29:null 2a:["$","title",null,{"children":"Ray Finned Fish Data analysis using BUSTEDS+PH | Alexander Lucaci | Observable"}] 2f:T492,** Table 1. ** Column definitions are as follows: 1. Gene corresponds to the candidate gene under study, 2. Scenario corresponds to the experimental design (see the dropdown menu below this text for details), 3. Seqs corresponds to the number of sequences in the alignment, 4. Site corresponds to the number of codons in the alignment 5. Num_FG corresponds to the number of foreground (i.e. Test) species and ancestral nodes in the alignment, 6. FG_pval corresponds to the LRT p-value for the foreground (test) species in the BUSTEDS+PH method, 7. BG_pval corresponds to the LRT p-value for the background species in the BUSTEDS+PH method, 8. DF_pval corresponds to the LRT p-value for the difference between the two selective regimes (BUSTEDS+PH method, in this scenario the null hypothesis is that one omega rate distribution applies over the entire tree, the alternative hypothesis is that separate omega distributions apply for the Foreground and Background branches), and finally 9. Class corresponds to the significance of p values (FG_pval, BG_pval, and DF_pval respectively) where 1 indicates significance and 0 indicates non-significance at a threshold of 0.05.30:T8ed,displayed_tree = { let tree = tree_object[0]; var t = tree.render("#tree_container", { //height:1024, width:800, '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; }31:T49f,table_data = { let raw_summary = Object.values(summary) //let no_trees = _.filter(raw_summary, (d)=>"Gene" in d) /*let fg_omegas = _.map(raw_summary, d => d["BISTEDS+PH_FG_OMEGAS"]) let bg_omegas = _.map(raw_summary, d => d["BUSTEDS+PH_BG_OMEGAS"]) let df_omegas = _.map(raw_summary, d => d["BUSTEDS+PH_DF_OMEGAS"]) */ let for_table = [] for (let i = 0; i < raw_summary.length; i ++){ for_table[i] = { "Gene" : raw_summary[i]["Gene"], "Scenario" : raw_summary[i]["BUSTED+PH_SCENARIO"], "Seqs" : raw_summary[i]["Seqs"], "Site" : raw_summary[i]["Sites"], "Num_FG" : raw_summary[i]["BUSTEDS+PH_NumFG"], "FG_pval" : raw_summary[i]["BUSTEDS+PH_pvalue"], "BG_pval" : raw_summary[i]["BUSTEDS+PH_pvalue_BG"], "DF_pval" : raw_summary[i]["BUSTEDS+PH_pvalue_shared"], "Class" : "C" + (raw_summary[i]["BUSTEDS+PH_pvalue"] <= 0.05 ? 1 : 0) + (raw_summary[i]["BUSTEDS+PH_pvalue_BG"] > 0.05 ? 0 : 1) + (raw_summary[i]["BUSTEDS+PH_pvalue_shared"] <= 0.05 ? 1: 0) //"FG_omegas" :fg_omegas[i]["omega"], //"BG_omegas" :bg_omegas[i]["omega"], //"DF_omegas" :df_omegas[i]["omega"], } } return for_table }32:T569,interpretation = { let inter = "" if (gene_entry['BUSTEDS+PH_pvalue'] > 0.05){ inter = "There is no evidence of " + gene_entry.Gene + " experiencing positive selection associated with scenario " + String(selected_num) + "." } else { if (gene_entry['BUSTEDS+PH_pvalue_BG'] > 0.05) { if (gene_entry['BUSTEDS+PH_pvalue_shared'] > 0.05) { inter = "There is evidence of " + gene_entry.Gene + " experiencing positive selection associated with scenario " + String(selected_num) + " but there is no statistically significant difference between selection on the test (foreground) and background branches." } else { inter = "There is strong evidence of " + gene_entry.Gene + " experiencing positive selection associated with scenario " + String(selected_num) + "." } } else { inter = "For the gene " + gene_entry.Gene + ", selection is acting on the test (foreground) branches, but selection is also acting on background branches. " if (gene_entry['BUSTEDS+PH_pvalue_shared'] > 0.05) { inter = inter + "There is no statistically significant difference between the test and background branches in terms of selective pressure." } else { inter = inter + "There is a statistically significant difference between the test and background branches in terms of selective pressure." } } } return inter }33:[{"id":"9947de0d44237342a008ac9be0e05597831c6e4a890b720a824c856f5e4740d2116392d2ac96cf860aebb40a7041642eb8924e84c688638cbb42d902c45323f2","href":"https://static.observableusercontent.com/files/9947de0d44237342a008ac9be0e05597831c6e4a890b720a824c856f5e4740d2116392d2ac96cf860aebb40a7041642eb8924e84c688638cbb42d902c45323f2","name":"summary_all_scenarios@5.json","type":"application/json","lastModified":1696253081242.896,"size":2645208}] 2d:{"id":"ebfee7e44d801dc3","slug":null,"title":"Ray Finned Fish Data analysis using BUSTEDS+PH","thumbnail":"831595528f996266ba151eeb873c870e36b69af5a8a0cef35d5ac41742106135","access_level":"public","create_time":"$D2023-10-02T13:24:41.242Z","update_time":"$D2023-10-02T13:26:44.398Z","view_time":null,"first_public_time":"$D2023-10-02T13:24:41.242Z","fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"0022ea5182eedc15","login":"aglucaci","name":"Alexander Lucaci","avatar_url":"https://avatars-next.observableusercontent.com/31da2eec8e58cf73fded4c73b820d7f6c0e73dc86ee1ef2bf16742992286ad77","tier":"starter_2024"},"version":707,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"f610d52ff7f59ec1","login":"aglucaci","name":"Alexander Lucaci","avatar_url":"https://avatars-next.observableusercontent.com/31da2eec8e58cf73fded4c73b820d7f6c0e73dc86ee1ef2bf16742992286ad77","tier":null}],"members":[{"type":"individual","id":"f610d52ff7f59ec1","login":"aglucaci","name":"Alexander Lucaci","avatar_url":"https://avatars-next.observableusercontent.com/31da2eec8e58cf73fded4c73b820d7f6c0e73dc86ee1ef2bf16742992286ad77","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Ray Finned Fish Data analysis using BUSTEDS+PH","cells":[{"id":0,"value":"# Ray Finned Fish Data analysis using BUSTEDS+PH","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":249,"value":"Table of all data, unfiltered:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":255,"value":"viewof search = Inputs.search(table_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":262,"value":"table1 = Inputs.table(search, {\n rows : 15 \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":314,"value":"$2f","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":16,"value":"viewof selected = Inputs.select(Scenarios.map(d => d.Description), {label: \"Show this scenario:\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":229,"value":"## For the scenario: ${selected}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":237,"value":"We have seen abnormally large branch lengths in all scenarios. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":360,"value":"Plot.plot({\n x: {type: \"log\", domain: [domain_len[0], Math.ceil(domain_len[1])], tickFormat: \",\"}, grid: true,\n color: {\n type: \"ordinal\",\n scheme: \"category10\",\n legend: \"ramp\"\n },\n marks: [\n Plot.dot(longest_len, {y: \"Gene\", x: \"Max_length\", stroke: \"Distribution\", fill: \"Distribution\"})\n //Plot.barX(longest_len, {y: \"Gene\", x: \"Max_length\", fill: \"Distribution\"}),\n //Plot.ruleX([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":313,"value":"** Figure 1. **\n Longest branch length in each gene tree for the scenario: ${selected}. Colors indicate which distribution the longest branch came from. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":458,"value":"-------------------------------------------------------------------------\n## Exploring individual genes within the selected scenario: ${selected}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":72,"value":"viewof gene_id = Inputs.select(_.map(filtered_summary, d => d.Gene).sort(), {label: \"Show this gene:\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":416,"value":"** For gene ${gene_id} in scenario: ${selected} **","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":170,"value":"trendLegend = swatches({\n color: species_colors\n //tickFormat: \"%\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":7,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":627,"value":"** Figure 2. **\nGene tree for ${gene_id} generated by BUSTED-PH. Colors indicate foreground, background, and nuisance branches (if using) respectively. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":639,"value":"-------------------------------------------------------------------------\n\n## Test for phenotype-specific positive selection in gene ${gene_id} for selected scenario: ${selected}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":423,"value":"The p-value for the test for positive selection in the *test* (foreground) branches is: ** ${gene_entry['BUSTEDS+PH_pvalue']} **\n\nThe p-value for the test for positive selection in the *background* branches is: ** ${gene_entry['BUSTEDS+PH_pvalue_BG']} **\n\nAnd the p-value for the test for the *difference* between the test (foreground) and background branch sets is: ** ${gene_entry['BUSTEDS+PH_pvalue_shared']} **","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":446,"value":"** ${interpretation} **","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":454,"value":"Omega values: ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":540,"value":"omega_plot.legend(\"color\", {\n label: \"Omega values →\",\n tickSize: 3,\n ticks: 10,\n //tickFormat: r.tickFormat,\n //round: r.round,\n width: 600,\n height: 45\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":482,"value":"omega_plot = Plot.plot({\n color: {\n type: \"sqrt\",\n //pivot: 4,\n scheme: \"brbg\",\n reverse: true,\n //legend: true\n }, \n y: {\n domain: ['FG', 'BG', 'ALL']\n },\n marks: [\n Plot.barX(stacked_omegas, {y: \"dist\",x: \"prop\", fill: omega_vals}),\n Plot.ruleX([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":608,"value":"** Figure 3. **\nOmega distribution for gene ${gene_id}. Sites were categorized into three omega values for each of the three distributions. The three distributions include: foreground branches, background branches, and all branches in both FG and BG. Prop indicates proportion of sites that fall into the omega category. The largest omega value category for each of the three distributions are: ${omega_vals[2].toFixed(3)} for the FG, ${omega_vals[5].toFixed(3)} for the BG, and ${omega_vals[8].toFixed(3)} for all branches. \n\n-------------------------------------------------------------------------","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":103,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":212,"value":"## Data/imports for tree image (Ignore below this line):","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":94,"value":"tree_object = [new phylotree.phylotree(gene_entry.TREE || \"()\"), gene_entry.BUSTEDPH_FG_BG_NODES || [], gene_entry.BUSTEDPH_BRANCH_L]\n\n//tree_object = [new phylotree.phylotree(gene_entry.TREE || \"()\"), gene_entry.BUSTEDPH_FG_BG_NODES || [], bl_1]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":11,"value":"phylotree=require (\"https://dl.dropboxusercontent.com/s/i528j2lqnyzknit/phylotree.js\")\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":203,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":172,"value":"species_colors = d3.scaleOrdinal(d3.schemeCategory10).domain ([...species]);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":145,"value":"d3 = require('d3')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":174,"value":"species = {\n let nodes = _.map(filtered_summary, d => d.BUSTEDPH_FG_BG_NODES)\n let vals = []\n for (let i = 0; i < nodes.length; i ++) {\n for (let j in nodes[i]){\n vals.push(nodes[i][j])\n }\n }\n return [...new Set(vals)]\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":198,"value":"import { Legend, swatches } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":219,"value":"Parsing data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":467,"value":"stacked_omegas = {\n let dist = [\"BUSTEDS+PH_FG_OMEGAS\", \"BUSTEDS+PH_BG_OMEGAS\", 'BUSTEDS+PH_DF_OMEGAS']\n let dist_short = ['FG', 'BG', 'ALL']\n let stacked = []\n for (let i = 0; i x.Gene === gene_id)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":19,"value":"Scenarios = { const scenario = [{Scenario: 1, Description: \"All endothermic fish vs. all ectothermic fish (scenario 1)\"},\n {Scenario: 2, Description: \"Regional endothermy vs. rest of the tree (scenario 2)\"},\n {Scenario: 3, Description: \"Eye/brain endothermy vs. rest of the tree (scenario 3)\"},\n {Scenario: 4, Description: \"Full body endothermy vs. rest of the tree (scenario 4)\"}] \n return scenario\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":56,"value":"selected_num = _.find(Scenarios, x=> x.Description === selected).Scenario","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":156,"value":"summary = FileAttachment(\"summary_all_scenarios@5.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":518,"value":"omega_vals =_.map(stacked_omegas, d => d.omega)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":327,"value":"filtered_len = _.map(filtered_summary, d => d.BUSTEDPH_BRANCH_L)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":336,"value":"longest_len = {\n let max_length = []\n let vals = []\n for (let i = 0; i < filtered_len.length; i ++) {\n let vals = []\n for (let j in filtered_len[i]){\n vals.push(filtered_len[i][j])\n }\n let gene_max = Math.max(...vals)\n let species = Object.keys(filtered_len[i]).find(key => filtered_len[i][key] === gene_max)\n max_length[i] = { Gene : filtered_summary[i]['Gene'], \n Max_length : gene_max,\n Species : species,\n Distribution : filtered_summary[i]['BUSTEDPH_FG_BG_NODES'][species]\n }\n }\n return max_length\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":381,"value":"domain_len = [Math.min(...longest_len.map(o => o.Max_length)), Math.max(...longest_len.map(o => o.Max_length))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":252,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":39,"value":"filtered_summary = _.filter(summary, d => d[\"BUSTED+PH_SCENARIO\"] === selected_num)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":429,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":223,"value":"Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":74,"value":"import {select,checkbox, autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":490,"value":"import {StackedBarChart} from \"@d3/stacked-horizontal-bar-chart\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 2e:"$2d:body"