2f:T767,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" : ""} ${contributing_sites && contributing_sites.length == 1? "\n>Most of the statistical signal for episodic diversifying selection in this alignment is derived from a single codon site (**codon " + (1+contributing_sites[0]) + "**). This could be a sign of possible data quality issues, or outsized influence of a few substitutions, especially if they involve replacing multiple nucleotides along a short branch. You may want to examine the alignment at this site using BUSTED visualization tools, performing model-averaged inference, or rerunning the alignment with data at that site masked to confirm robustness of the result" : ""} ${+results_json.analysis.version >= 4.5 && has_error_sink ? "This analysis was run with the error sink category enabled" : ""} `30:Tcae,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 diversifying 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)
${results_json["Evidence Ratios"]["constrained"] ?_.filter (bsPositiveSelection, d=>d.ER >= 100).length : "N/A"} (branch, site) pairs with EBF ≥ 100	${contributing_sites ? contributing_sites.length : "N/A"} Sites contributing most signal to EDS detection	${!_.isUndefined(sub_fractions[0]) ? percentageFormat (sub_fractions[0]) : "N/A"}:${!_.isUndefined(sub_fractions[1]) ? percentageFormat (sub_fractions[1]) : "N/A"} Expected fractions of MH subs (2H:3H)

`31:T9b5,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).", "Error-sink support" : "Empirical Bayes Factors for branch/codon combinations placed in the error-sink category (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" })32:T7b3,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" : { //"autosize": {"resize" : true}, "vconcat" : _.map (_.range (1, results_json.input["number of sites"], er_step_size()), (d)=> { return BSPosteriorPlot (bsPositiveSelection, d, er_step_size()) }).concat (characterPlot (codonComposition (null, true), bsPositiveSelection))}, "Error-sink support" : { //"autosize": {"resize" : true}, "vconcat" : _.map (_.range (1, results_json.input["number of sites"], er_step_size()), (d)=> { return BSPosteriorPlot (bsErrorSink, d, er_step_size()) }).concat (characterPlot (codonComposition (null, false), bsErrorSink))}, "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") } )33: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` }]; }34: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"]) }35:T41a,codonComposition = (filter, diff_mode)=> { let results = []; let offset = 0; _.each (results_json["substitutions"], (data, partition) => { _.each (data, (per_site, site)=> { if (filter && ! filter (site, partition)) return; let info = generateNodeLabels (tree_objects[partition], per_site); if (diff_mode == false) { _.each (info, (p,i)=> { results.push ({'Key' : i + "|" + ((+site) + offset + 1), 'value' : p[0], 'aa' : p[1]}); }); } else { const root_label = info['root'][0]; _.each (info, (p,i)=> { let codon_label = _.map (p[0], (c,i)=>c == root_label[i] ? '.' : c).join (""); if (i == 'root') codon_label = p[0]; results.push ({'Key' : i + "|" + ((+site) + offset + 1), 'value' : codon_label, 'aa' : p[1]}); }); } }); offset += _.size (data); }); return results; }36:T7c1,characterPlot = (data, data2)=> { const branch_order = treeNodeOrdering (0, true); let N = results_json.input["number of sequences"]; let box_size = 10; let font_size = 12; console.log (data2); let spec = { "width": {"step": 2.5*font_size}, "height" : {"step" : font_size}, "data" : {"values" : data, }, "transform" : [ {"calculate" : "parseInt (split(datum.Key, '|')[1])", "as" : "Codon"}, {"calculate" : "split(datum.Key, '|')[0]", "as" : "Branch"}, {"filter": {"param": "character_view", "empty" : true}}, { "lookup": "Key", "from": { "data": {"values" : data2}, "key": "Key", "fields": ["ER"] } } ], "encoding": { "x": { "field": "Codon", "type" : "ordinal", "scale": {"domain": {"selection": "character_view", "encoding": "x"}}, "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Codon", "labelFontSize" : font_size} : null }, "y": { "field": "Branch", "scale": {"domain": {"selection": "character_view", "encoding": "y"}}, "type" : "ordinal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Branch", "labelFontSize" : font_size} : null } }, "layer": [ { "mark": {"type": "text", "clip" : true, "fontSize" : font_size, "font" : "monospace", "fontWeight" : "bold", "color" : "#444", "opacity" : 1.0, "tooltip" : true}, "encoding": { "text" : {"field" : "value"}, "color": { "field" : "aa" //"field": "ER", //"type" : "quantitative", //"legend" : {"orient" : "top"}, //"sort": "ascending", //"scale" : {"scheme" : "lightgreyred"} } } } ] }; return spec; }37: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" } } }]} }38: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"} } }]}) ) } }39: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"}} }}] }}3a: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"]} } } } ] } }3b: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}, } } ] }; }3c: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; }3d:T7d1,BSPosteriorPlot = (data, from, step)=> { const branch_order = treeNodeOrdering (0); let N = tested_branch_count; 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 = 4; font_size = 0;} } let spec = { "width": {"step": box_size}, "height" : {"step" : box_size}, "data" : {"values" : data }, "transform" : [ {"calculate" : "parseInt (split(datum.Key, '|')[1])", "as" : "Codon"}, {"calculate" : "split(datum.Key, '|')[0]", "as" : "Branch"}, {"filter" : {"field" : "Codon", "range" : [from, from+step-1]}} ], "params": [{ "name": "character_view", "select": { "type": "interval", "encodings": ["x","y"], "nearest" : "true", "mark" : {"stroke" : "#444", "strokeWidth" : 3}, }, "value": {"x": _.range (20), "y": branch_order} }], "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" : "redyellowblue", "domainMid" : 1} } } } ] }; return spec; }3e: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}, } } ] }; }3f: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" } } } ] }; }40:T13ce,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" || color_branches == "Error-sink support") { let branch_values = {}; const es = color_branches == "Error-sink support"; let rate_class = es ? 0 : (test_omega.length - 1 + (has_error_sink ? 1 : 0)); let prior = es ? get_error_sink_rate ("Test")["proportion"] : 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 class"][rate_class]; 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; } 41:T1e30,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" || color_branches == "Error-sink support") { let branch_values = {}; const es = color_branches == "Error-sink support"; let rate_class = es ? 0 : (test_omega.length - 1 + (has_error_sink ? 1 : 0)); let prior = es ? get_error_sink_rate ("Test")["proportion"] : 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"][rate_class][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; }42:Tc37,bustedStyle = html` `43:[] 2d:{"title":"BUSTED analysis result visualization","cells":[{"id":0,"value":"md`# BUSTED analysis result visualization`","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"viewof results_file = params.get (\"url\") ? localFileInput({accept: [\".json\",\".gz\"], value : params.get (\"url\"), disable: true}) : localFileInput({accept: [\".json\",\".gz\"]}) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1302,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":3809,"value":"md`${has_error_sink_nt? \"

Possible alignment errors detected

A \" +proportionFormat (get_error_sink_rate (\"Test\")[\"proportion\"]) + \" fraction of the alignment (test branches\" + (has_background ? \", and \" + proportionFormat (get_error_sink_rate (\"Background\")[\"proportion\"]) + \" of background branches)\" : \")\" ) + \" was placed in the error sink rate class, meaning that misalignment or other data quality issues may be present. You may use exploratory plots and other components on this page to further explore this.

\" : \"\"}`","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":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":2362,"value":"alignmentHeader = md`#### Alignment-wide results`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2417,"value":"viewof rate_table = Inputs.table (distributionTable, {\n header : {'LogL' : \"Log (L)\", \"AICc\" : \"AIC-c\", \"p\" : html`Params.{\n return html`${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"},{"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"},{"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_type ? plot_legends[plot_type] : \"No plotting options available\"}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1328,"value":"figure1 = plot_type ? vegalite (plot_specs[plot_type]) : md``","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:treeViewOptions, 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":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"},{"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":3447,"value":"{\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"},{"id":3453,"value":"{\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"},{"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-1][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"},{"id":638,"value":"floatFormat = d3.format (\".4g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":3791,"value":"percentageFormat = d3.format (\".2p\")","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\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3787,"value":"sub_fractions = _.map ([\"Fraction of subs rate at which 2 nucleotides are changed instantly within a single codon\", \"Fraction of subs rate at which 3 nucleotides are changed instantly within a single codon\"], (d)=>results_json[\"fits\"][\"Unconstrained model\"][\"Rate Distributions\"][d])","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"get_json = async (json_source)=> {\n if (json_source) {\n return d3.json (json_source);\n }\n try {\n return JSON.parse (pako.inflate(await results_file.arrayBuffer(), { to: 'string' }));//results_file.json();\n } catch {}\n return results_file.json();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":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 = [\n [\"Evidence ratio for ω>1 (constrained)\", (d)=>results_json[\"Evidence Ratios\"][\"constrained\"]], \n [\"Evidence ratio for ω>1 (optimized)\", (d)=>results_json[\"Evidence Ratios\"][\"optimized null\"]], \n [\"Synonymous rates\", (d)=>srv_rate_classes > 0], \n [\"Support for positive selection\", (d)=>bsPositiveSelection.length > 0], \n [\"Error-sink support\", (d)=>has_error_sink_nt && get_error_sink_rate (\"Test\")[\"proportion\"] > 0], \n [\"Site-level LR support\", (d)=>results_json[\"Evidence Ratios\"][\"optimized null\"]], \n [\"Support for 2H\", (d)=>mh_rates['DH']], \n [\"Support for 3H\", (d)=>mh_rates['TH']], \n [\"Support for 2H+3H\", (d)=>mh_rates['DH'] && mh_rates['TH']]\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1665,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":3737,"value":"contributing_sites = {\n let site_lr = _.sortBy (_.map (fig1data, (d,i)=>[d[\"LR\"],i]), (d)=>-d[0]);\n const lrs = d3.sum (site_lr, (d)=>d[0]);\n if (lrs > 2) {\n let sites = [];\n let s = site_lr[0][0];\n sites.push (site_lr[0][1]);\n let i = 1;\n while (s < lrs * 0.8) {\n s += site_lr[i][0]; \n sites.push (site_lr[i][1]);\n i ++;\n }\n return sites;\n }\n return null;\n \n}","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":3814,"value":"get_error_sink_rate = (tag) => {\n let rd = _.get (results_json, [\"fits\",\"Unconstrained model\",\"Rate Distributions\", tag, \"0\"]);\n return rd;\n}","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":2798,"value":"srv_distribution = getRateDistribution ([\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Synonymous site-to-site rates\"], [\"rate\",\"proportion\"])","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":2331,"value":"srv_hmm = \"Viterbi synonymous rate path\" in results_json","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 = ({\n 'Diversifying' : '#e3243b',\n 'Neutral' : '#444',\n 'Purifying' : 'green',\n 'Invariable' : '#CCC'\n })","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":2554,"value":"test_omega = getRateDistribution ([\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Test\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2419,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2369,"value":"getRateDistribution = (keys, tags)=> {\n tags = tags || [\"omega\", \"proportion\"];\n const rate_info = _.get (results_json, keys);\n if (rate_info) {\n let clip_first = has_error_sink && tags[0] == 'omega';\n \n return _.sortBy (_.map (clip_first ? _.chain(rate_info).toPairs().filter ((d)=>d[0] != '0').fromPairs().value() : 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"},{"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"},{"id":1097,"value":"sites_table = {\n return [{}, siteTableData[0], siteTableData[1]];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"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 ({'Key' : branch + \"|\" + (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"},{"id":3910,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":3925,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":3604,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":3882,"value":"bsErrorSink = {\n if (has_error_sink_nt) {\n let weight = get_error_sink_rate (\"Test\")[\"proportion\"];\n return posteriorsPerBranchSite (0, weight / (1-weight));\n }\n return [];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2931,"value":"bsPositiveSelection = {\n let w = test_omega[test_omega.length - 1].weight;\n if (w < 1) {\n return posteriorsPerBranchSite (test_omega.length - 1 + (has_error_sink ? 1 :0),w / (1-w));\n }\n return [];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3166,"value":"twoHBranchSite = mutliHitER (\"Evidence ratio for 2H\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":3176,"value":"threeHBranchSite = mutliHitER (\"Evidence ratio for 3H\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":3180,"value":"multiHBranchSite = mutliHitER (\"Evidence ratio for 2H+3H\")","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":3112,"value":"treeNodeOrdering = (index, root)=> {\n let order = [];\n if (root) {order.push ('root');}\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"},{"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"},{"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"},{"id":3795,"value":"has_error_sink = {\n const hc = results_json[\"analysis\"][\"settings\"] && results_json[\"analysis\"][\"settings\"][\"error-sink\"];\n if (hc) {\n //if (get_error_sink_rate (\"Test\")[\"proportion\"] > 0) return true;\n //if (has_background && get_error_sink_rate (\"Background\")[\"proportion\"] > 0) return true;\n return true;\n }\n return false;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4018,"value":"has_error_sink_nt = {\n if (has_error_sink) {\n if (get_error_sink_rate (\"Test\")[\"proportion\"] > 0) return true;\n if (has_background && get_error_sink_rate (\"Background\")[\"proportion\"] > 0) return true;\n return false;\n }\n return false;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3502,"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"},{"id":9,"value":"d3 = require (\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"floatFmt = d3.format (\".2g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":841,"value":"seqNames = (tree)=> {\n let seq_names = [];\n tree.traverse_and_compute (n=>{\n if (n.children && n.children.length) return;\n seq_names.push (n.data.name);\n });\n return seq_names;\n};","pinned":false,"hidden":false,"mode":"ojs"},{"id":1748,"value":"dyn_range_cap = 10000","pinned":false,"hidden":false,"mode":"ojs"},{"id":1869,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":2012,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":1800,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":2738,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":2821,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":2948,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":1363,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":1702,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"id":13,"value":"_=require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"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"},{"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":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\"]+1), (d)=>\"Codon \" + d));\n }\n return opts;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"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 if (has_error_sink) options.push (\"Error-sink support\");\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 (\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":174,"value":"import {select, text, input, autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2230,"value":"params = new URLSearchParams(location.search)","pinned":false,"hidden":false,"mode":"ojs"},{"id":350,"value":"import {legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2277,"value":"import {localFileInput} from \"@mbostock/localfile\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2531,"value":"import {renderDiscreteDistribution,renderNDiscreteDistributions, renderTwoDiscreteDistributions} from \"@spond/omega-plots\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2997,"value":"import {translate_ambiguous_codon} from \"@spond/usual-vs-unusual-mutation-analyses\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3107,"value":"import {Legend, Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4174,"value":"pako = require('pako@2.1.0/dist/pako.min.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"$42","pinned":false,"hidden":false,"mode":"ojs"},{"id":598,"value":"simpleIcons = html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W43","stdlib":"1","titling":"auto"}