31:T550,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	${property_set} ${property_count}-property set	${count_sites} sites where properties are important

`32:T711,plot_legends = ({ "q-values for selection" : "Q-values derived from the asymptotic mixture Χ² " + " test statistic for the likelihood ratio omnibus test for property importance. Solid line = user selected significance threshold.", "Property support heatmap" : "Point estimates of property importance parameters (λ), shown using different colors; λ>0 implies that changes to this property are disfavored, λ<0 - that they are favored. Uncorrected P-values for the likelihood ratio tests for individual property importance at a given site are shown through size and opacity. Overall test significance is presented as FDR-corrected q-values. ", "Site rates" : "Site-level rate maximum likelihood estimates (MLE). For each site, the horizontal tick depicts the synonymous rate (α) MLE. The circles show the baseline non-synonymous rate (β) MLEs under PRIME, and squares -- the same rate estimated with FEL (no property effect). 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)." })33:T759,siteTableData = { let site_info = []; let index = 0; const mle_headers = _.map (results_json["MLE"]["headers"], (d)=>{ d[2] = ( (md`${d[0]}`).innerText); return d; }); _.each (results_json["data partitions"], (pinfo, partition)=> { const mle_data = results_json["MLE"]["content"][partition]; _.each (pinfo["coverage"][0], (ignore, i)=> { let site_record = { 'Partition' : siteIndexPartitionCodon[index][0], 'Codon' : siteIndexPartitionCodon[index][1] }; _.each (mle_headers, (info, idx)=> { //if (idx != 8) { site_record[info[2]] = mle_data[i][idx]; //} }); let site_class = "Property agnostic"; if (mle_data[i][0] == 0 && mle_data[i][2] == 0 && mle_data[i][3] == 0) { site_class = "Invariable"; } else { if (mle_data[i][10] <= +pv) { site_class = "Property informed"; } } site_record['class'] = site_class; site_info.push (site_record); index++; }) }); let options = { 'Partition' : html`Part.`, 'Codon' : html`Codon`, 'class' : html`Class`, 'dN/dS' : html`dN/dS` }; _.each (mle_headers, (info, idx)=> { if (idx == 0) { options[info[2]] = html`${info[0]}`; } else if (idx != 8) { options[info[2]] = html`${info[0]}`; } }); return [site_info, options,mle_headers]; }34:T84a,rate_density = (data)=> { let rate_options = [["α","α"],["β-","β-"],["β+","β+"]]; return { "data" : {"values" : _.map (data, (d)=> { let dd = _.clone (d); _.each (["α","β-","β+"], (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"} } }]}) ) } }35: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"}} }}] }}36: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"]} } } } ] } }37: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}, } } ] }; }38:T9ef,RateEstimatesPlot = (data, from, step)=> { //const branch_order = treeNodeOrdering (0); //let N = property_count; let box_size = 12; let font_size = 12; /* 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}, "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Codon", "labelFontSize" : font_size} : null }, "y": { "field": "prop", "type" : "nominal", "scale" : {"domain" : _.sortBy (_.uniq(_.map (property_MLEs, d=>d.prop)))}, "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Property", "labelFontSize" : font_size} : null } }, "layer": [ { "data" : {"values" : _.filter (data, (d,i)=> d.idx >= from && d.idx < from + step && d.prop != 'Overall'), // remove "=" from the latter "<=" }, "mark": {"type": "circle", "color" : "lightgrey", "opacity" : 1.0, "tooltip" : true}, "encoding": { "color": { "field": "lambda", "type" : "quantitative", "legend" : {"orient" : "top", "title" : "Property importance"}, "sort": "ascending", "scale" : {"type" : "symlog", "scheme" : "redblue", "domainMid" : 0}, }, "size" : {"field" : "p", "type" : "quantitative", "scale" : {"inverse" : true, domain : [1,0], "type" : "sqrt", range : [10, 100]}}, "opacity" : {"field" : "p", "type" : "quantitative", "scale" : {"inverse" : true, domain : [1,0], "type" : "sqrt"}} } }, { "data" : {"values" : _.filter (data, (d,i)=> d.idx >= from && d.idx < from + step && d.prop == 'Overall'), // remove "=" from the latter "<=" }, "mark": {"type": "circle", "color" : "black", "opacity" : 1.0, "tooltip" : true}, "encoding": { "size" : {"field" : "p", "type" : "quantitative", "scale" : {"inverse" : true, domain : [1,0], "type" : "sqrt", range : [10, 100]}}, "opacity" : {"field" : "p", "type" : "quantitative", "scale" : {"inverse" : true, domain : [1,0], "type" : "sqrt"}} } } ] }; return spec; }39:Tb64,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 >= from -1 && i< from + step -1), // -1 because 0 indexing && // remove "=" from the latter "<=" (d)=> { let dd = _.clone (d); _.each (["α","β", "FEL α", "FEL β"], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); //dd.α = -dd.α; return dd; })}, "transform" :[{"calculate": 'min (' + dyn_range_cap + ',exp (datum["β"]))', "as": "beta"}], "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"} }, } }, { "mark": {"type": "point", "filled" : true, "tooltip" : true}, "encoding": { "y": { "field": "beta", "type" : "quantitative", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Rate MLE"}, "scale" : {"type" : "symlog"}, }, "fill" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, { "mark": {"type": "square", "filled" : true, "tooltip" : true}, "encoding": { "y": { "field": "FEL β", "type" : "quantitative", }, "fill" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, { "mark": {"type": "rule", "tooltip" : true}, "encoding": { "y": { "field": "beta", "type" : "quantitative", }, "y2": { "field": "FEL β", "type" : "quantitative", }, } } ] }; }3a:T416,denser_plot = (data)=> { let columns = [["α","α"],["β-","β-"],["p-","p-"],["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" } }})) }; }3b: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; } 3c:T1bfe,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["MLE"]["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); 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"][omega_rate_classes-1][s-1]; branch_values [n.data.name] = posteriors/(1-posteriors)/prior; if (branch_values [n.data.name] < 1) branch_values [n.data.name] = null; } }); let color_scale = d3.scaleSequentialLog(d3.extent (_.map (branch_values, (d)=>d)),d3.interpolateTurbo); t.color_scale = color_scale; t.color_scale_title = "Empirical Bayes Factor"; t.style_edges ((e,n) => { const is_tested = branch_values[n.target.data.name]; if (is_tested) { e.style ("stroke", color_scale(is_tested)).style ("stroke-width", "5").style ("opacity",null); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } 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; }3d:Tc29,html` `3e:[] 2f:{"title":"PRIME analysis result visualization","cells":[{"id":0,"value":"md`# PRIME analysis result visualization`","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"viewof results_file = params.get (\"url\") ? localFileInput({accept: \".json\", value : params.get (\"url\"), disable: true}) : localFileInput({accept: \".json\"}) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1302,"value":"intro = md`Based on the likelihood ratio test, _biochemical properties_ (${property_set}) had a significant influence on the evolution of **${count_sites}** sites in this dataset (q≤${pv}).
PRIME analysis (v${results_json.analysis.version}) was performed on the alignment from ${results_json.input[\"file name\"]} using HyPhy v${results_json.runtime}.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1119,"value":"viewof pv = Inputs.text({label: html`q-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":"$31","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**. ${plot_type ? plot_legends[plot_type] : \"No plotting options available\"}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1328,"value":"figure1 = plot_type ? vegalite (plot_specs[plot_type]) : md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":3803,"value":"viewof table_filter = Inputs.checkbox([\"Property informed\", \"Property agnostic\",\"Invariable\"], {value: [\"Property informed\", \"Property agnostic\"], label: html`Show`, format: x => html`${x}`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"table1caption=md`**Table 1**. Detailed site-by-site results from the PRIME 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[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":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":99,"value":"get_json = async (json_source)=> {\n if (json_source) {\n return d3.json (json_source);\n }\n return results_file.json();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3599,"value":"count_sites = _.chain (results_json[\"MLE\"][\"content\"]).mapValues ((d)=>_.filter (d, (r)=>r[10] <= +pv).length).values().sum().value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":4138,"value":"property_set = results_json.analysis[\"residue_properties\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":4199,"value":"property_count = _.size (results_json['model']['residue_properties'])","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":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1758,"value":"plot_options = [\n [\"Property support heatmap\", (d)=>true],\n [\"q-values for selection\", (d)=>true],\n [\"Site rates\", (d)=>true], \n [\"Dense rate plot\", (d)=>1], \n [\"Rate density plots\", (d)=>1]]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1665,"value":"plot_specs = (\n{\n \"q-values for selection\" : {\n \"width\": 800, \"height\": 150, \n \"vconcat\" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {\n return ERPlot (fig1data, d, 70, siteTableData[2][10][2], \"q-value for selection\", true, \"log\")\n })},\n \"Site rates\" : {\n \"width\": 800, \"height\": 150, \n \"vconcat\" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {\n return alpha_beta_plot (fig1data, d, 70)\n })},\n \"Rate density plots\" : rate_density (fig1data),\n \"Dense rate plot\" : denser_plot(fig1data),\n \"Property support heatmap\" : {\n \"vconcat\" : _.map (_.range (1, fig1data.length + 1 , 80), (d)=> {\n return RateEstimatesPlot (property_MLEs, d, 80)\n })}\n}\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1449,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1214,"value":"tested_branch_count = d3.median (_.chain (results_json.tested).map ().map((d)=>_.filter (_.map (d), (d)=>d==\"test\").length).value())","pinned":false,"hidden":false,"mode":"ojs"},{"id":3572,"value":"has_resamples = _.get (results_json, [\"MLE\",\"LRT\"]) ? _.sample (_.get (results_json, [\"MLE\",\"LRT\"])[\"0\"]).length : 0","pinned":false,"hidden":false,"mode":"ojs"},{"id":3677,"value":"has_substitutions = _.get (results_json, [\"MLE\",\"substitutions\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2422,"value":"has_background = _.get (results_json, [\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Background\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1260,"value":"table_colors = ({\n 'Property informed' : '#e3243b',\n 'Property agnostic' : '#444',\n 'Invariable' : '#CCC'\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":3669,"value":"omega_rate_classes=2","pinned":false,"hidden":false,"mode":"ojs"},{"id":2554,"value":"test_omega = getRateDistribution ([\"fits\",\"Unconstrained model\",\"Rate Distributions\",\"Test\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2369,"value":"getRateDistribution = (keys, tags)=> {\n tags = tags || [\"omega\", \"proportion\"];\n const rate_info = _.get (results_json, keys);\n if (rate_info) {\n return _.sortBy (_.map (rate_info, (d)=>({\n \"value\" : d[tags[0]],\n \"weight\" : d[tags[1]]\n })), (d)=>d.rate);\n }\n return null;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2676,"value":"siteIndexPartitionCodon = {\n return _.chain (results_json['data partitions']).map ((d,k)=>_.map (d['coverage'][0], (site)=>[+k+1,site+1])).flatten().value();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1097,"value":"sites_table = {\n return [{'class' : (d)=>html`${d}`, '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 || 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 \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][4];\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":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 = 50","pinned":false,"hidden":false,"mode":"ojs"},{"id":1869,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2012,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1800,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":2738,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":4207,"value":"property_MLEs = {\n let records = [];\n _.each (sites_table[1], (r,idx) => {\n let record = {'Codon' : r.Codon};\n for (let i = 0; i < property_count; i++) {\n record ['idx'] = idx;\n record ['prop'] = i + 1;\n record ['lambda'] = r[\"λ\"+(i+1)];\n record ['p'] = Math.max (0, r[\"p\"+(i+1)]);\n records.push (_.clone(record));\n };\n record ['idx'] = idx;\n record['prop'] = 'Overall';\n record['lambda'] = 'N/A';\n record['p'] = r['q-value'];\n records.push (_.clone(record));\n }\n );\n return records;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4367,"value":"Min = (a,b)=>a{ \n let Min = (a,b)=>aMath.exp (a);\n //let lambda_Factor_I_bipolar = 1;\n //let lambda_Factor_II_secondary_structure = 1;\n let expr = \"\";\n for (let p in results_json[\"model\"][\"parameters\"]) {\n expr += (\"let \" + p + \" = 0;\");\n }\n return eval(expr + results_json[\"model\"][\"Q\"][0][4]);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2948,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":1702,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":2181,"value":"$3a","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][4];\n if (pp < 1) return pp/(1-pp);\n return Infinity;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":756,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":3006,"value":"$3c","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.MLE.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.MLE.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 = [\"None\",\"Tested\"];\n if (results_json.MLE.substitutions) {\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 {'value' : ratio (record[1], alpha),\n 'weight' : record[2]},\n {'value' : ratio (record[3], alpha),\n 'weight' : record[4]}, \n ];\n\n return rateInfo;\n\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":587,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":598,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3e","stdlib":"1","titling":"auto"}