31:T4ad,md` 1. ** ID ** : gene / alignment ID 2. ** Sequences ** : number of sequences in the aligment 3. ** Sites ** : number of sites in the alignment 3. ** L (FG) / L (BG) ** : tree length under the unconstrained model (FG branches / BG branches) 4. ** FG p-value ** : p-value of the BUSTED test on foreground branches; if significant (bolded), implies that there are detectable differences in selection **intensity** between ciliate groups. 5. ** BG p-value ** : p-value of the BUSTED test on background branches; if significant (bolded), implies that there are detectable differences in selection **intensity** between ciliate groups. 6. ** Sel. sites (FG/BG) ** : Sites that are showing support for positive selection in the Foreground/Background branches with the log-evidence ratio of 5 or higher. 7. ** DIFF p-value ** : p-value of the tests for differences in selection between FG and BG; if significant (bolded), implies that there are detectable differences in selection **regimes**. 8 ** Selection associated with phenotype ** : if Yes, then there is selection on **foreground**, no selection on **background**, and **differences** in selection between the two groups. `32:T8ed,displayed_tree = { let tree = tree_object[0]; var t = tree.render("#tree_container", { //height:1024, width:400, 'show-scale' : true, 'left-right-spacing': 'fit-to-size', //'top-bottom-spacing': 'fit-to-size', //'internal-names' : true, 'is-radial' : false, 'node_circle_size' : (n)=>0 }); const bs = (n) => { if (tree.is_leafnode (n)) { return 3; } return 1; }; let nodeStates = null; t.node_bubble_size = bs; t.phylotree.node_bubble_size = bs; t.options["draw-size-bubbles"] = true; t.font_size = 10; t.spacing_x (10); t.phylotree.branch_length_accessor = n=> { //console.log (summary_json[segment_id]); let bl = tree_object[2][n.data.name]; /*if (bl > 0.25) { n.data.censored_bl = true; return 0.25; } else { n.data.censored_bl = false; }*/ return bl; } t.node_label ((n)=>{ return n.data.name.split ("_").slice (0,3).join (" "); //return ""; }); function sort_nodes (asc) { tree.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; }); tree.resort_children (function (a,b) { return (a["count_depth"] - b["count_depth"]) * (asc ? 1 : -1); }); } sort_nodes (true); t.style_nodes ((e,n) => { e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); if (n.data.name in tree_object[1]) { let binfo = tree_object[1][n.data.name]; e.selectAll ("circle").style ('fill', species_colors (binfo)); e.selectAll ("text").style ('fill', species_colors (binfo)); } //} }); t.style_edges ((e,d) => { if (d.target.data.name in tree_object[1]) { let binfo = tree_object[1][d.target.data.name]; console.log (e); e.style ('stroke', species_colors (binfo)); e.style ('stroke-width', 2); } }); t.placenodes(); t.update(); return t; }33:T710,plotPie = (values, id, skipK)=>{ skipK = true; return vegalite ({ "columns": 3, "hconcat": _.map (_.groupBy (values, (d)=>d.type), (d, type) => { let is_dnds = "omega" in d[0]; let title = (is_dnds ? "ω for " : "") + type + (type == baseline_by_segment[id] ? " (reference)" : ""); let rate = is_dnds ? "omega" : "rate"; return { "title" : title, "data" : {"values" : d}, "encoding": { "theta": {"field": "proportion", "type": "quantitative", "stack": true}, "color": {"field": rate, "type": "quantitative", "title" : "ω", "legend" : {"titleFontSize" : 14, "orient" : "top"}, "scale": { "domain" : [0,1,4], "type" : "sqrt", "clamp" : true, "scheme": "sinebow" } }}, "layer" : [ { "mark": {"type": "arc", "innerRadius": 40, "outerRadius" : 60, tooltip : true, padAngle : 0.0, strokeWidth : 1, strokeOpacity: 1}, "encoding" : { "stroke" : {value : "black"}, } }, { "mark": {"type": "text", "outerRadius" : 70, "fontSize" : 18}, "encoding": { "color" : {"value" : "black"}, "text": { "field": rate, "type": "nominal", "format" : ".2f", "condition": {"test": "datum['" + rate +"'] <= 1.", "value": ""}, } } }, { "mark": {"type": "text", "outerRadius" : 20, "fontSize" : 18}, "encoding": { "color" : {"value" : "black"}, "text": { "field": "proportion", "type": "nominal", "format" : ".2p", "condition": {"test": "datum['" + rate +"'] <= 1.", "value": ""} } } } ]} }), "view": {"stroke": null} }) }34:Tc83,// Table in array format (no d3) averys_table = { const new_table = []; for (let j = 0; j < table_data.length; j++){ new_table[j] = {"ID": table_data[j]["ID"], "Sequences": table_data[j]["Sequences"], "Sites": table_data[j]["Sites"], "L BG": table_data[j]["L"]["background"], "L FG": table_data[j]["L"]["test"], "FG p-value": table_data[j]["FG p-value"], "BG p-value": table_data[j]["BG p-value"], "SelSites FG": table_data[j]["SelSites"]["0"], "SelSites BG": table_data[j]["SelSites"]["1"], "DIFF p-value": table_data[j]["DIFF p-value"], "Selection associated with phenotype": table_data[j]["PH"] /* "w1_t": "p1_t": "w2_t": "p2_t": "w3_t": "p3_t": "w1_b": "p1_b": "w2_b": "p2_b": "w3_b": "p3_b": */ }; } let count = 0; for (let gene in result_json){ for (let i=0; i 0.05 && averys_table[j]["DIFF p-value"] < 0.05){ pos[count] = { "ID" : averys_table[j]["ID"], "Sequences": averys_table[j]["Sequences"], "Sites": averys_table[j]["Sites"], "L BG": averys_table[j]["L BG"], "L FG": averys_table[j]["L FG"], "FG p-value": averys_table[j]["FG p-value"], "BG p-value": averys_table[j]["BG p-value"], "SelSites FG": averys_table[j]["SelSites FG"], "SelSites BG": averys_table[j]["SelSites BG"], "DIFF p-value": averys_table[j]["DIFF p-value"], "Selection associated with phenotype": averys_table[j]["Selection associated with phenotype"], "w1_t":averys_table[j]["w1_t"], "p1_t":averys_table[j]["p1_t"], "w2_t":averys_table[j]["w2_t"], "p2_t":averys_table[j]["p2_t"], "w3_t":averys_table[j]["w3_t"], "p3_t":averys_table[j]["p3_t"], "w1_b":averys_table[j]["w1_b"], "p1_b":averys_table[j]["p1_b"], "w2_b":averys_table[j]["w2_b"], "p2_b":averys_table[j]["p2_b"], "w3_b":averys_table[j]["w3_b"], "p3_b":averys_table[j]["p3_b"] } count = ++count } } return pos } /* let pos = [] for (const gene in result_json){ if(result_json[gene]["tests"]["FG"]["p-value"] < 0.05 && result_json[gene]["tests"]["BG"]["p-value"] > 0.05 && result_json[gene]["tests"]["DIFF"]["p-value"] < 0.05){ pos.push(gene) } } return pos } const new_table = []; const keys = ["ID", "Sequences", "Sites", "FG p-value", "BG p-value", "DIFF p-value", "PH"]; for (let j = 0; j < table_data.length; j++){ for (let i = 0; i < keys.length; i++){ new_table[j] = {"ID": table_data[j]["ID"], "Sequences": table_data[j]["Sequences"], "Sites": table_data[j]["Sites"], "L BG": table_data[j]["L"]["background"], "L FG": table_data[j]["L"]["test"], "FG p-value": table_data[j]["FG p-value"], "BG p-value": table_data[j]["BG p-value"], "SelSites FG": table_data[j]["SelSites"]["0"], "SelSites BG": table_data[j]["SelSites"]["1"], "DIFF p-value": table_data[j]["DIFF p-value"], "Selection associated with phenotype": table_data[j]["PH"] }; } } */36:T41c,// What hyphy prints out for decision tree results decision_tree = { if (_.get (result_json,[gene_id,"tests","FG","p-value"]) > .05) { return "There is no evidence of selection associated with the chosen phenotype." } else { if (_.get (result_json,[gene_id,"tests","BG","p-value"]) > 0.05) { if (_.get (result_json,[gene_id,"tests","DIFF","p-value"]) > .05) { return "There is evidence of selection associated with the chosen phenotype, but no statistically significant difference between the test and background selective regimes." } else { return "There is strong evidence of selection is associated with the chosen phenotype." } } else { if (_.get (result_json,[gene_id,"tests","DIFF","p-value"]) > .05) { return "selection is occurring with or without the chosen phenotype, but with similar selective regimes." } else { return "selection is occurring with or without the chosen phenotype, but with different selective regimes." } } } }37:T59f,//figure from busted-ph simulations (just looks like grass) /* canvas = { const h = average_pos.length; if (h) { const w = 300; const R = 2; const context = DOM.context2d(w*R*2, 2*h*R, 1); for (let i = 0; i < h; i++) { let from = 0.; let to = average_pos[i].p1_t; context.fillStyle = omega_color (average_pos[i].w1_t); context.fillRect( from*w*R, i*2*R, R*(to-from)*w, R ); from = to; to += average_pos[i].p2_t; context.fillStyle = omega_color (average_pos[i].w2_t); context.fillRect( from*w*R, i*2*R, R*(to-from)*w, R ); from = to; to += average_pos[i].p3_t; context.fillStyle = omega_color (average_pos[i].w3_t); context.fillRect( from*w*R, i*2*R, R*(to-from)*w, R ); from = 0.; to = average_pos[i].p1_b; context.fillStyle = omega_color (average_pos[i].w1_b); context.fillRect( from*w*R, i*2*R + R, R*(to-from)*w, R ); from = to; to += average_pos[i].p2_b; context.fillStyle = omega_color (average_pos[i].w2_b); context.fillRect( from*w*R, i*2*R + R , R*(to-from)*w, R ); from = to; to += average_pos[i].p3_b; context.fillStyle = omega_color (average_pos[i].w3_b); context.fillRect( from*w*R, i*2*R + R , R*(to-from)*w, R ); } return context.canvas; } return DOM.context2d(1,1,1).canvas; } */38:[] 2f:{"title":"BUSTED-PH results visualizer","cells":[{"id":0,"value":"md`# BUSTED-PH results visualizer`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3176,"value":"viewof busted_json = params.get (\"url\") ? localFileInput({accept: \".json\", value : params.get (\"url\"), disable: true}) : localFileInput({accept: \".json\"}) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":2156,"value":"md`### Summary of BUSTED-PH analyses\nBUSTED-PH results are available for **${_.size (_.filter (result_json, (d)=>\"fits\" in d))}** alignments. Of those, **${busted_significant.length}** yielded at least one sigfinicant result (p≤0.05) in BUSTED-PH test. Of those \n\n* **${_.filter (table_data, (d)=>d[\"FG p-value\"]<=0.05).length}** had evidence of selection on **Foreground**\n* **${_.filter (table_data, (d)=>d[\"BG p-value\"]<=0.05).length}** had evidence of selection on **Background**\n* **${_.filter (table_data, (d)=>d[\"DIFF p-value\"]<=0.05).length}** had evidence of differences between **Foreground** and **Background**\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2809,"value":"table1_title = md`**Table 1** BUSTED-PH results on individual alignments`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2817,"value":"table1 = Inputs.table(averys_table)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2838,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2912,"value":"fig1_label = md`**Figure 1**. Disributions of mean ω by group`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2920,"value":"fig1=vegalite ({\n \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.json\",\n \"data\": {\n \"values\": _.filter (mean_dnds_plot, (r)=>r.omega < 2)\n },\n \"width\": 800,\n \"height\": 50,\n \"transform\":[{\n \"density\": \"omega\",\n \"groupby\": [\"clade\"],\n \"bandwidth\": 0.025\n }],\n \"mark\": {type: \"area\", \"opacity\" : 0.5, tooltip : true, line : true},\n \"encoding\": {\n \"x\": {\n \"field\": \"value\",\n \"grid\" : null,\n \"title\": \"Density\",\n \"type\": \"quantitative\",\n \"axis\": {\"grid\": false}\n },\n \"y\": {\n \"field\": \"density\",\n \"type\": \"quantitative\",\n \"title\" : \"\",\n \"axis\": {\"grid\": false}\n },\n \"color\" : {\"value\" : \"grey\"},\n \"row\" : {\"field\" : \"clade\", \"title\" : \"dN/dS\", \"sort\": \"ascending\"}\n }\n})\n\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2943,"value":"table2_label = md`**Table 2**. Disributions of mean ω by group`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2946,"value":"table2 = table (mean_dnds_groups,{\n limit: 5000,\n enableCSVDownload: true,\n sort: true,\n enableFilter: false,\n columns: [\n {\n key : 'group',\n name: \"Group\"\n }, \n {\n key : 'mean',\n name: \"Mean\",\n render: (val)=>float_format (val)\n }, \n {\n key : 'median',\n name: \"Median\",\n render: (val)=>float_format (val)\n }, \n {\n key : 'Q1',\n name: \"5%\",\n render: (val)=>float_format (val)\n },\n {\n key : 'Q3',\n name: \"95%\",\n render: (val)=>float_format (val)\n }\n ]\n})\n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":2992,"value":"md`### Summary of BUSTED[S] analyses\nBUSTED[S] results are available for **${_.size (_.filter (result_json, (d)=>\"BUSTED\" in d))}** alignments. Of those, **${busted_significant.length}** yeilded a sigfinicant result (p≤0.05) for epidosic selection in at least one group.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2233,"value":"md`### Gene-by-gene information`","pinned":false,"hidden":false,"mode":"ojs"},{"id":966,"value":"viewof gene_id = {\n const dd3 = autoSelect ({\n title: \"Gene ID\",\n options: _.keys (result_json)\n \n });\n dd3.input.style.fontSize = \"12px\";\n dd3.input.style.marginTop = \"8px\";\n return dd3;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2643,"value":"md`#### Standard RELAX test`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2636,"value":"md`\n**${gene_id}** has \n${_.get (result_json,[gene_id,\"sequences\"])} sequences and ${_.get (result_json,[gene_id,\"sites\"])} sites.\n\n_Test summary._\n\n1. The p-value for selection of **foreground** branches **${float_format(_.get (result_json,[gene_id,\"tests\",\"FG\",\"p-value\"]))}**.\n2. The p-value for selection of **background** branches **${float_format(_.get (result_json,[gene_id,\"tests\",\"BG\",\"p-value\"]))}**.\n3. The p-value for differences in selection between the two sets of branches **${float_format(_.get (result_json,[gene_id,\"tests\",\"DIFF\",\"p-value\"]))}**.\n\n\n_Interpretation:_\n\n **${gene_id}** shows ${decision_tree}\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2137,"value":"fig2_label = md`**Figure 2**. Rate distributions for this gene using the BUSTED-PH model`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2134,"value":"fig2 = plotPie (_.flatten (_.map (_.get (result_json, [gene_id,\"distributions\",\"unconstrained\",\"Rate Distributions\"]), (d,k)=> {\n return _.map (d, (r)=>{\n let rp = r;\n rp.type = k;\n return rp;});\n \n})) || [], gene_id)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2662,"value":"fig3_label = md`**Figure 3**. Rate distributions for this gene using the BUSTED-PH model, where background and foreground share the same distribution`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2665,"value":"fig3 = plotPie (_.flatten (_.map (_.get (result_json, [gene_id,\"distributions\",\"shared\",\"Rate Distributions\"]), (d,k)=> {\n return _.map (d, (r)=>{\n let rp = r;\n rp.type = k;\n return rp;});\n \n})) || [], gene_id)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2236,"value":"md`### Tree for ${gene_id}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1941,"value":"trendLegend = swatches({\n color: species_colors,\n title: \"Viral species\",\n //tickFormat: \"%\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":870,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3500,"value":"### Correction for multiple testing","pinned":false,"hidden":false,"mode":"md"},{"id":3877,"value":"viewof chosen_LRT = select ({\n \"options\" : [\"Foreground specific\", \"Background specific\", \"Tree-wide\"],\n \"title\" : \"Choose LRT:\"\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":3871,"value":"viewof cutoffs = Inputs.form([\n Inputs.text( {value : 0.05, label: \"p-value\", validate : validateValue}),\n Inputs.text( {value : 0.20, label: \"q-value\", validate : validateValue})\n])","pinned":false,"hidden":false,"mode":"ojs"},{"id":3873,"value":"validateValue = (t)=> {\n console.log (t.value);\n let tt = parseFloat (t.value);\n if (tt >= 0 && tt <= 1) return true;\n return false;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3906,"value":"rejected = {\n return _.map (['No correction', 'FDR', 'Bonferroni', 'Holm-Bonferroni'], (d)=>{\n let cnt = _.filter (pv_data, (c)=>c['p-value'] <= c[d]).length;\n return {\n 'Procedure ' : d,\n 'Significant results' : cnt,\n 'Fraction of significant results' : cnt/pv_data.length\n \n }\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3909,"value":"viewof table3 = Inputs.table (rejected, {\n rows : 15\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3913,"value":"vegalite ({\n \"data\": {\"values\": pv_data},\n \"width\" : 800,\n \"height\" : 350,\n \"layer\" :\n _.map (colors.domain(), (d) => {\n return {\n mark : {\"type\" : \"point\", \"filled\" : false, \"size\" : 1, \"color\" : colors (d)},\n \"encoding\": {\n \"x\": {\"field\": \"index\", \"type\": \"quantitative\", \"axis\" : {\"grid\" : false, \"title\" : \"Test index\", labelFontSize : 14}},\n \"y\": {\"field\": d, \"type\": \"quantitative\", \"axis\" : {\"grid\" : false, \"title\" : \"Value\", labelFontSize : 14, tickCount : 5}, \"scale\" : {\"type\" : \"pow\", \"exponent\" : 0.2}},\n }\n }\n })\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3916,"value":"colors = d3.scaleOrdinal([\"p-value\", \"No correction\", \"FDR\", \"Bonferroni\", \"Holm-Bonferroni\"], d3.schemeCategory10)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3886,"value":"pvalue_data = {\n var pvalue_column = []\n if (chosen_LRT == \"Foreground specific\"){\n pvalue_column = table_data.map(person => ({ \"p-value\" : person[\"FG p-value\"]}))\n }\n else if(chosen_LRT == \"Background specific\"){\n pvalue_column = table_data.map(person => ({ \"p-value\" : person[\"BG p-value\"]}))\n }\n else if(chosen_LRT == \"Tree-wide\"){\n pvalue_column = table_data.map(person => ({ \"p-value\" : person[\"DIFF p-value\"]}))\n}\n //var result = table_data.map(person => ({ \"p-value\" : person[\"FG p-value\"]}));\n //return result \n return pvalue_column\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":3835,"value":"pv_data = {\n let rd = _.sortBy (_.map (_.filter (pvalue_data, (d)=>_.isNumber (_.get (d, \"p-value\"))), (d)=>_.mapValues (_.pick (d, \"p-value\"), (d)=>Math.max (0, d))), (d)=>d[\"p-value\"]);\n _.each (rd, (d,i)=> {\n d.index = i;\n d[\"No correction\"] = +cutoffs[0];\n d[\"p-value\"] = d[\"p-value\"];\n d[\"FDR\"] = (i+1)/rd.length * (+cutoffs[1]); // (rank+1)/(# p values) * (q-value)\n d[\"Bonferroni\"] = +cutoffs[0] / rd.length; // (p value threshold)/(# p values)\n d[\"Holm-Bonferroni\"] = +cutoffs[0] / (rd.length - i + 1); // \n });\n return rd;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2701,"value":"md`### Libraries and data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"d3 = require (\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"phylotree=require (\"https://dl.dropboxusercontent.com/s/i528j2lqnyzknit/phylotree.js\")\n//require (\"phylotree/build/phylotree.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"_=require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":78,"value":"//resolve(\"phylotree@0.1/phylotree.css\")\n//require (\"https://dl.dropbox.com/s/mjhii51tjy1o5gt/phylotree.js\")\nhtml`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1905,"value":"tree_object = [new phylotree.phylotree(_.get (result_json, [gene_id,\"tree\"]) || \"()\"), _.get (result_json, [gene_id,\"tested\",0]) || [], _.get (result_json, [gene_id,\"lengths\"])]","pinned":false,"hidden":false,"mode":"ojs"},{"id":127,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":874,"value":"import { table } from \"@gampleman/table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":174,"value":"import {select,checkbox, autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":488,"value":"vegalite = require(\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2288,"value":"float_format = d3.format (\".3g\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":2217,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":2321,"value":"report_ratio = (a,b)=> {\n if (a == 0 && b == 0) return 'N.D.';\n if (a == 0) return 'Infinite';\n return float_format (b/a);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1929,"value":"species = new Set(_.flatten(_.map (has_relax, (f)=>_.map (_.uniqBy (_.toPairs (f[\"tested\"][0]), (d)=>d[1]), (d)=>d[1]))))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3030,"value":"has_relax = _.chain (result_json).toPairs().filter ((d)=>\"tested\" in d[1]).fromPairs().value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1937,"value":"species_colors = d3.scaleOrdinal(d3.schemeCategory10).domain ([...species]);","pinned":false,"hidden":false,"mode":"ojs"},{"id":2785,"value":"table_data = _.map (_.toPairs (result_json), (d)=> {\n let res = {\n \"ID\" : d[0] \n };\n \n let pfg = (d)=>d.tests.FG[\"p-value\"];\n let pbg = (d)=>d.tests.BG[\"p-value\"];\n let pdiff = (d)=>d.tests.DIFF[\"p-value\"];\n let haz = (d)=>pfg(d)<=0.05 && pbg (d)>0.05 && pdiff (d)<=0.05;\n let sites = (d)=>[d.fg_selected, d.bg_selected];\n let l = (d)=>_.mapValues(_.groupBy (_.toPairs(d.lengths), (i)=>d.tested[0][i[0]]), (ll)=>d3.sum (ll, (i)=>i[1]));\n \n let inject = [\n [\"Sequences\", [\"sequences\"]],\n [\"Sites\", [\"sites\"]],\n [\"FG p-value\", pfg],\n [\"BG p-value\", pbg],\n [\"DIFF p-value\", pdiff],\n [\"SelSites\", sites],\n [\"PH\", haz],\n [\"L\", l]\n \n ];\n \n _.each (inject, (i)=> {\n res[i[0]] = _.isFunction (i[1]) ? i[1](d[1], d[0]) :_.get (d[1], i[1]);\n });\n \n return res;\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":2998,"value":"busted_significant = _.filter (_.toPairs (result_json), (f)=>f[1].tests && d3.min (_.map(f[1].tests, (test)=>test[\"p-value\"])) <= 0.05)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2475,"value":"baseline_by_segment = _.chain (result_json).toPairs().filter ((d)=>\"fits\" in d[1]).map ((d)=> {\n d[1]=\"Reference\";\n return d;\n}).fromPairs().value();","pinned":false,"hidden":false,"mode":"ojs"},{"id":2952,"value":"mean_dnds_groups = _.chain (_.groupBy (mean_dnds_plot, (d)=>d.clade)).map ((d,k)=>{\n return {\n group : k,\n mean : d3.mean (d, (e)=>e.omega),\n median : d3.median (d, (e)=>e.omega),\n Q1 : d3.quantile (d, 0.05, (e)=>e.omega),\n Q3 : d3.quantile (d, 0.95, (e)=>e.omega)\n };\n}).value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2904,"value":"mean_dnds_plot = _.filter (_.flatten (_.map (mean_dnds, (d, f)=> {\n return _.map (d, (dnds, gr)=> { \n return {'clade' : gr, \"omega\" : dnds, \"gene\" : f};\n });\n})), (r)=>_.isNumber (r.omega) && r.clade != \"Unclassified\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2865,"value":"mean_dnds = {\n return _.mapValues (result_json, (d,f)=> {\n if (\"fits\" in d == false) return; \n let test_groups = new Set (_.values (d.tested[0]));\n let mapper = {\"Unclassified\" : \"Unclassified\"};\n if (test_groups.has (\"Test\")) {\n _.some (d.tested[0], (g,s) => {\n if (g == \"Test\") {\n mapper[\"Test\"] = s.split (\"_\")[0]; \n } else {\n mapper[\"Reference\"] = s.split (\"_\")[0]; \n }\n return _.size (mapper) == 3;\n }\n );\n } else {\n mapper = _.fromPairs ( _.map ([...test_groups], (d)=>[d,d]));\n }\n return _.fromPairs ([['mapping', mapper]].concat(_.map ([...test_groups], (g)=> {\n return ([mapper[g],d.fits[\"MG94xREV with separate rates for branch sets\"][\"Rate Distributions\"][\"non-synonymous/synonymous rate ratio for *\" + g + \"*\"][0][0]]);\n })));\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":754,"value":"//result_json = await d3.json (\"https://data.hyphy.org/web/busted-ph/\"+ phenotype +\"_ags.json\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1943,"value":"import { legend, swatches } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2060,"value":"import {translation_table} from \"@spond/usual-vs-unusual-mutation-analyses\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3276,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":3496,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":3759,"value":"reduced_pos = {\n let reduced_list = pos_sel.filter(word => word.w3_t < 1000000000)\n return reduced_list\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3317,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":3607,"value":"\\","pinned":false,"hidden":false,"mode":"md"},{"id":3672,"value":"sorted = {\n const list = pos_sel\n list.sort((a, b) => (a.w3_t > b.w3_t) ? 1 : -1)\n return list\n \n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3733,"value":"reduced_busted = {\n let reduced_list = averys_table.filter(word => word.w3_t < 1000000000)\n return reduced_list\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3682,"value":"/*\n average_pos = {\n let keys = Object.keys(sorted[0])\n keys = keys.filter(key => key != 'ID' && key != 'Selection associated with phenotype');\n const len_bins = sorted.length/10 // len_bins = 31\n let dummy_list = [] // to store 10 lines at a time\n let bin_list = [] // to store average of dummy_list\n for (let i = 0; i < 10; i++){\n dummy_list = sorted.slice(i*len_bins, (i + 1) * len_bins + 1)\n bin_list[i] = {}\n for (let name of keys){\n const average_val = dummy_list.reduce((total, next) => total + next[name], 0) / dummy_list.length\n bin_list[i][name] = average_val\n }\n }\n return bin_list\n}\n*/","pinned":false,"hidden":false,"mode":"ojs"},{"id":3719,"value":"//dotplot = Plot.dot(reduced_busted, {x: \"w3_t\", y: \"w1_t\", stroke: \"Selection associated with phenotype\"}).plot()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3756,"value":"//dotplot2 = Plot.dot(reduced_pos, {x: \"w3_t\", y: \"w1_t\", stroke: \"Selection associated with phenotype\"}).plot()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3773,"value":"//dotplot3 = Plot.dot(reduced_busted, {x: \"w3_t\", y: \"FG p-value\", stroke: \"Selection associated with phenotype\"}).plot()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3656,"value":"/*hist = {\n let w3_t_pos = []\n for (let i = 0; i < pos_sel.length; i++){\n w3_t_pos.push(pos_sel[i][\"w3_t\"])\n }\n var histGenerator = d3.bin()\n .domain([Math.min(...w3_t_pos),Math.max(...w3_t_pos)]) \n .thresholds(10); \n var bins = histGenerator(w3_t_pos);\n return bins\n}\n*/","pinned":true,"hidden":false,"mode":"ojs"},{"id":3569,"value":"//lenend for figure from BUSTED-PH simulations (but the figure just looks like grass here)\n/*\nLegend(omega_color, {\n title: \"dN/dS\",\n tickFormat: \".2\"\n})\n\n*/","pinned":false,"hidden":false,"mode":"ojs"},{"id":3514,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":3551,"value":"//For BUSTED-PH simulation figure\nomega_color = d3.scaleDiverging([10, 1, 0], d3.interpolatePiYG)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3571,"value":"//For BUSTED-PH simulation figure\nimport {Legend, Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3466,"value":" codeml_missed = {\n let missed = []\n for (const gene in result_json){\n if(result_json[gene][\"tests\"][\"FG\"][\"p-value\"] < 0.05 && result_json[gene][\"tests\"][\"BG\"][\"p-value\"] < 0.05 && result_json[gene][\"tests\"][\"DIFF\"][\"p-value\"] > 0.05){\n missed.push(gene)\n }\n }\n return missed\n} \n","pinned":false,"hidden":false,"mode":"ojs"},{"id":3810,"value":"result_json = await get_json (params.get (\"url\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3798,"value":"import {localFileInput} from \"@mbostock/localfile\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3802,"value":"params = new URLSearchParams(location.search)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3806,"value":"get_json = async (json_source)=> {\n if (json_source) {\n return d3.json (json_source);\n }\n return busted_json.json();\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}