "#444"}, "encoding": { "y": { "field": "alpha=beta", "type" : "quantitative" }, "y2": { "field": "amb2", "type" : "quantitative" } } } ] }; }3f:T12e1,display_tree = (i) => { let dim = treeDim.length ? _.map (treeDim.split ("x"), (d)=>+d) : null; let T = tree_objects[i]; T.branch_length_accessor = (n)=>(n.data.name in results_json["branch attributes"][i] ? results_json["branch attributes"][i][n.data.name][branch_length] : 0) || 0; let alignTips = treeLabels.indexOf ("align tips") >= 0; var t = T.render({ height:dim && dim[0], width:dim && dim[1], 'align-tips' : alignTips, 'show-scale' : true, 'is-radial' : false, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', 'node_circle_size' : (n)=>0, 'internal-names' : treeLabels.indexOf ("show internal") >= 0 } ); 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.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); }); let map2MH = {'Support for 2H' : "Evidence ratio for 2H", 'Support for 3H' : "Evidence ratio for 3H", 'Support for 2H+3H' : "Evidence ratio for 2H+3H"}; if (color_branches == "Tested") { t.style_edges ((e,n) => { const is_tested = results_json["tested"][i][n.target.data.name] == "test"; if (is_tested) { e.style ("stroke", "firebrick"); } else { e.style ("stroke", null); } }); } else if (color_branches == "Support for Selection") { let branch_values = {}; let prior = test_omega[test_omega.length-1].weight; prior = prior / (1-prior); T.traverse_and_compute ( (n)=> { let posteriors = results_json["branch attributes"][i][n.data.name]; if (posteriors && posteriors["Posterior prob omega clas"]) { posteriors = posteriors["Posterior prob omega clas"][test_omega.length-1]; branch_values [n.data.name] = posteriors/(1-posteriors)/prior; if (branch_values [n.data.name] < 1) branch_values [n.data.name] = null; } }); let color_scale = d3.scaleSequentialLog(d3.extent (_.map (branch_values, (d)=>d)),[0.1,1]); t.style_edges ((e,n) => { const is_tested = branch_values[n.target.data.name]; if (is_tested) { e.style ("opacity", color_scale(is_tested)).style ("stroke-width", "5").style ("stroke","firebrick"); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } }); } else if (color_branches == "Substitutions") { let labels = subs_by_branch (i); let color_scale = d3.scaleSequential(d3.extent (_.map (labels, d=>d)), d3.interpolatePuOr); t.color_scale = color_scale; t.color_scale_title = "Min # of nucleotide substitutions"; t.style_edges ((e,n) => { const is_tested = labels[n.target.data.name]; if (is_tested) { e.style ("stroke", color_scale(is_tested)).style ("stroke-width", "4").style ("opacity",1.0); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } }); } else if (map2MH [color_branches]) { let branch_values = site_support_by_branch (i, map2MH [color_branches], pv); let color_scale = d3.scaleSequentialLog(d3.extent (_.map (branch_values, (d)=>d)),[0.2,1]); t.style_edges ((e,n) => { const is_tested = branch_values[n.target.data.name]; if (is_tested) { e.style ("opacity", color_scale(is_tested)).style ("stroke-width", "5").style ("stroke","firebrick"); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } }); } t.placenodes(); t.update(); t.nwk = T.getNewick((n)=>results_json["tested"][i][n.data.name] == "test" ? "{Foreground}" : ""); return t; } 40:T1d44,display_tree_site = (i,s) => { let dim = treeDim.length ? _.map (treeDim.split ("x"), (d)=>+d) : null; let T = tree_objects[i]; T.branch_length_accessor = (n)=>results_json["branch attributes"][i][n.data.name][branch_length] || 0; let node_labels = generateNodeLabels (T, results_json["substitutions"][i][(+s)-1]); let labelDomain = new Set(); let showAA = treeLabels.indexOf ("amino-acids") >= 0; let showCodons = treeLabels.indexOf ("codons") >= 0; let showSeqNames = treeLabels.indexOf ("sequence names") >= 0; let showOnlyMH = treeLabels.indexOf ("show only multiple hits") >= 0; let showOnlyNS = treeLabels.indexOf ("show only non-synonymous changes") >= 0; let alignTips = treeLabels.indexOf ("align tips") >= 0; var t = T.render({ height:dim && dim[0], width:dim && dim[1], 'show-scale' : true, 'is-radial' : false, 'align-tips' : alignTips, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', 'node_circle_size' : (n)=>0, 'internal-names' : treeLabels.indexOf ("show internal") >= 0 } ); t.nodeLabel ((n)=> { if (!n._display_me) { return ""; } let label = ""; if (showCodons) { label = node_labels[n.data.name][0]; if (showAA) label += "/"; } if (showAA) label += node_labels[n.data.name][1]; labelDomain.add (label); if (showSeqNames) label += ":" + 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); }); } T.traverse_and_compute (function (n) { n._display_me = ! (showOnlyMH || showOnlyNS); if (!n._display_me) { if (node_labels[n.data.name]) { if (showOnlyMH && node_labels[n.data.name][3] > 1) n._display_me = true; if (! n._display_me) { if (showOnlyNS) { if (n.parent) { const my_aa = node_labels[n.data.name][1]; const parent_aa = node_labels[n.parent.data.name][1]; if (my_aa != parent_aa && my_aa != '-' && parent_aa != '-') { n._display_me = true; if (showOnlyMH) n._display_me = node_labels[n.data.name][3] > 1; } else { n._display_me = false; } } } } } } if (n._display_me && n.parent) { n.parent._display_me = true; } },"pre-order"); sort_nodes (true); t.style_nodes ((e,n) => { e.selectAll ("text").style ("fill", label_color_scale(t.nodeLabel ()(n).split (":")[0])); e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); }); let map2MH = {'Support for 2H' : "Evidence ratio for 2H", 'Support for 3H' : "Evidence ratio for 3H", 'Support for 2H+3H' : "Evidence ratio for 2H+3H"}; if (color_branches == "Tested") { t.style_edges ((e,n) => { const is_tested = results_json["tested"][i][n.target.data.name] == "test"; if (is_tested) { e.style ("stroke", "firebrick"); } else { e.style ("stroke", null); } }); } else if (color_branches == "Support for selection") { let branch_values = {}; let prior = test_omega[test_omega.length-1].weight; prior = prior / (1-prior); T.traverse_and_compute ( (n)=> { let posteriors = results_json["branch attributes"][i][n.data.name]; if (posteriors && posteriors["Posterior prob omega class by site"]) { posteriors = posteriors["Posterior prob omega class by site"][test_omega.length-1][s-1]; branch_values [n.data.name] = posteriors/(1-posteriors)/prior; if (branch_values [n.data.name] < 1) branch_values [n.data.name] = null; } }); let color_scale = d3.scaleSequentialLog(d3.extent (_.map (branch_values, (d)=>d)),d3.interpolateTurbo); t.color_scale = color_scale; t.color_scale_title = "Empirical Bayes Factor"; t.style_edges ((e,n) => { const is_tested = branch_values[n.target.data.name]; if (is_tested) { e.style ("stroke", color_scale(is_tested)).style ("stroke-width", "5").style ("opacity",null); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } }); } else if (color_branches == "Substitutions") { let color_scale = d3.scaleOrdinal([0,1,2,3], d3.schemePuOr[4]); t.color_scale = color_scale; t.color_scale_title = "Min # of nucleotide substitutions"; t.style_edges ((e,n) => { const is_tested = node_labels[n.target.data.name]; if (is_tested && is_tested[3]) { e.style ("stroke", color_scale(is_tested[3])).style ("stroke-width", "5").style ("opacity","1"); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d[2] + "→" + d[0] + "(" + d[3] + ")"); } else { e.style ("stroke", null); } }); } else if (map2MH [color_branches]) { let branch_values = {}; T.traverse_and_compute ( (n)=> { let posteriors = results_json["branch attributes"][i][n.data.name]; if (posteriors && posteriors[map2MH [color_branches]]) { let supp = _.find (posteriors[map2MH [color_branches]], (d)=>d[0] == s-1); if (supp) { branch_values[n.data.name] = supp[1]; } } }); let color_scale = d3.scaleSequentialLog(d3.extent (_.map (branch_values, (d)=>d)),d3.interpolateTurbo); t.color_scale = color_scale; t.color_scale_title = "Empirical Bayes Factor"; t.style_edges ((e,n) => { const is_tested = branch_values[n.target.data.name]; if (is_tested) { e.style ("stroke", color_scale(is_tested)).style ("stroke-width", "5").style ("opacity","1"); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } }); } t.placenodes(); t.update(); label_color_scale.domain (labelDomain); return t; }41:Tc37,bustedStyle = html` `42:[] 2f:{"id":"93cd4257e67ba346","slug":"busted","title":"BUSTED analysis result visualization","thumbnail":"b261be360591d393d1ca53908e7b1fc643201f889ab654ddf1b485d021b25b32","access_level":"public","create_time":"$D2022-05-27T18:02:22.896Z","update_time":"$D2022-09-21T23:28:01.728Z","view_time":null,"first_public_time":"$D2022-05-27T18:03:06.476Z","fork":{"id":"a6645431739ba032","slug":"busted","title":"BUSTED analysis result visualization","owner":{"id":"9bf992815a7b66f2","login":"spond","name":"Sergei Pond"}},"stars":0,"starred":false,"owner":{"type":"team","id":"bb6e02155a1088c2","login":"hyphy_software","name":"HyPhy","avatar_url":"https://avatars-next.observableusercontent.com/adbab5e53037f4041ab0b0dfcb13b54a2eefb34da95a35a45d0a5eab042b6def","tier":"starter_2024"},"version":3486,"publish_version":3486,"role":null,"authored":false,"authors":[{"type":"individual","id":"6f023d87bfec4d48","login":"hyphy_software","name":"HyPhy","avatar_url":"https://avatars-next.observableusercontent.com/adbab5e53037f4041ab0b0dfcb13b54a2eefb34da95a35a45d0a5eab042b6def","tier":null}],"members":[{"type":"individual","id":"6f023d87bfec4d48","login":"hyphy_software","name":"HyPhy","avatar_url":"https://avatars-next.observableusercontent.com/adbab5e53037f4041ab0b0dfcb13b54a2eefb34da95a35a45d0a5eab042b6def","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"BUSTED analysis result visualization","cells":[{"id":0,"value":"md`# BUSTED analysis result visualization`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":89,"value":"viewof results_file = params.get (\"url\") ? localFileInput({accept: \".json\", value : params.get (\"url\"), disable: true}) : localFileInput({accept: \".json\"}) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1302,"value":"intro = md`Based on the likelihood ratio test, there **is ${results_json[\"test results\"][\"p-value\"]>0.05 ? \"no\" : \"\"}** evidence of _episodic diversifying selection_ in this dataset (p=${floatFormat(results_json[\"test results\"][\"p-value\"])}).
BUSTED analysis (v${results_json.analysis.version}) was performed on the alignment from ${results_json.input[\"file name\"]} using HyPhy v${results_json.runtime}. This analysis ** ${srv_rate_classes > 0 ? \"included\" : \"did not include\"}** site-to-site synonymous rate variation${srv_hmm?\" with linear autocorrelation (HMM)\" : \"\"}. ${_.isUndefined (mh_rates['DH']) ? \"\" : (_.isUndefined (mh_rates['TH']) ? \"Double nucleotide substitutions were included in the model.\" : \"Double and triple nucleotide substitutions were included in the model.\")}${+results_json.analysis.version < 4.0 ? \"Some of the visualizations are not available for BUSTED analyses before v4.0\" : \"\"}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1673,"value":"citation = md`**Suggested citation**: ${results_json.analysis[\"citation\"]}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1119,"value":"viewof pv = Inputs.text({label: html`Evidence ratio threshold`, value: \"10\", submit: \"Update\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":592,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2362,"value":"alignmentHeader = md`#### Alignment-wide results`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2417,"value":"viewof rate_table = Inputs.table (distributionTable, {\n header : {'LogL' : \"Log (L)\", \"AICc\" : \"AIC-c\", \"p\" : html`Params.{\n return html`${d[0] + \" \" + d[2]}
\n ${_.map (d[1], (c,i)=> floatFormat(c.value) + \" (\" + proportionFormat (c.weight) + \") \")}\n Mean = ${floatFormat (distMean (d[1]))}, CoV = ${floatFormat (Math.sqrt (distVar (d[1]))/distMean (d[1]))}`},\n 'plot' : (d)=>renderDiscreteDistribution (d[1],{\"height\" : 50, \"width\" : 150, \"scale\" : \"sqrt\", \"ref\" : d[0].length == 0 ? [null] : [1]})\n },\n layout: \"auto\",\n height: 150 + (srv_rate_classes ? 120 : 0) + (has_background ? 120 :0)\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2647,"value":"distComparisonPlot = rate_table.length == 2 ? renderTwoDiscreteDistributions (rate_table[0].dist[1],rate_table[1].dist[1],{\"label\" : {\"chart\" : rate_table[0].plot[0], \"series\" : [rate_table[0].dist[3],rate_table[1].dist[3]]}, \"width\" : 700, \"height\" : 120, \"scale\" : \"sqrt\", \"margin\" : {top: 5, right: 250, bottom: 30, left: 20}}) : md`Select exactly two distributions to plot a side-by-side comparison`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1646,"value":"viewof plot_type = Inputs.select(_.map (_.filter (plot_options, (d)=>d[1](results_json)), d=>d[0]),{label: html`Plot type`})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":538,"value":"fig1caption=md`**Figure 1**. ${plot_type ? plot_legends[plot_type] : \"No plotting options available\"}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1328,"value":"figure1 = plot_type ? vegalite (plot_specs[plot_type]) : md``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1111,"value":"table1caption=md`**Table 1**. Detailed site-by-site results from the BUSTED analysis`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1114,"value":"viewof table1 = Inputs.table (sites_table[1], {\n rows : 10,\n format: sites_table[0],\n layout: \"auto\",\n header: sites_table[2]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1549,"value":"viewof tree_id = autoSelect ({options:treeViewOptions, size : 10, title: html`Tree to view`, placeholder : \"Select partition / codon tree to view\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2881,"value":"viewof branch_length = Inputs.select(_.chain (results_json[\"branch attributes\"][\"attributes\"]).toPairs().filter (d=>d[1][\"attribute type\"] == \"branch length\").map (d=>d[0]).value(),{value: \"unconstrained\", label: html`Branch length `})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3086,"value":"viewof treeLabels = Inputs.checkbox(\n [\"amino-acids\",\"codons\",\"show internal\",\"show only multiple hits\",\"show only non-synonymous changes\",\"sequence names\",\"align tips\"],{\"value\" : [\"amino-acids\"], label: html`Tree labels` }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3095,"value":"viewof color_branches = Inputs.select(tree_color_options,{value: \"Support for selection\", label: html`Color branches `})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1793,"value":"viewof treeDim = text({placeholder : \"1024 x 800\", description: \"Tree dimension (height x width in pixels), leave blank to auto-scale\", submit: \"Resize\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3477,"value":"fig2legend = {\n tree_id;\n if (figure2 && figure2.color_scale) {\n return Legend(figure2.color_scale, {\n title: figure2.color_scale_title,\n tickFormat: \"f\"\n });\n }\n return html``;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3476,"value":"fig2render = {\n if (figure2) {\n return figure2.show();\n }\n return md`### Select a tree option from the menu above to view`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2225,"value":"#### Code, data, and libraries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":15,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1565,"value":"figure2 = {\n let toDisplay = tree_id.split (\" \");\n if (toDisplay.length > 1) {\n if (toDisplay[0] == \"Codon\") { \n const codon_index = (+toDisplay[1]);\n let partition_id = siteIndexPartitionCodon [codon_index][0]-1;\n let TT = display_tree_site (partition_id, codon_index);\n return TT;\n } \n let TT = display_tree((-1) + (+toDisplay[1]));\n return TT;\n }\n return null;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":638,"value":"floatFormat = d3.format (\".4g\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2518,"value":"proportionFormat = d3.format (\".5p\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2257,"value":"results_json = await get_json (params.get (\"url\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":99,"value":"get_json = async (json_source)=> {\n if (json_source) {\n return d3.json (json_source);\n }\n return results_file.json();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1638,"value":"fig1data = {\n let in_set = new Set (_.map (table1, (d)=>d.Codon));\n return _.filter (siteTableData[0], (x)=>in_set.has (x.Codon));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1653,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1758,"value":"plot_options = [[\"Evidence ratio for ω>1 (constrained)\", (d)=>results_json[\"Evidence Ratios\"][\"constrained\"]], [\"Evidence ratio for ω>1 (optimized)\", (d)=>results_json[\"Evidence Ratios\"][\"optimized null\"]], [\"Synonymous rates\", (d)=>srv_rate_classes > 0], [\"Support for positive selection\", (d)=>bsPositiveSelection.length > 0], [\"Site-level LR support\", (d)=>results_json[\"Evidence Ratios\"][\"optimized null\"]], [\"Support for 2H\", (d)=>mh_rates['DH']], [\"Support for 3H\", (d)=>mh_rates['TH']], [\"Support for 2H+3H\", (d)=>mh_rates['DH'] && mh_rates['TH']]]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1665,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1449,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1214,"value":"tested_branch_count = d3.median (_.chain (results_json.tested).map ().map((d)=>_.filter (_.map (d), (d)=>d==\"test\").length).value())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1314,"value":"srv_rate_classes = _.size (results_json.fits[\"Unconstrained model\"][\"Rate Distributions\"][\"Synonymous site-to-site rates\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2798,"value":"srv_distribution = getRateDistribution ([\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Synonymous site-to-site rates\"], [\"rate\",\"proportion\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2325,"value":"omega_rate_classes = _.size (test_omega)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2422,"value":"has_background = _.get (results_json, [\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Background\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2331,"value":"srv_hmm = \"Viterbi synonymous rate path\" in results_json","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2584,"value":"mh_rates = ({\n 'DH' : _.get (results_json, ['fits', 'Unconstrained model','Rate Distributions', 'rate at which 2 nucleotides are changed instantly within a single codon']),\n 'TH' : _.get (results_json, ['fits', 'Unconstrained model','Rate Distributions', 'rate at which 3 nucleotides are changed instantly within a single codon'])\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1260,"value":"table_colors = ({\n 'Diversifying' : '#e3243b',\n 'Neutral' : '#444',\n 'Purifying' : 'green',\n 'Invariable' : '#CCC'\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2390,"value":"countBranchesER = (er)=> {\n let prior = test_omega[omega_rate_classes-1].weight / (1-test_omega[omega_rate_classes-1].weight);\n let count = [];\n _.each (results_json[\"branch attributes\"], (part_info, part)=> {\n _.each (part_info, (data, branch)=> {\n if (data[\"Posterior prob omega class\"]) {\n let post = data[\"Posterior prob omega class\"][omega_rate_classes-1];\n post = post / (1-post);\n if (post / prior >= er) {\n count.push ([part, branch, post/prior]);\n }\n }\n });\n });\n return count;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2554,"value":"test_omega = getRateDistribution ([\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Test\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2419,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2369,"value":"getRateDistribution = (keys, tags)=> {\n tags = tags || [\"omega\", \"proportion\"];\n const rate_info = _.get (results_json, keys);\n if (rate_info) {\n return _.sortBy (_.map (rate_info, (d)=>({\n \"value\" : d[tags[0]],\n \"weight\" : d[tags[1]]\n })), (d)=>d.rate);\n }\n return null;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2676,"value":"siteIndexPartitionCodon = {\n return _.chain (results_json['data partitions']).map ((d,k)=>_.map (d['coverage'][0], (site)=>[+k+1,site+1])).flatten().value();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1097,"value":"sites_table = {\n return [{}, siteTableData[0], siteTableData[1]];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2911,"value":"posteriorsPerBranchSite = (rate_class, prior_odds)=> {\n let results = [];\n let offset = 0;\n _.each (results_json[\"branch attributes\"], (data, partition) => {\n let partition_size = 0;\n _.each (data, (per_branch, branch)=> {\n if (per_branch [\"Posterior prob omega class by site\"]) {\n _.each (per_branch [\"Posterior prob omega class by site\"][rate_class], (p,i)=> {\n results.push ({'Branch' : branch, 'Codon' : i + offset + 1, 'Posterior' : p, 'ER' : (p/(1-p))/prior_odds});\n }); \n partition_size = per_branch [\"Posterior prob omega class by site\"][rate_class].length;\n }\n });\n offset += partition_size;\n });\n return results;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3168,"value":"mutliHitER = (key)=> {\n let results = [];\n let offset = 0;\n _.each (results_json[\"branch attributes\"], (data, partition) => {\n let partition_size = 0;\n _.each (data, (per_branch, branch)=> {\n if (per_branch [key]) {\n _.each (per_branch [key], (p,i)=> {\n results.push ({'Branch' : branch, 'Codon' : p[0] + offset, 'ER' : p[1]});\n }); \n partition_size = results_json[\"data partitions\"][partition][\"coverage\"][0].length;\n }\n });\n offset += partition_size;\n });\n return results;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3466,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2931,"value":"bsPositiveSelection = posteriorsPerBranchSite (test_omega.length - 1, test_omega[test_omega.length - 1].weight / (1-test_omega[test_omega.length - 1].weight))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3166,"value":"twoHBranchSite = mutliHitER (\"Evidence ratio for 2H\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3176,"value":"threeHBranchSite = mutliHitER (\"Evidence ratio for 3H\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3180,"value":"multiHBranchSite = mutliHitER (\"Evidence ratio for 2H+3H\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2983,"value":"generateNodeLabels = (T, labels)=> {\n let L = {};\n T.traverse_and_compute (function (n) {\n if (n.data.name in labels) {\n L[n.data.name] = [labels[n.data.name], translate_ambiguous_codon (labels[n.data.name]),'',0];\n if (n.parent) {\n L[n.data.name][2] = L[n.parent.data.name][0]; \n _.each (L[n.data.name][0], (c,i)=> {\n const c2 = L[n.data.name][2][i];\n if (c2 != c && c != '-' && c2 != '-') {\n L[n.data.name][3] ++;\n }\n });\n }\n } else {\n if (n.parent) {\n L[n.data.name] = _.clone (L[n.parent.data.name]);\n L[n.data.name][2] = L[n.data.name][0];\n L[n.data.name][3] = 0;\n } else {\n L['root'] = [labels[\"root\"], translate_ambiguous_codon (labels[\"root\"]), \"\", 0];\n }\n }\n },\"pre-order\");\n return L;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3112,"value":"treeNodeOrdering = (index)=> {\n let order = [];\n const T = tree_objects[index];\n function sort_nodes (asc) {\n T.traverse_and_compute (function (n) {\n var d = 1;\n if (n.children && n.children.length) {\n d += d3.max (n.children, function (d) { return d[\"count_depth\"];});\n } \n\n n[\"count_depth\"] = d;\n });\n T.resortChildren (function (a,b) {\n return (a[\"count_depth\"] - b[\"count_depth\"]) * (asc ? 1 : -1);\n });\n }\n sort_nodes (true);\n T.traverse_and_compute (function (n) {\n if (results_json.tested[index][n.data.name] == \"test\") {\n order.push (n.data.name);\n }\n });\n return order;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2631,"value":"distMean = (d)=> {\n let m = 0;\n _.each (d, (r)=> {\n m += r['value'] * r['weight'];\n });\n return m;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2789,"value":"distVar = (d)=> {\n let m2 = 0, m = distMean (d);\n _.each (d, (r)=> {\n m2 += r['value']*r['value'] * r['weight'];\n });\n return m2 - m*m;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3465,"value":"er_step_size = ()=> {\n let N = results_json.input[\"number of sequences\"];\n if (N < 100) return 70;\n if (N < 200) return 140;\n return 600;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":9,"value":"d3 = require (\"d3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":416,"value":"floatFmt = d3.format (\".2g\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":841,"value":"seqNames = (tree)=> {\n let seq_names = [];\n tree.traverse_and_compute (n=>{\n if (n.children && n.children.length) return;\n seq_names.push (n.data.name);\n });\n return seq_names;\n};","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1748,"value":"dyn_range_cap = 10000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1869,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2012,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1800,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2738,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2821,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2948,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1363,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1702,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2181,"value":"denser_plot = (data)=> {\n let columns = [[\"alpha\",\"α\"],[\"beta\",\"β\"],[\"p-value\", \"p-value\"]];\n return {\n \"data\" : {\"values\" : _.map (data,\n (d)=> {\n let dd = _.clone (d);\n _.each (columns, (f)=> {\n dd[f[0]] = Math.min (dyn_range_cap, dd[f[0]]);\n });\n return dd;\n })}, \n \n \"vconcat\" : _.map (columns, (cc,i)=> ({\n \"width\" : 800,\n \"height\" : 50,\n \"mark\": {\"type\": \"area\", \"color\" : \"lightblue\", \"stroke\" : \"black\", \"interpolate\" : \"step\"},\n \"encoding\": {\n \"x\": {\n \"field\": \"codon\",\n \"type\" : \"quantitative\",\n \"axis\": {\"grid\" : false, \"titleFontSize\" : 14, \"title\" : i == columns.length -1 ? \"Codon\" : null}\n },\n \"y\": {\n \"field\": cc[0],\n \"type\" : \"quantitative\",\n \"axis\": {\"grid\" : false, \"titleFontSize\" : 14, \"title\" : cc[1]}\n }\n }}))\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":13,"value":"_=require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":692,"value":"tree_objects = _.map (results_json.input.trees, (tree,i)=> {\n let T = new phylotree.phylotree (tree);\n T.branch_length_accessor = (n)=>results_json[\"branch attributes\"][i][n.data.name][\"Global MG94xREV\"];\n return T;\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":711,"value":"totalTreeLength = (tree)=> {\n let L = 0;\n tree.traverse_and_compute ( (n)=> {\n if (tree.branch_length (n)) {\n L += +tree.branch_length (n);\n }\n });\n return L;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3395,"value":"subs_by_branch = (i)=>{\n let counts = {};\n _.each (results_json.substitutions[i], (states, site)=> {\n _.each (states, (state, branch)=> {\n if (branch != \"root\") {\n if (state != '---') {\n counts[branch] = 1 + (counts[branch] ? counts[branch] : 0);\n }\n }\n });\n });\n return counts;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3409,"value":"site_support_by_branch = (i, key, er)=> {\n let counts = {};\n _.each (results_json[\"branch attributes\"][i], (attribs, branch)=> {\n if (key in attribs) {\n _.each (attribs[key], (d)=> {\n if (d[1] >= er) {\n counts[branch] = 1 + (counts[branch] ? counts[branch] : 0);\n }\n });\n }});\n return counts;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3132,"value":"label_color_scale = d3.scaleOrdinal([], d3.schemeCategory10)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":756,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3006,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3441,"value":"treeViewOptions = {\n let opts = _.map (_.range (1,tree_objects.length+1), (d)=>\"Partition \" + d);\n if (results_json.substitutions) {\n opts = opts.concat(_.map (_.range (1,results_json.input[\"number of sites\"]), (d)=>\"Codon \" + d));\n }\n return opts;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3257,"value":"tree_color_options = {\n let options = [\"Tested\"];\n if (results_json.substitutions) {\n options.push (\"Support for selection\");\n options.push (\"Substitutions\");\n }\n if (mh_rates[\"DH\"]) options.push (\"Support for 2H\");\n if (mh_rates[\"TH\"]) options.push (\"Support for 3H\");\n if (mh_rates[\"DH\"] && mh_rates[\"TH\"]) options.push (\"Support for 2H+3H\");\n return options;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":78,"value":"phylotree=require (\"phylotree\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":432,"value":"vegalite = require (\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":174,"value":"import {select, text, input, autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2230,"value":"params = new URLSearchParams(location.search)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":350,"value":"import {legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2277,"value":"import {localFileInput} from \"@mbostock/localfile\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2531,"value":"import {renderDiscreteDistribution,renderNDiscreteDistributions, renderTwoDiscreteDistributions} from \"@spond/omega-plots\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2997,"value":"import {translate_ambiguous_codon} from \"@spond/usual-vs-unusual-mutation-analyses\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3107,"value":"import {Legend, Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":587,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":598,"value":"simpleIcons = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W42","stdlib":"1","titling":"auto"}} 30:"$2f:body"