30:T8eb,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	${_.filter (results_json["Evidence Ratios"]["constrained"][0], (d)=>Math.exp (d) >= pv).length} Sites with ER≥${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)

`31:T7f3,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.", "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.", "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." })32:T86e,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"], 70), (d)=> { return BSPosteriorPlot (bsPositiveSelection, d, 70) })}, "Support for 2H" : { "vconcat" : _.map (_.range (1, results_json.input["number of sites"], 70), (d)=> { return BSPosteriorPlot (twoHBranchSite, d, 70) })}, "Support for 3H" : { "vconcat" : _.map (_.range (1, results_json.input["number of sites"], 70), (d)=> { return BSPosteriorPlot (threeHBranchSite, d, 70) })}, "Support for 2H+3H" : { "vconcat" : _.map (_.range (1, results_json.input["number of sites"], 70), (d)=> { return BSPosteriorPlot (multiHBranchSite, d, 70) })}, "Compare ER" : ERPlotComp (fig1data, scatter_plot[0], scatter_plot[1], scatter_plot[2]), "Compare BS support" : BSPlotComp (compBSModels, scatter_plot[2]), "LR difference CDS" : cdsQuant (fig1data, "Diff LR"), "LogL difference" : { "width": 800, "height": 150, "vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> { return ERPlot (fig1data, d, 70, "Diff logl") })}, "LogL difference scatter" : ScatterPlotComp (fig1data, "LogL ref", "LogL comp", scatter_plot[2]), "LR difference scatter" : ScatterPlotComp (fig1data, "LR ref", "LR comp", scatter_plot[2]), "LR difference" : { "width": 800, "height": 150, "vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> { return ERPlot (fig1data, d, 70, "Diff LR") })}, } )33:T112a,siteTableData = { let site_info = []; _.each (results_json["Evidence Ratios"]['constrained'][0], (er, index)=> { let site_record = { 'Partition' : siteIndexPartitionCodon[index][0], 'Codon' : siteIndexPartitionCodon[index][1], 'ER (constrained)' : er, 'ER (optimized null)' : results_json["Evidence Ratios"]['optimized null'][0][index] }; site_record ['ER (constrained comp)'] = results_json_compare["Evidence Ratios"]['constrained'][0][index]; site_record ['ER (opt comp)'] = results_json_compare["Evidence Ratios"]['optimized null'][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 (get_srv_rate_classes (results_json_compare)) { let site_srv = []; _.each (get_srv_distribution (results_json_compare), (d,i)=> { site_srv.push ({'value' : d.value, 'weight' : results_json["Synonymous site-posteriors"][i][index]}); }); site_record['SRV posterior mean comp'] = distMean (site_srv); } site_record["LogL ref"] = results_json["Site Log Likelihood"]["unconstrained"][0][index]; site_record["LogL comp"] = results_json_compare ["Site Log Likelihood"]["unconstrained"][0][index]; site_record["Diff logl"] = results_json["Site Log Likelihood"]["unconstrained"][0][index] - results_json_compare ["Site Log Likelihood"]["unconstrained"][0][index]; site_record["LR ref"] = results_json["Site Log Likelihood"]["unconstrained"][0][index] - results_json["Site Log Likelihood"]["optimized null"][0][index]; site_record["LR comp"] = results_json_compare ["Site Log Likelihood"]["unconstrained"][0][index] - results_json_compare ["Site Log Likelihood"]["optimized null"][0][index]; site_record["Diff LR"] = results_json["Site Log Likelihood"]["unconstrained"][0][index] - results_json["Site Log Likelihood"]["optimized null"][0][index] - results_json_compare ["Site Log Likelihood"]["unconstrained"][0][index] + results_json_compare ["Site Log Likelihood"]["optimized null"][0][index]; site_info.push (site_record); }) return [site_info, { 'Partition' : html`Part.`, 'Codon' : html`Codon`, 'ER (constrained)' : html`ER (ω>1, constr.)`, 'ER (constrained comp)' : html`ER (ω>1, constr. comp.)`, 'ER (optimized null)' : html`ER (ω>1, optim.)`, 'ER (opt comp)' : html`ER (ω>1, optim. comp.)`, 'SRV posterior mean' : html`E_post[α]`, 'SRV posterior mean comp' : html`E_post[α] comp.`, 'SRV viterbi' : html`α`, 'Diff logl' : html`Δ log(L)`, 'Diff LR' : html`ΔΔ log(L)`, 'LogL ref' : html`log(L)_Ref`, 'LogL comp' : html`log(L)_Comp`, 'LR ref' : html`LR_Ref`, 'LR comp' : html`LR_Comp`, }]; }34:T62f,distributionTable = { let table = []; _.each ([["Primary", results_json],["Comparison", results_json_compare]], (data)=> { if (data[1]) { _.each (["Unconstrained model", "Constrained model"], (m)=> { let record = {'Model' : data[0] + " " + m}; record['LogL'] = _.get (data[1],["fits",m,"Log Likelihood"]); record['AICc'] = _.get (data[1],["fits",m,"AIC-c"]); record['p'] = _.get (data[1],["fits",m,"estimated parameters"]); record['dist'] = ["Tested", getRateDistribution (["fits",m,"Rate Distributions","Test"], null, data[1]),"ω",m + " ω tested"]; record['plot'] = ["ω",record['dist'][1]]; table.push (record); if (get_has_background (data[1])) { record = {'Model' : data[0] + " " + m}; record['dist'] = ["Background", getRateDistribution (["fits",m,"Rate Distributions","Background"],null, data[1]),"ω",m + " ω background" ]; record['plot'] = ["ω",record['dist'][1]]; table.push (record); } if (get_srv_rate_classes (data[1])) { record = {'Model' : data[0] + " " + m}; record['dist'] = ["Synonymous rates", getRateDistribution (["fits",m,"Rate Distributions","Synonymous site-to-site rates"], ["rate", "proportion"], data[1]),"",m + " syn rates" ]; record['plot'] = ["",record['dist'][1]]; table.push (record); } }); } }) return table; //getRateDistribution (["fits","Unconstrained model","Rate Distributions","Test"]) }35:T7aa,compBSModels = { let mref = _.groupBy (bsPositiveSelection, (d)=>d.Branch + "|" + d.Codon); let dh_support = {}, th_support = {}, mh_support = {}; if (twoHBranchSite) { dh_support = _.mapValues (_.groupBy (twoHBranchSite, (d)=>d.Branch + "|" + d.Codon), d=>d[0].ER); } if (threeHBranchSite) { th_support = _.mapValues (_.groupBy (threeHBranchSite, (d)=>d.Branch + "|" + d.Codon), d=>d[0].ER); } if (multiHBranchSite) { mh_support = _.mapValues (_.groupBy (multiHBranchSite, (d)=>d.Branch + "|" + d.Codon), d=>d[0].ER); } let comp_omega = getRateDistribution (["fits","Unconstrained model","Rate Distributions","Test"], null, results_json_compare); let site_filter = new RegExp (scatter_plot[3].length ? scatter_plot[3] : '.'); let branch_filter = new RegExp (scatter_plot[4].length ? scatter_plot[4] : '.'); let res = []; _.each (posteriorsPerBranchSite (comp_omega.length - 1, comp_omega[comp_omega.length - 1].weight / (1-comp_omega[comp_omega.length - 1].weight), results_json_compare), (d)=> { if (d.Branch.search (branch_filter) >= 0 && (""+d.Codon).search (site_filter) >= 0) { let key = d.Branch + "|" + d.Codon; let record = { 'Branch' : d.Branch, 'Codon' : d.Codon, 'Comparison model' : d.ER, 'Reference model' : mref[key][0].ER }; if (mh_rates['DH']) { if (key in dh_support) { record['2H support'] = dh_support[key]; } } if (mh_rates['TH']) { if (key in th_support) { record['3H support'] = th_support[key]; } } if (mh_rates['DH'] && mh_rates['TH']) { if (key in mh_support) { record['2H+3H support'] = mh_support[key]; } } res.push (record); } }); return res; }36: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"} } }]}) ) } }37: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"}} }}] }}38: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"]} } } } ] } }39:T78e,ERPlot = (data, from, step, key)=> { let scale = d3.extent (data, (d)=>d[key]); scale[1] = 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), (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}, } } ] }; }3a:T4d1,ERPlotComp = (data, key1, key2,key3)=> { return { "width" : 600, "height" : 600, "data" : {"values" : _.map (data, (d)=> { let dd = _.clone (d); _.each ([key1, key2], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "encoding": { "x": { "field": key1, "type" : "quantitative", "scale" : {"type" : "log"}, "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Reference model"} } }, "layer": [ { "mark": {"type": "point", "size" : 64, "filled" : true, "color" : "grey", "opacity" : 0.8, "tooltip" : {"content" : "data"}}, "encoding": { "y": { "field": key2, "type" : "quantitative", "scale" : {"type" : "log"}, "axis" : {"grid" : false, "titleFontSize" : 14, "title" : "Comparison model"} }, "color": { "field": key3, "type" : "quantitative", "legend" : {"orient" : "bottom-right"}, "scale" : {"scheme" : "turbo"} } } } ] }; }3b:T57a,cdsQuant = (data, key1, ref)=> { return { "config": { "mark": {"invalid": null} }, "width" : 600, "height" : 600, "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, //"sort" : "descending", "type": "quantitative" }, "y": { "field": "CC", "type": "quantitative", "scale" : {"type" : "linear"}, "title" : "Cumulative sum" } }}, { "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" } }} ] }; }3c:T7ef,BSPlotComp = (data, key1)=> { _.each (data, (d)=> { d["Reference model clipped"] = Math.max (d["Reference model"], 1e-5); d["Comparison model clipped"] = Math.max (d["Comparison model"], 1e-5); }); let domain_x = d3.extent (_.map (data, d=>d["Reference model clipped"])); let domain_y = d3.extent (_.map (data, d=>d["Comparison model clipped"])); domain_x[0] = Math.min (domain_x[0], domain_y[0]); domain_x[1] = Math.max (domain_x[1], domain_y[1]); return { "config": { "mark": {"invalid": null} }, "width" : 600, "height" : 600, "data" : {"values" : data}, "encoding": { "x": { "field": "Reference model clipped", "type" : "quantitative", "scale" : {"type" : "log", "domain" : domain_x}, "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Reference model"} } }, "layer": [ { "mark": {"type": "point", "size" : 64, "filled" : true, "color" : "grey", "opacity" : 0.8, "tooltip" : {"content" : "data"}}, "encoding": { "y": { "field": "Comparison model clipped", "type" : "quantitative", "scale" : {"type" : "log", "domain" : domain_x}, "axis" : {"grid" : false, "titleFontSize" : 14, "title" : "Comparison model"} }, "color": { "field": key1, "type" : "quantitative", "condition": { "test": "!datum['" + key1 + "']", "value": "#aaa" }, "legend" : {"orient" : "bottom-right"}, "scale" : {"type" : "symlog", "scheme" : "turbo"} }, "opacity": { "value" : 1., "type" : "quantitative", "condition": { "test": "!datum['" + key1 + "']", "value": "0.2" } } } } ] }; }3d:T52c,ScatterPlotComp = (data, x, y, color)=> { let domain_x = d3.extent (_.map (data, d=>d[x])); let domain_y = d3.extent (_.map (data, d=>d[y])); domain_x[0] = Math.min (domain_x[0], domain_y[0]); domain_x[1] = Math.max (domain_x[1], domain_y[1]); return { "width" : 600, "height" : 600, "data" : {"values" : data}, "encoding": { "x": { "field": x, "type" : "quantitative", "scale" : {"type" : "linear", "domain" : domain_x}, "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Reference model"} } }, "layer": [ { "mark": {"type": "point", "size" : 64, "filled" : true, "color" : "grey", "opacity" : 0.8, "tooltip" : {"content" : "data"}}, "encoding": { "y": { "field": y, "type" : "quantitative", "scale" : {"type" : "linear", "domain" : domain_x}, "axis" : {"grid" : false, "titleFontSize" : 14, "title" : "Comparison model"} }, "color": { "field": color, "type" : "quantitative", "legend" : {"orient" : "bottom-right"}, "scale" : {"type" : "symlog", "scheme" : "turbo"} } } } ] }; }3e:T6f4,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" : {"field" : "ER (constrained)", "type" : "quantitative", "scale" : {"type" : "log", "scheme": "turbo"}, "legend" : {"orient" : "top"}} } } ] }; 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; }3f:T45d,BSPosteriorPlot = (data, from, step)=> { const branch_order = treeNodeOrdering (0); let spec = { "width": {"step": 10}, "height" : {"step" : 10}, "data" : {"values" : _.filter (data, (d,i)=> d.Codon >= from && d.Codon <= from + step), }, "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon", "labelFontSize" : 8} }, "y": { "field": "Branch", "scale" : {"domain" : branch_order}, "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Branch"} } }, "layer": [ { "mark": {"type": "rect", "size" : 2, "color" : "lightgrey", "opacity" : 1.0, "tooltip" : true}, "encoding": { "color": { "field": "ER", "type" : "quantitative", "legend" : {"orient" : "top"}, "scale" : {"type" : "log", "scheme" : "turbo"} } } } ] }; return spec; }40:T1163,display_tree = (i) => { 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; var t = T.render({ height:dim && dim[0], width:dim && dim[1], '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)), [0,1]); t.style_edges ((e,n) => { const is_tested = labels[n.target.data.name]; if (is_tested) { e.style ("opacity", color_scale(is_tested)).style ("stroke-width", "4").style ("stroke","firebrick"); 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(); return t; } 41:T24a4,display_tree_site = (i,s) => { const use_this_data = use_model == "Reference" ? results_json : results_json_compare; let dim = treeDim.length ? _.map (treeDim.split ("x"), (d)=>+d) : null; let T = tree_objects[i]; T.branch_length_accessor = (n)=>use_this_data["branch attributes"][i][n.data.name][branch_length] || 0; let node_labels = generateNodeLabels (T, use_this_data["substitutions"][i][(+s)-1]); var t = T.render({ height:dim && dim[0], width:dim && dim[1], '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 } ); let labelDomain = new Set(); let showAA = treeLabels.indexOf ("amino-acids") >= 0; let showCodons = treeLabels.indexOf ("codons") >= 0; let showOnlyMH = treeLabels.indexOf ("show only multiple hits") >= 0; let showOnlyNS = treeLabels.indexOf ("show only non-synonymous changes") >= 0; let showSN = treeLabels.indexOf ("show sequence names") >= 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 (showSN) 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 = use_this_data["tested"][i][n.target.data.name] == "test"; if (is_tested) { e.style ("stroke", "firebrick"); } else { e.style ("stroke", null); } }); } else if (color_branches == "Difference in support for selection") { let branch_values = {}; let dist1 = test_omega; let dist2 = getRateDistribution (["fits","Unconstrained model","Rate Distributions","Test"], null, results_json_compare); let prior1 = dist1[dist1.length-1].weight; let prior2 = dist2[dist2.length-1].weight; prior1 = prior1 / (1-prior1); prior2 = prior2 / (1-prior1); 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)/prior1; } posteriors = results_json_compare ["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]; let comp = posteriors/(1-posteriors)/prior2; if (comp > 1 || branch_values [n.data.name] > 1) branch_values [n.data.name] /= comp ; else 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.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); } }); } else if (color_branches == "Support for selection") { let branch_values = {}; const useDist = use_model == "Reference" ? test_omega : getRateDistribution (["fits","Unconstrained model","Rate Distributions","Test"], null, results_json_compare); let prior = useDist[useDist.length-1].weight; prior = prior / (1-prior); T.traverse_and_compute ( (n)=> { let posteriors = use_this_data["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)),[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 color_scale = d3.scaleSequential([0,3], [0,1]); t.style_edges ((e,n) => { const is_tested = node_labels[n.target.data.name]; if (is_tested && is_tested[3]) { e.style ("opacity", color_scale(is_tested[3])).style ("stroke-width", "5").style ("stroke","firebrick"); 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 = use_this_data["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)),[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(); return t; }42:Tc29,html` `43:[] 2e:{"title":"Compare BUSTED analysis results","cells":[{"id":0,"value":"md`# Compare BUSTED analysis results`","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":3432,"value":"viewof results_file_compare = params.get (\"url\") ? localFileInput({title: \"Compare with\", accept: \".json\", value : params.get (\"url\"), disable: true}) : localFileInput({accept: \".json\"}) ","pinned":false,"hidden":false,"mode":"ojs"},{"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.\")}\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1673,"value":"citation = md`**Suggested citation**: ${results_json.analysis[\"citation\"]}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1119,"value":"viewof pv = Inputs.text({label: html`Evidence ratio threshold`, value: \"10\", submit: \"Update\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":592,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":2362,"value":"#### Alignment-wide results","pinned":false,"hidden":false,"mode":"md"},{"id":3464,"value":"viewof dist_table_filter = Inputs.search (distributionTable)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2417,"value":"viewof rate_table = Inputs.table (dist_table_filter, {\n header : {'LogL' : \"Log (L)\", \"AICc\" : \"AIC-c\", \"p\" : html`Params.{\n return html`${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\" : 200, \"scale\" : \"symlog\", \"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"},{"id":2647,"value":"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"},{"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"},{"id":538,"value":"fig1caption=md`**Figure 1**. ${plot_legends[plot_type]}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3528,"value":"viewof scatter_plot = Inputs.form([\n Inputs.select(_.keys (fig1data[0]).slice (2), {label: \"x\"}),\n Inputs.select(_.keys (fig1data[0]).slice (2), {label: \"y\"}),\n Inputs.select(colorOptions.slice (2), {label: \"color\"}),\n Inputs.text({label: \"site filter\", submit : true, value: \".*\"}),\n Inputs.text({label: \"branch filter\", value: \".*\", submit : true})\n])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1328,"value":"figure1 = vegalite (plot_specs[plot_type])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"table1caption=md`**Table 1**. Detailed site-by-site results from the BUSTED analysis`","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":1549,"value":"viewof tree_id = autoSelect ({options:_.map (_.range (1,tree_objects.length+1), (d)=>\"Partition \" + d).concat(_.map (_.range (1,results_json.input[\"number of sites\"]), (d)=>\"Codon \" + d)), size : 10, title: html`Tree to view`, placeholder : \"Select partition / codon tree to view\"})","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":3585,"value":"viewof use_model = Inputs.select([\"Reference\",\"Comparison\"],{value: \"Reference\", label: html`Use this model `})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3086,"value":"viewof treeLabels = Inputs.checkbox(\n [\"amino-acids\",\"codons\",\"show internal\",\"show only multiple hits\",\"show only non-synonymous changes\", \"show sequence names\"],{\"value\" : [\"amino-acids\"], label: html`Tree labels` }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"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"},{"id":3723,"value":"{\n tree_id;\n if (color_branches == \"Difference in support for selection\") \n return Legend(figure2.color_scale, {\n title: \"\",\n tickFormat: \"g\"\n });\n return html``;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3764,"value":"figure2.show()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2225,"value":"#### Code, data, and libraries","pinned":false,"hidden":false,"mode":"md"},{"id":15,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs"},{"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 return display_tree_site (partition_id, codon_index);\n \n } \n return display_tree((-1) + (+toDisplay[1])).show();\n }\n return md`### Select a tree option from the menu above to view`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":638,"value":"floatFormat = d3.format (\".4g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2518,"value":"proportionFormat = d3.format (\".5p\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2257,"value":"results_json = await get_json (params.get (\"url\"),results_file)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3440,"value":"results_json_compare = await get_json (params.get (\"url-comparison\"),results_file_compare)","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"get_json = async (json_source, input_object)=> {\n if (json_source) {\n return d3.json (json_source);\n }\n return input_object.json();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":1653,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1758,"value":"plot_options = [[\"Evidence ratio for ω>1 (constrained)\", (d)=>1], [\"Evidence ratio for ω>1 (optimized)\", (d)=>1], [\"Synonymous rates\", (d)=>srv_rate_classes > 0], [\"Support for positive selection\", (d)=>1], [\"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']], [\"LogL difference\", (d)=>results_json && results_json_compare], [\"LogL difference scatter\", (d)=>results_json && results_json_compare], [\"LR difference\", (d)=>results_json && results_json_compare], [\"LR difference scatter\", (d)=>results_json && results_json_compare], [\"Compare ER\", (d)=>results_json && results_json_compare], [\"Compare BS support\", (d)=>results_json && results_json_compare], [\"LR difference CDS\", (d)=>results_json && results_json_compare]]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1665,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1449,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"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"},{"id":3448,"value":"get_srv_rate_classes = (d)=> {\n if (d)\n return _.size (d.fits[\"Unconstrained model\"][\"Rate Distributions\"][\"Synonymous site-to-site rates\"]);\n return null;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2798,"value":"srv_distribution = getRateDistribution ([\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Synonymous site-to-site rates\"], [\"rate\",\"proportion\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":3486,"value":"get_srv_distribution = (d)=> {\n return getRateDistribution ([\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Synonymous site-to-site rates\"], [\"rate\",\"proportion\"], d)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2325,"value":"omega_rate_classes = _.size (test_omega)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2422,"value":"has_background = _.get (results_json, [\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Background\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":3453,"value":"get_has_background = (d)=> {\n if (d)\n return _.get (d, [\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Background\"]);\n else return null;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2331,"value":"srv_hmm = \"Viterbi synonymous rate path\" in results_json","pinned":false,"hidden":false,"mode":"ojs"},{"id":3728,"value":"treeColorScale = d3.scaleSequentialLog([1e-10, 100], d3.interpolatePuOr)","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":1260,"value":"table_colors = 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(n)=>results_json[\"branch attributes\"][i][n.data.name][\"Global MG94xREV\"];\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":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"},{"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"},{"id":3132,"value":"label_color_scale = d3.scaleOrdinal([], d3.schemeCategory10)","pinned":false,"hidden":false,"mode":"ojs"},{"id":756,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":3006,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":3257,"value":"tree_color_options = {\n let options = [\"Tested\",\"Support for selection\",\"Substitutions\",\"Difference in support for selection\"];\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"},{"id":78,"value":"phylotree=require (\"phylotree\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":432,"value":"vegalite = require 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