30:Ta7d,md`# Carnivorous plants: model comparison for BUSTEDS-MH and BUSTEDS. These files represent MT (Mitochondrial) and PT (Plastome, Chloroplast) data from carnivorous plants with 98 genes in total. Overall, the complete results includes: * BUSTEDS-MH analysis with ${d3.count(data.filter(d => d.Model === "BUSTEDS-MH" && d.Dataset === "Mitochondrial") , d => d.w1)} genes of Mitochondrial origin * BUSTEDS analysis with ${d3.count(data.filter(d => d.Model === "BUSTEDS" && d.Dataset === "Mitochondrial") , d => d.w1)} genes of Mitochondrial origin * BUSTEDS-MH analysis with ${d3.count(data.filter(d => d.Model === "BUSTEDS" && d.Dataset === "Chloroplast") , d => d.w1)} genes of Chloroplast origin * BUSTEDS analysis with ${d3.count(data.filter(d => d.Model === "BUSTEDS" && d.Dataset === "Chloroplast") , d => d.w1)} genes of Chloroplast origin Of the genes under investigation, ${d3.count(data.filter(d => d.delta_cAIC > 5 && d.Model === "BUSTEDS-MH") , d => d.w1)} (${d3.format(".1f")(d3.count(data.filter(d => d.delta_cAIC > 5 && d.Model === "BUSTEDS-MH"), d => d.w1) / 98 * 100)}%) datasets prefer BUSTEDS-MH over BUSTEDS (by cAIC). When we split datasets by Mitochondrial origin, we observe the following results for genes which have statistically significant (LRT p<=0.05) evidence for gene-wide episodic selection, breaking down into: * ${d3.count(data.filter(d => d.pvalue <= 0.05 && d.Dataset === "Mitochondrial" && d.Model === "BUSTEDS"), d => d.w1)} BUSTEDS analyses having evidence of gene-wide episodic selection * ${d3.count(data.filter(d => d.pvalue <= 0.05 && d.Dataset === "Mitochondrial" && d.Model === "BUSTEDS-MH"), d => d.w1)} BUSTEDS-MH analyses having evidence of gene-wide episodic selection. And, that ${d3.count(data.filter(d => d.delta_cAIC > 5 && d.Dataset === "Mitochondrial" && d.Model === "BUSTEDS-MH"), d => d.w1)} Mitochondrial genes prefer BUSTEDS-MH over BUSTEDS (by cAIC). When we split datasets by Chloroplast origin, we observe the following results for genes which have statistically significant (LRT p<=0.05) evidence for gene-wide episodic selection, breaking down into: * ${d3.count(data.filter(d => d.pvalue <= 0.05 && d.Dataset === "Chloroplast" && d.Model === "BUSTEDS"), d => d.w1)} BUSTEDS analyses having evidence of gene-wide episodic selection * ${d3.count(data.filter(d => d.pvalue <= 0.05 && d.Dataset === "Chloroplast" && d.Model === "BUSTEDS-MH"), d => d.w1)} BUSTEDS-MH analyses having evidence of gene-wide episodic selection. And, that ${d3.count(data.filter(d => d.delta_cAIC > 5 && d.Dataset === "Chloroplast" && d.Model === "BUSTEDS-MH"), d => d.w1)} Chloroplast genes prefer BUSTEDS-MH over BUSTEDS (by cAIC). `31:[] 2e:{"title":"Carnivorous plants: model comparison for BUSTEDS-MH and BUSTEDS.","cells":[{"id":0,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":413,"value":"## Table 1. All results\n\nSearch for a specific gene (by Filename)","pinned":false,"hidden":false,"mode":"md"},{"id":548,"value":"viewof search = Inputs.search(data, {\n datalist: [\"Filename\"], \n placeholder: \"Search Filenames\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":990,"value":"Inputs.table(search, {columns: [\"Filename\", \"Model\", \"Dataset\", \"N\", \"Num_Codons\", \n \"w1\", \"p1\", \"w2\", \"p2\", \"w3\", \"p3\",\n \"SRV1\", \"SRV_p1\", \"SRV2\", \"SRV_p2\", \"SRV3\", \"SRV_p3\",\n \"cAIC\", \"LRT\", \"pvalue\",\n \"DH\", \"TH\", \"TH_SI\"]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":967,"value":"## Table 2. Additional search criteria on results\nAdditional search criteria, implemented:\n* By which model was used for the analysis (\"BUSTEDS-MH\" or \"BUSTEDS\")\n* By Dataset (\"Chloroplast\" or \"Mitochondrial\")\n* Evidence for gene-wide selection at the LRT pvalue <= 0.05 level\n* By which model is preferred, by delta cAIC","pinned":false,"hidden":false,"mode":"md"},{"id":973,"value":"viewof filter1 = Radio([\"BUSTEDS-MH\", \"BUSTEDS\", \"Both\"], { label: \"Model choice\", value: \"Both\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":976,"value":"viewof filter2 = Radio([\"Mitochondrial\", \"Chloroplast\", \"All\"], { label: \"Dataset type\", value: \"All\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":979,"value":"viewof filter3 = Radio([\"Yes\", \"No\", \"All\"], { label: \"Evidence of Gene-wide selection?\", value: \"All\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":983,"value":"viewof filter4 = Radio([\"Yes\", \"No\", \"All\"], { label: \"Model Preference by delta cAIC\", value: \"All\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":904,"value":"Inputs.table(filter_switch, {columns: [\"Filename\", \"Model\", \"Dataset\", \"N\", \"Num_Codons\", \n \"w1\", \"p1\", \"w2\", \"p2\", \"w3\", \"p3\",\n \"SRV1\", \"SRV_p1\", \"SRV2\", \"SRV_p2\", \"SRV3\", \"SRV_p3\",\n \"cAIC\", \"LRT\", \"pvalue\",\n \"DH\", \"TH\", \"TH_SI\"]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1242,"value":"The filtering returned ${d3.count(filter_switch, d => d.w1)} items","pinned":false,"hidden":false,"mode":"md"},{"id":1079,"value":"## Figure 1. Demonstrating various parameter values by filename (gene).","pinned":false,"hidden":false,"mode":"md"},{"id":1088,"value":"viewof w3p3 = {\n const temperatures = vl.markBar({interpolate: \"basis\", color: \"darkseagreen\", opacity: .8})\n .data(filter_switch)\n .encode(\n vl.x().fieldQ(\"w3\").stack(\"null\"),\n vl.y().fieldN(\"Filename\"), \n vl.tooltip().fieldN(\"Filename\"), \n vl.color().fieldN(\"Model\")\n )\n .width(400)\n .height(d3.count(filter_switch, d => d.w1) * 7);\n \n const species = vl.markBar({opacity: .8})\n .data(filter_switch)\n .encode(\n vl.x().fieldQ(\"p3\").stack(\"null\").scale({domain: [0, 1]}), \n vl.y().fieldN(\"Filename\"), \n vl.tooltip().fieldN(\"Filename\"), \n vl.color().fieldN(\"Model\")\n )\n .width(400)\n .height(d3.count(filter_switch, d => d.w1) * 7);\n return vl.vconcat(vl.hconcat(temperatures, species)).render();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1105,"value":"viewof SRV3P3 = {\n const temperatures = vl.markBar({ interpolate: \"basis\", color: \"darkseagreen\", opacity: .8})\n .data(filter_switch)\n .encode(\n vl.x().fieldQ(\"SRV3\").stack(\"null\"), // For x, use the Horsepower field\n vl.y().fieldN(\"Filename\"), // For y, use the Miles_per_Gallon field\n vl.tooltip().fieldN(\"Filename\"), // For tooltips, show the Name field\n vl.color().fieldN(\"Model\")\n )\n .width(400)\n .height(d3.count(filter_switch, d => d.w1) * 5);\n \n const species = vl.markBar({opacity: .8})\n .data(filter_switch)\n .encode(\n vl.x().fieldQ(\"SRV_p3\").stack(\"null\"), // For x, use the Horsepower field\n vl.y().fieldN(\"Filename\"), // For y, use the Miles_per_Gallon field\n vl.tooltip().fieldN(\"Filename\"), // For tooltips, show the Name field\n vl.color().fieldN(\"Model\")\n )\n .width(400)\n .height(d3.count(filter_switch, d => d.w1) * 5);\n return vl.vconcat(vl.hconcat(temperatures, species)).render();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1032,"value":"vl.markBar() // Make a scatter chart\n .data(filter_switch) // Using the cars data (below)\n .encode(\n vl.x().fieldQ(\"DH\"), // For x, use the Horsepower field\n vl.y().fieldN(\"Filename\"), // For y, use the Miles_per_Gallon field\n vl.tooltip().fieldN(\"Filename\"), // For tooltips, show the Name field\n vl.color().fieldN(\"Model\")\n ).width(400).height(d3.count(filter_switch, d => d.w1) * 5)\n .render() // Draw the chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":1034,"value":"vl.markBar() // Make a scatter chart\n .data(filter_switch) // Using the cars data (below)\n .encode(\n vl.x().fieldQ(\"TH\"), // For x, use the Horsepower field\n vl.y().fieldN(\"Filename\"), // For y, use the Miles_per_Gallon field\n vl.tooltip().fieldN(\"Filename\"), // For tooltips, show the Name field\n vl.color().fieldN(\"Model\"),\n \n ).width(400).height(d3.count(filter_switch, d => d.w1) * 5)\n .render() // Draw the chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":1036,"value":"vl.markBar() // Make a scatter chart\n .data(filter_switch) // Using the cars data (below)\n .encode(\n vl.x().fieldQ(\"TH_SI\"), // For x, use the Horsepower field\n vl.y().fieldN(\"Filename\"), // For y, use the Miles_per_Gallon field\n vl.tooltip().fieldN(\"Filename\"), // For tooltips, show the Name field\n vl.color().fieldN(\"Model\")\n ).width(400).height(d3.count(filter_switch, d => d.w1) * 5)\n .render() // Draw the chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":1293,"value":"vl.markBar() // Make a scatter chart\n .data(filter_switch) // Using the cars data (below)\n .encode(\n vl.x().fieldQ(\"cAIC\"), // For x, use the Horsepower field\n vl.y().fieldN(\"Filename\"), // For y, use the Miles_per_Gallon field\n vl.tooltip().fieldN(\"Filename\"), // For tooltips, show the Name field\n vl.color().fieldN(\"Model\")\n ).width(400).height(d3.count(filter_switch, d => d.w1) * 5)\n .render() // Draw the chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":372,"value":"## Imports","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"data = d3.csv(\"https://raw.githubusercontent.com/aglucaci/BUSTEDS-MH/Plants/data/PLANTS_MODEL_COMPARISON.csv\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"d3 = require(\"d3@7\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":212,"value":"import {\n line,\n bar,\n area,\n histogram,\n range\n} from \"@hamzaamjad/tiny-charts\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":240,"value":"style = await html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":360,"value":"function sparkbar(max) {\n return x => htl.html`

${x.toLocaleString(\"en\")}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":465,"value":"import { SummaryTable } from \"@observablehq/summary-table\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":830,"value":"import {filters} from \"@gampleman/crossfilter-input\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":863,"value":"import { table } from '@gnestor/table'","pinned":true,"hidden":false,"mode":"ojs"},{"id":897,"value":"import {Radio} from \"@observablehq/inputs\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":1016,"value":"vega = require(\"vega-embed@5\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1018,"value":"import {vl} from \"@vega/vega-lite-api-v5\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":1357,"value":"import {rangeSlider} from '@mootari/range-slider@1312'","pinned":true,"hidden":false,"mode":"ojs"},{"id":581,"value":"## Declares","pinned":true,"hidden":false,"mode":"md"},{"id":585,"value":"selectedBUSTEDS = data.filter(d => d.Model === \"BUSTEDS\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":590,"value":"selectedMito = data.filter(d => d.Dataset === \"Mitochondrial\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":781,"value":"selectedMito_by_cAIC = selectedMito.filter(d => d.delta_cAIC > 5)","pinned":false,"hidden":false,"mode":"ojs"},{"id":799,"value":"selectedMito_by_pvalue = selectedMito.filter(d => d.pvalue <= 0.05)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1151,"value":"selectedMito_BUSTEDS_MH_by_pvalue = data.filter(d => d.pvalue <= 0.05 && d.Dataset === \"Mitochondrial\" && d.Model === \"BUSTEDS-MH\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1160,"value":"selectedChloro_BUSTEDS_MH_by_pvalue = data.filter(d => d.pvalue <= 0.05 && d.Dataset === \"Chloroplast\" && d.Model === \"BUSTEDS-MH\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":592,"value":"selectedChloro = data.filter(d => d.Dataset === \"Chloroplast\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":795,"value":"selectedChloro_by_cAIC = selectedChloro.filter(d => d.delta_cAIC > 5)","pinned":true,"hidden":false,"mode":"ojs"},{"id":801,"value":"selectedChloro_by_pvalue = selectedChloro.filter(d => d.pvalue <= 0.05)","pinned":true,"hidden":false,"mode":"ojs"},{"id":626,"value":"selected_BUSTEDS_MH_pvalue = selectedBUSTEDSMH.filter(d => d.pvalue <= 0.05)","pinned":true,"hidden":false,"mode":"ojs"},{"id":586,"value":"selectedBUSTEDSMH = data.filter(d => d.Model === \"BUSTEDS-MH\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":629,"value":"selected_BUSTEDS_pvalue = selectedBUSTEDS.filter(d => d.pvalue <= 0.05)","pinned":true,"hidden":false,"mode":"ojs"},{"id":647,"value":"selected_BUSTEDS_delta_cAIC = selectedBUSTEDS.filter(d => d.delta_cAIC > 5)","pinned":true,"hidden":false,"mode":"ojs"},{"id":649,"value":"selected_BUSTEDS_MH_delta_cAIC = selectedBUSTEDSMH.filter(d => d.delta_cAIC > 5)","pinned":true,"hidden":false,"mode":"ojs"},{"id":746,"value":"count_Datasets = d3.count(selectedMito, d => d.w1) / 2 + (d3.count(selectedChloro, d => d.w1) / 2)","pinned":true,"hidden":false,"mode":"ojs"},{"id":816,"value":"selectedMito_BUSTEDSMH = selectedMito.filter(d => d.Model === \"BUSTEDS-MH\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":819,"value":"selectedMito_BUSTEDSMH_cAIC = selectedMito_BUSTEDSMH.filter(d => d.delta_cAIC > 5)","pinned":true,"hidden":false,"mode":"ojs"},{"id":823,"value":"selectedChloro_BUSTEDSMH = selectedChloro.filter(d => d.Model === \"BUSTEDS-MH\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":827,"value":"selectedChloro_BUSTEDSMH_cAIC = selectedChloro_BUSTEDSMH.filter(d => d.delta_cAIC > 5)","pinned":true,"hidden":false,"mode":"ojs"},{"id":997,"value":"## Data filters","pinned":false,"hidden":false,"mode":"md"},{"id":1000,"value":"data_filter1 = search.filter(search => search.Model == filter1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1003,"value":"data_filter2 = data_filter1.filter(data_filter1 => data_filter1.Dataset == filter2)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1006,"value":"data_filter3 = data_filter2.filter(data_filter2 => data_filter2.pvalue <= 0.05)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1010,"value":"data_filter4 = data_filter3.filter(data_filter3 => data_filter3.delta_cAIC > 5)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1211,"value":"filter_switch = {\n let result;\n if (filter1 === \"Both\") {\n result = search;\n } else {\n result = search.filter(search => search.Model == filter1);\n }\n\n if (filter2 != \"All\") {\n result = result.filter(result => result.Dataset == filter2);\n }\n\n if (filter3 === \"Yes\") {\n result = result.filter(result => result.pvalue <= 0.05);\n } else if (filter3 === \"No\") {\n result = result.filter(result => result.pvalue >= 0.05)\n }\n\n if (filter4 === \"Yes\") {\n result = result.filter(result => result.delta_cAIC > 5);\n } else if (filter4 === \"No\") {\n result = result.filter(result => result.delta_cAIC < 5)\n }\n \n // Finally\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}