30:T819,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	${has_resamples ? has_resamples : "N/A"} bootstrap replicates	${count_sites} sites subject to episodic diversifying selection
${selected_branches_per_selected_site} median branches with support for selection/selected site	${count_sites_with_variation} sites with variable ω across branches	${meme_rates} ω classes per site

`31:T6ec,plot_legends = ({ "p-values for selection" : "P-values derived from the " + (has_resamples ? "parametric bootstrap" : "asymptotic mixture Χ² ") + " test statistic for likelihood ratio tests for episodic diversifying selection. Solid line = user selected significance threshold.", "p-values for variability" : "P-values derived from the asymptotic mixture Χ²₂ test statistic for likelihood ratio tests for variable ω at this site. Solid line = user selected significance threshold.", "Site rates" : "Site-level rate maximum likelihood estimates (MLE). For each site, the horizontal tick depicts the synonymous rate (α) MLE. Circles show non-synonymous (β- and β+) MLEs, and the size of a circle reflects the weight parameter inferred for the corresponding rate. The non-synonymous rate estimates are connected by a vertical line to enhance readability and show the range of inferred non-synonymous rates and their relationship to the synonymous rate. Estimates above " + dyn_range_cap +" are censored at this value.", "Dense rate plot" : "Maximum likelihood estimates of synonymous (α), non-synonymous rates (β-, β+), and non-synonymous weights (p-,p+) at each site. Estimates above " + dyn_range_cap +" are censored at this value. p-values for episodic diversifying selection are also shown", "Rate density plots" : "Kernel density estimates of site-level rate estimates. Means are shown with red rules. Estimates above " + dyn_range_cap +" are censored at this value.", "Support for positive selection": "Empirical Bayes Factors for ω>1 at a particular branch and site (only tested branches are included).", "Q-Q plots" : "Comparison of asymptotic vs boostrap LRT distributions (limited to 60 sites)." })32:T507,plot_specs = ( { "p-values for selection" : { "width": 800, "height": 150, "vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> { return ERPlot (fig1data, d, 70, siteTableData[2][pv_column][2], "p-value for selection", true, "log") })}, "p-values for variability" : { "width": 800, "height": 150, "vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> { return ERPlot (fig1data, d, 70, has_site_LRT ? siteTableData[2][11][2] : [], "p-value for variability", true, "log") })}, "Site rates" : { "width": 800, "height": 150, "vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> { return alpha_beta_plot (fig1data, d, 70) })}, "Rate density plots" : rate_density (fig1data), "Dense rate plot" : denser_plot(fig1data), "Support for positive selection" : { "vconcat" : _.map (_.range (1, results_json.input["number of sites"], er_step_size()), (d)=> { return BSPosteriorPlot (bsPositiveSelection, d, er_step_size()) })}, "Q-Q plots" : has_resamples ? { "columns": 6, "hconcat": _.map (_.map (_.filter (table1, (d)=>d.class != "Invariable").slice (0,60), (d)=>[d.Partition, d.Codon]), (d)=>qq_plot (qq(_.map (results_json.MLE.LRT[d[0]-1][d[1]-1], (d)=>(d[0]))), "Site "+d[1]))} : null } )33:Tc43,siteTableData = { let site_info = []; let index = 0; let show_distribution = table_options.indexOf ('Distribution plot') >= 0; let show_q_values = table_options.indexOf ('Show q-values') >= 0; let show_substitutions = table_options.indexOf ('Show substitutions (tested branches)') >= 0; const mle_headers = _.map (results_json["MLE"]["headers"], (d)=>{ d[2] = ( (md`${d[0]}`).innerText); return d; }); let q_values = []; _.each (results_json["data partitions"], (pinfo, partition)=> { const mle_data = results_json["MLE"]["content"][partition]; _.each (pinfo["coverage"][0], (ignore, i)=> { let site_record = { 'Partition' : siteIndexPartitionCodon[index][0], 'Codon' : siteIndexPartitionCodon[index][1] }; if (show_distribution) { site_record['dN/dS'] = omega_plot (mle_data[i]); } _.each (mle_headers, (info, idx)=> { if (idx < 4+2*meme_rates) { site_record[info[2]] = mle_data[i][idx]; } }); let site_class = "Neutral"; let siteRates = [mle_data[i][0]]; for (let cc = 0; cc < meme_rates; cc++) { siteRates.push (mle_data[i][1 + 2*cc]); } if (d3.max (siteRates) == 0.) { site_class = "Invariable"; } else { if (mle_data[i][2+2*meme_rates] <= +pv) { site_class = "Diversifying"; } } if (show_q_values) { site_record['q'] = 1; q_values.push ([site_info.length, mle_data[i][6]]); } if (show_substitutions) { site_record['Substitutions'] = generateSubstitutionLists (tree_objects[partition],results_json["substitutions"][partition][i],results_json.tested[partition]); } site_record['class'] = site_class; site_info.push (site_record); index++; }) }); if (show_q_values) { q_values = _.map (_.sortBy (q_values, (d)=>d[1]), (d,i)=> [d[0],d[1]*results_json.input["number of sites"]/(i+1)]); _.each (q_values, (d)=> { site_info[d[0]]['q'] = Math.min (1,d[1]); }); } let options = { 'Partition' : html`Part.`, 'Codon' : html`Codon`, 'class' : html`Class`, 'dN/dS' : html`dN/dS` }; _.each (mle_headers, (info, idx)=> { if (idx == 0) { options[info[2]] = html`${info[0]}`; } else if (idx != 8) { options[info[2]] = html`${info[0]}`; } }); return [site_info, options,mle_headers]; }34:T6c3,generateSubstitutionLists = (T, labels, test_set)=> { if (!labels) return []; let L = {}; let subs = {}; T.traverse_and_compute (function (n) { if (n.data.name in labels) { L[n.data.name] = [labels[n.data.name], translate_ambiguous_codon (labels[n.data.name]),'',0]; if (n.parent) { L[n.data.name][2] = L[n.parent.data.name][0]; _.each (L[n.data.name][0], (c,i)=> { const c2 = L[n.data.name][2][i]; if (c2 != c && c != '-' && c2 != '-') { L[n.data.name][3] ++; } }); if (L[n.data.name][3] && test_set[n.data.name] == "test") { let sub; if (L[n.parent.data.name][0] < L[n.data.name][0]) { sub = L[n.parent.data.name][0] + "(" + L[n.parent.data.name][1] + "):" + L[n.data.name][0] + "(" + L[n.data.name][1] + ")"; } else { sub = L[n.data.name][0] + "(" + L[n.data.name][1] + "):" + L[n.parent.data.name][0] + "(" + L[n.parent.data.name][1] + ")"; } if (sub in subs) { subs[sub]++; } else { subs[sub] = 1; } } } } else { if (n.parent) { L[n.data.name] = _.clone (L[n.parent.data.name]); L[n.data.name][2] = L[n.data.name][0]; L[n.data.name][3] = 0; } else { L['root'] = [labels["root"], translate_ambiguous_codon (labels["root"]), "", 0]; } } },"pre-order"); return _.sortBy (_.toPairs (subs), d=>-d[1]); }35:T830,rate_density = (data)=> { let rate_options = rate_plot_vars; return { "data" : {"values" : _.map (data, (d)=> { let dd = _.clone (d); _.each (rate_options, (f)=> { dd[f[0]] = Math.min (dyn_range_cap, dd[f[0]]); }); 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"} } }]}) ) } }36: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"}} }}] }}37: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"]} } } } ] } }38:T90f,ERPlot = (data, from, step, key, label, low, scale_type)=> { let scale = d3.extent (data, (d)=>d[key]); scale_type = scale_type || "linear"; scale[1] = Math.min (dyn_range_cap,Math.max (scale[1], pv)); if (scale_type == "log") scale[0] = Math.max (scale[0], 1e-20); //scale = d3.nice (scale[0], scale[1], 10); return { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=> i >= from -1 && i < from + step -1), // -1 because 0 indexing && // remove "=" from the latter "<=" (d)=> { let dd = _.clone (d); _.each ([key], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); if (scale_type == "log") { dd[f] = Math.max (1e-20, 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" : scale_type, "domain" : scale}, "axis" : {"grid" : false, "title" : label} } } }, { "mark": { "stroke": "black", "type": "point", "size" : 100, "filled" : true, "color" : (low ? "lightgrey" : "firebrick"), "tooltip" : {"contents" : "data"}, "opacity" : 1.}, "encoding": { "y": { "field": key, "type" : "quantitative", }, "color" : {"condition": {"test": "datum['" + key + "'] " + (low ? "<=" : ">=") + 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}, } } ] }; }39:T618,BSPosteriorPlot = (data, from, step)=> { const branch_order = treeNodeOrdering (0); let N = results_json.input["number of sequences"]; let box_size = 10; let font_size = 8; if (N > 50) { if (N <= 100) {box_size = 8; font_size = 6;} else if (N <= 200) {box_size = 5; font_size = 5;} else {box_size = 2; font_size = 0;} } let spec = { "width": {"step": box_size}, "height" : {"step" : box_size}, "data" : {"values" : _.filter (data, (d,i)=> d.Codon >= from && d.Codon < from + step), // remove "=" from the latter "<=" }, "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Codon", "labelFontSize" : font_size} : null }, "y": { "field": "Branch", "scale" : {"domain" : branch_order}, "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Branch", "labelFontSize" : font_size} : null } }, "layer": [ { "mark": {"type": "rect", "size" : 2, "color" : "lightgrey", "opacity" : 1.0, "tooltip" : true}, "encoding": { "color": { "field": "ER", "type" : "quantitative", "legend" : {"orient" : "top"}, "sort": "descending", "scale" : {"type" : "log", "scheme" : "redblue", "domainMid" : 1} } } } ] }; return spec; }3a:Td8b,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);}); let spec = { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i >= from -1 && i< from + step -1), // -1 because 0 indexing && // remove "=" from the latter "<=" (d)=> { let dd = _.clone (d); let minw = 1e100, maxw = 0; _.each (rate_plot_vars, (f,i)=> { dd[f[0]] = Math.min (dyn_range_cap, dd[f[0]]); if (i > 0) { if (dd[weight_plot_vars[i-1]] == 0) { delete dd[f[0]]; } else { minw = Math.min (minw, dd[f[0]] ); maxw = Math.max (maxw, dd[f[0]] ); } } }); dd["minw"] = minw; dd["maxw"] = maxw; //dd.alpha = -dd.alpha; return dd; })}, "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": "tick", "filled" : true, "tooltip" : true, thickness : 4, "fill" : "black"}, "encoding": { "y": { "field": "α", "type" : "quantitative", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Rate MLE"}, "scale" : {"type" : "symlog"} //"labelExpr": "datum.label > 0 ? 'β = ' + datum.label: (toNumber(datum.label) == '0' ? '0' : 'α = ' + replace (datum.label, /[^0-9\.]/,''))"} }, } }, { "mark": {"type": "rule", "tooltip" : true}, "encoding": { "y": { "field": "minw", "type" : "quantitative", }, "y2": { "field": "maxw", "type" : "quantitative", }, } }, { "mark": {"type": "point", "filled" : true, "tooltip" : true}, "encoding": { "y": { "field": "β+", "type" : "quantitative", }, "size" : {"field" : "p+","type" : "quantitative"}, "fill" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } } ] }; for (let i = 1; i < rate_plot_vars.length; i++) { spec["layer"].push ({ "mark": {"type": "point", "filled" : true, "tooltip" : true}, "encoding": { "y": { "field": rate_plot_vars[i][0], "type" : "quantitative", }, "size" : {"field" : weight_plot_vars[i-1],"type" : "quantitative"}, "fill" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }); } return spec; }3b:T4ff,denser_plot = (data)=> { let columns = [["α","α"]]; if (meme_rates == 2) { columns.push (...[["β-","β-"],["p-","p-"]]); } else { for (let i = 1; i < meme_rates; i++) { columns.push (["β" + i, "β" + i]); columns.push (["p" + i, "p" + i]); } } columns.push (...[["β+","β+"],["p+","p+"],["p-value", "p-value"]]); return { "data" : {"values" : _.map (data, (d)=> { let dd = _.clone (d); _.each (columns, (f)=> { dd[f[0]] = Math.min (dyn_range_cap, dd[f[0]]); }); return dd; })}, "vconcat" : _.map (columns, (cc,i)=> ({ "width" : 800, "height" : 50, "mark": {"type": "area", "color" : "lightblue", "stroke" : "black", "interpolate" : "step"}, "encoding": { "x": { "field": "Codon", "type" : "quantitative", "axis": {"grid" : false, "titleFontSize" : 14, "title" : i == columns.length -1 ? "Codon" : null} }, "y": { "field": cc[0], "type" : "quantitative", "axis": {"grid" : false, "titleFontSize" : 14, "title" : cc[1]}, "scale" : "symlog" } }})) }; }3c:Teeb,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; 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); }); if (color_branches == "Tested") { t.style_edges ((e,n) => { const is_tested = results_json["tested"][i][n.target.data.name] == "test"; if (is_tested) { e.style ("stroke", "firebrick"); } else { e.style ("stroke", null); } }); } else if (color_branches == "Support for Selection") { let branch_values = {}; let prior = test_omega[test_omega.length-1].weight; prior = prior / (1-prior); T.traverse_and_compute ( (n)=> { let posteriors = results_json["branch attributes"][i][n.data.name]; if (posteriors && posteriors["Posterior prob omega clas"]) { posteriors = posteriors["Posterior prob omega clas"][test_omega.length-1]; branch_values [n.data.name] = posteriors/(1-posteriors)/prior; if (branch_values [n.data.name] < 1) branch_values [n.data.name] = null; } }); let color_scale = d3.scaleSequentialLog(d3.extent (_.map (branch_values, (d)=>d)),[0.1,1]); t.style_edges ((e,n) => { const is_tested = branch_values[n.target.data.name]; if (is_tested) { e.style ("opacity", color_scale(is_tested)).style ("stroke-width", "5").style ("stroke","firebrick"); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } }); } else if (color_branches == "Substitutions") { let labels = subs_by_branch (i); let color_scale = d3.scaleSequential(d3.extent (_.map (labels, d=>d)), d3.interpolatePuOr); t.color_scale = color_scale; t.color_scale_title = "Min # of nucleotide substitutions"; t.style_edges ((e,n) => { const is_tested = labels[n.target.data.name]; if (is_tested) { e.style ("stroke", color_scale(is_tested)).style ("stroke-width", "4").style ("opacity",1.0); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } }); } t.placenodes(); t.update(); return t; } 3d:T1cd0,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; s = treeViewOptions[1][s][1]; 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); if (shade_branches == "Tested") { e.style ("opacity", results_json["tested"][i][n.data.name] == "test" ? 1.0 : 0.25); } else { e.style ("opacity",1.0); } }); if (shade_branches == "Tested") { t.style_edges ((e,n) => { const is_tested = results_json["tested"][i][n.target.data.name] == "test"; if (is_tested) { e.style ("opacity", 1.0); } else { e.style ("opacity", 0.25); } }); } else { t.style_edges ((e,n) => { e.style ("opacity", 1.); }); } 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); } e.style ("opacity", (shade_branches != "Tested" || is_tested) ? 1.0 : 0.25); }); } else if (color_branches == "Support for selection") { let branch_values = {}; let prior = get_prior_odds (i,s-1); //console.log (prior); //console.log (omega_rate_classes); 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"][meme_rates-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; //console.log (n.data.name, posteriors); } }); //console.log (branch_values); 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", is_tested > 1 ? "5" : "1").style ("opacity",null); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } e.style ("opacity", (shade_branches != "Tested" || results_json["tested"][i][n.target.data.name] == "test") ? 1.0 : 0.25); }); } 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); } e.style ("opacity", (shade_branches != "Tested" || results_json["tested"][i][n.target.data.name] == "test") ? 1.0 : 0.25); }); } t.placenodes(); t.update(); label_color_scale.domain (labelDomain); return t; }3e:Tc29,html` `3f:[] 2e:{"title":"MEME analysis result visualization","cells":[{"id":0,"value":"md`# MEME 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":4287,"value":"analysis_summary()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1119,"value":"viewof pv = Inputs.text({label: html`p-value threshold`, value: \"0.1\", submit: \"Update\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1673,"value":"citation = md`**Suggested citation**: ${results_json.analysis[\"citation\"]}`","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":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**. ${plot_type ? plot_legends[plot_type] : \"No plotting options available\"}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1328,"value":"figure1 = plot_type ? vegalite (plot_specs[plot_type]) : md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":3803,"value":"viewof table_filter = Inputs.checkbox([\"Diversifying\", \"Neutral\",\"Invariable\"], {value: [\"Diversifying\", \"Neutral\", \"Invariable\"], label: html`Show`, format: x => html`${x}`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3834,"value":"viewof table_options = Inputs.checkbox([\"Distribution plot\",\"Show q-values\",\"Show substitutions (tested branches)\"], {value: [\"Show q-values\"], label: html`Options`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"table1caption=md`**Table 1**. Detailed site-by-site results from the MEME analysis`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1114,"value":"viewof table1 = Inputs.table (sites_table[1], {\n rows : 20,\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[0], 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":4097,"value":"viewof shade_branches = Inputs.select(tree_color_options.concat (\"None\"),{value: \"None\", label: html`Opaqueness of 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":2257,"value":"results_json = await get_json (params.get (\"url\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4336,"value":"meme_rates = _.get (results_json, [\"analysis\", \"settings\", \"rates\"]) || 2","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 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":2518,"value":"proportionFormat = d3.format (\".5p\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":4208,"value":"get_json = async (json_source)=> {\n if (json_source) {\n if (json_source.substr (json_source.length-3) == \".gz\") {\n return JSON.parse (pako.inflate(await d3.buffer(json_source), { to: 'string' }));\n }\n return d3.json (json_source);\n try {\n return d3.json (json_source);\n } catch {}\n //results_file.json();\n }\n try {\n return JSON.parse (pako.inflate(await results_file.arrayBuffer(), { to: 'string' }));//results_file.json();\n } catch {}\n return results_file.json();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4350,"value":"pv_column = 2+2*meme_rates","pinned":false,"hidden":false,"mode":"ojs"},{"id":3599,"value":"count_sites = _.chain (results_json[\"MLE\"][\"content\"]).mapValues ((d)=>_.filter (d, (r)=>r[pv_column] <= +pv).length).values().sum().value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3628,"value":"count_sites_with_variation = has_site_LRT ? _.chain (results_json[\"MLE\"][\"content\"]).mapValues ((d)=>_.filter (d, (r)=>r[7+2*meme_rates] <= +pv).length).values().sum().value() : \"N/A\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3639,"value":"selected_branches_per_selected_site = count_sites ? (_.chain (results_json[\"MLE\"][\"content\"]).mapValues ((d)=>_.filter (d, (r)=>r[6] <= +pv)).mapValues ((d)=>_.sum(_.map (d, (r)=>r[7]))).values().sum().value() / count_sites).toFixed (2) : \"N/A\"","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 = [[\"p-values for selection\", (d)=>true],[\"p-values for variability\", (d)=>has_site_LRT], [\"Site rates\", (d)=>true], [\"Support for positive selection\", (d)=>bsPositiveSelection.length > 0 && !has_er_bug],[\"Dense rate plot\", (d)=>1], [\"Rate density plots\", (d)=>1],[\"Q-Q plots\", (d)=>has_resamples]]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1665,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1449,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1214,"value":"tested_branch_count = d3.median (_.chain (results_json.tested).map ().map((d)=>_.filter (_.map (d), (d)=>d==\"test\").length).value())","pinned":false,"hidden":false,"mode":"ojs"},{"id":3572,"value":"has_resamples = _.get (results_json, [\"MLE\",\"LRT\"]) ? _.sample (_.get (results_json, [\"MLE\",\"LRT\"])[\"0\"]).length : 0","pinned":false,"hidden":false,"mode":"ojs"},{"id":3677,"value":"has_substitutions = _.get (results_json, [\"substitutions\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":3622,"value":"has_site_LRT = _.find (_.get (results_json, [\"MLE\",\"headers\"]), (d)=>d[0] == \"Variation p\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2422,"value":"has_background = _.get (results_json, [\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Background\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1260,"value":"table_colors = ({\n 'Diversifying' : '#e3243b',\n 'Neutral' : '#444',\n 'Invariable' : '#CCC'\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":3669,"value":"omega_rate_classes=2","pinned":false,"hidden":false,"mode":"ojs"},{"id":2554,"value":"test_omega = getRateDistribution ([\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Test\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2369,"value":"getRateDistribution = (keys, tags)=> {\n tags = tags || [\"omega\", \"proportion\"];\n const rate_info = _.get (results_json, keys);\n if (rate_info) {\n return _.sortBy (_.map (rate_info, (d)=>({\n \"value\" : d[tags[0]],\n \"weight\" : d[tags[1]]\n })), (d)=>d.rate);\n }\n return null;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2676,"value":"siteIndexPartitionCodon = {\n return _.chain (results_json['data partitions']).map ((d,k)=>_.map (d['coverage'][0], (site)=>[+k+1,site+1])).flatten().value();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1097,"value":"sites_table = {\n return [\n {'class' : (d)=>html`${d}`, \n 'Substitutions' : (d)=>d.length == 0 ? \"-\" : html`

${_.map (d, (c)=>\"\"+c[1]+\" \" + c[0]).join (\"
\")}

`,\n 'dN/dS' : (d)=>renderNDiscreteDistributions ([d],{\"height\" : 20, \"width\" : 200, \"scale\" : \"sqrt\"})}, _.filter (siteTableData[0], (x)=>table_filter.indexOf (x.class)>=0), siteTableData[1]];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3966,"value":"bsPositiveSelection = posteriorsPerBranchSite ()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3969,"value":"computeER = (prior, posterior) => {\n if (prior < 1) prior = prior / (1-prior); else prior = Infinity;\n if (posterior < 1) posterior = posterior / (1-posterior); else posterior = Infinity;\n if (posterior > 0) {\n return posterior / prior;\n } else {\n if (prior == 0) return 1;\n return Infinity;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2911,"value":"posteriorsPerBranchSite = (rate_class)=> {\n rate_class = rate_class || meme_rates-1;\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 let prior = results_json['MLE']['content'][partition][i][meme_rates*2];\n results.push ({'Branch' : branch, 'Codon' : i + offset + 1, 'Posterior' : p, 'ER' : computeER (prior, p)});\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":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":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":4154,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":3112,"value":"treeNodeOrdering = (index)=> {\n let order = [];\n const T = tree_objects[index];\n function sort_nodes (asc) {\n T.traverse_and_compute (function (n) {\n var d = 1;\n if (n.children && n.children.length) {\n d += d3.max (n.children, function (d) { return d[\"count_depth\"];});\n } \n\n n[\"count_depth\"] = d;\n });\n T.resortChildren (function (a,b) {\n return (a[\"count_depth\"] - b[\"count_depth\"]) * (asc ? 1 : -1);\n });\n }\n sort_nodes (true);\n T.traverse_and_compute (function (n) {\n if (results_json.tested[index][n.data.name] == \"test\") {\n order.push (n.data.name);\n }\n });\n return order;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"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":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":4366,"value":"has_er_bug = {\n if (meme_rates > 2) {\n const ers = _.get (_.sample (_.get (results_json, [\"branch attributes\",\"0\"])), \"Posterior prob omega class by site\");\n if (ers) {\n return ers.length < meme_rates;\n } \n }\n return false;\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":4381,"value":"rate_plot_vars = {\n let rate_options = [[\"α\",\"α\"]];\n\n if (meme_rates == 2) {\n if (_.find (results_json[\"MLE\"][\"headers\"], d=>d[3] == \"β-\")) {\n rate_options.push ([\"β-\",\"β-\"]);\n } else {\n rate_options.push ([\"β1\",\"β1\"]);\n }\n } else {\n for (let i = 1; i < meme_rates; i++) {\n rate_options.push ([\"β\"+i,\"β\"+i]);\n }\n }\n rate_options.push ([\"β+\",\"β+\"]);\n return rate_options;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4395,"value":"weight_plot_vars = {\n let rate_options = [];\n\n if (rate_plot_vars[1][0] == \"β1\") {\n rate_options.push (\"p1\");\n } else {\n rate_options.push (\"p-\");\n }\n for (let i = 2; i < meme_rates; i++) {\n rate_options.push (\"p\"+i);\n }\n rate_options.push (\"p+\");\n return rate_options;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1869,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":2012,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1800,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":2738,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":2948,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":1702,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":2181,"value":"$3b","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":4027,"value":"qq = (v)=> {\n let vs = _.map (_.sortBy (v), (v)=> v <0 ? 0. : v);\n let qq = [{'bs' : 0, 'c2' : 0}];\n _.each (vs, (v, i)=> {\n qq.push ({'bs' : (i+1)/vs.length, 'c2' : ss.cdf (v, 1)});\n });\n qq.push ([{'bs' : 1, 'c2' : 1}]);\n return _.map (qq, (d)=>({'bs' : 1-d.bs, 'c2' : 1-d.c2}));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3710,"value":"get_prior_odds = (part,site)=> {\n const pp = results_json[\"MLE\"][\"content\"][part][site][meme_rates*2];\n if (pp < 1) return pp/(1-pp);\n return Infinity;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":756,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":3006,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":3441,"value":"treeViewOptions = {\n let opts = _.map (_.range (1,tree_objects.length+1), (d)=>\"Partition \" + d);\n let codonIdxToPartIdx = {};\n if (results_json.substitutions) {\n //opts = opts.concat(_.map (_.range (1,results_json.input[\"number of sites\"]), (d)=>\"Codon \" + d));\n let offset = 0;\n _.each (results_json.substitutions, (sites, partition)=> {\n _.each (sites, (subs, site)=> {\n if (subs) {\n let idx = ((+site) + 1 + offset);\n codonIdxToPartIdx[idx] = [partition, (+site)+1];\n opts.push (\"Codon \" + idx);\n }\n })\n offset += results_json[\"data partitions\"][partition].coverage[0].length;\n }); \n }\n return [opts,codonIdxToPartIdx];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3257,"value":"tree_color_options = {\n let options = [\"Tested\"];\n if (results_json.substitutions) {\n options.push (\"Support for selection\");\n options.push (\"Substitutions\");\n }\n return options;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3844,"value":"omega_plot = (record)=>{\n const ratio = (beta, alpha)=> {\n if (alpha > 0) {\n return beta/alpha;\n }\n if (alpha == 0) {\n if (beta == 0) return 1;\n }\n return 100;\n }\n \n let alpha = record[0];\n\n let rateInfo = [];\n for (let i = 1; i <= 2*meme_rates; i+=2) {\n rateInfo.push (\n {\n 'value' : ratio (record[i], alpha),\n 'weight' : record[i+1]\n }\n );\n }\n\n \n return rateInfo;\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4264,"value":"async function* analysis_summary () {\n yield \"Waiting for the .JSON file to be loaded\";\n\n yield md`Based on the likelihood ratio test, _episodic diversifying selection_ has acted on **${count_sites}** sites in this dataset (p≤${pv}).
MEME analysis (v${results_json.analysis.version}) was performed on the alignment from ${results_json.input[\"file name\"]} using HyPhy v${results_json.runtime}. ${has_resamples > 0 ? \"This analysis used parametric bootstrap with \" + has_resamples + \" replicates to test for signicance.\" : \"\"} ${+results_json.analysis.version < 3.0 ? \"Some of the visualizations are not available for MEME analyses before v3.0\" : \"\"}`\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,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":3839,"value":"import {renderNDiscreteDistributions} from \"@spond/omega-plots\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4030,"value":"ss = require('https://bundle.run/chi-squared@1.1.0')","pinned":false,"hidden":false,"mode":"ojs"},{"id":4211,"value":"pako = require('pako@2.1.0/dist/pako.min.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":598,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3f","stdlib":"1","titling":"auto"}