31:Tfc8,network_chart = (trends)=> vegalite({ "transform": [ {"calculate": "if(datum.SecondLargestCluster > 0,datum.LargestCluster/datum.SecondLargestCluster,1)", "as": "CR"}, {"calculate": "datum.Edges/datum.Nodes", "as": "Degree"}, ], data: {values: trends}, "hconcat" : [ { //"title" : "Threshold effect", "width": 200 , "height": 200, "encoding": { "x": { "field": "Threshold", "type": "quantitative", "axis": {"title": "Genetic Distance Threshold", titleFontSize: 20, labelFontSize: 14, format : "p",tickCount : 20,grid : false}, }}, "layer": [ { "mark": { "type": "line", "color" : "#3c368e","strokeWidth" : 4 }, "encoding": { "y": { "type": "quantitative", "field": "Clusters", "axis": {"title": "Number of Clusters", titleFontSize: 18, labelFontSize: 14, grid : false, "titleColor" : "#3c368e"}, } } }, { "mark": { "type": "line", "color" : "#fcb71e", "strokeWidth" : 4 }, "encoding": { "y": { "type": "quantitative", "field": "CR", "scale" : { "type" : "log" }, "axis": {"title": "R\u2081\u2082", titleFontSize: 18, labelFontSize: 14, titleColor : "#fcb71e", grid : false}, } } } , { "mark": { "type": "line", "strokeWidth" : 2, "opacity" : 0.5, "color" : "black", "interpolate" : "step-before", }, "encoding": { "y": { "type": "quantitative", "field": "Score", "axis": null, } } }, ], "resolve": {"scale": {"y": "independent"}} }, { "width": 300 , "height": 300, "encoding": { "x": { "field": "Threshold", "type": "quantitative", "axis": {"title": "Genetic Distance Threshold", titleFontSize: 18, labelFontSize: 14, format : "p", tickCount : 20,grid : false}, }}, "layer": [ { "mark": { "type": "line", "color" : "#3c368e" }, "encoding": { "y": { "type": "quantitative", "field": "Nodes", "axis": {"title": "Clustered Individuals", titleFontSize: 18, labelFontSize: 14, grid : false, titleColor : "#3c368e"}, } } }, { "mark": { "type": "line", "color" : "#fcb71e" }, "encoding": { "y": { "type": "quantitative", "field": "Degree", "scale" : {type : "log"}, "axis": {"title": "Mean degree", titleFontSize: 18, labelFontSize: 14,titleColor : "#fcb71e", grid : false}, } } }, { "mark": { "type": "line", "strokeWidth" : 2, "opacity" : 0.5, "color" : "black", "interpolate" : "step-before", }, "encoding": { "y": { "type": "quantitative", "field": "Score", "axis": null, } } }, ], "resolve": {"scale": {"y": "independent"}} } ] })32:[] 2f:{"title":"Network properties as a function of distance threshold","cells":[{"id":0,"value":"md`# Network properties as a function of distance threshold`","pinned":false,"hidden":false,"mode":"ojs"},{"id":457,"value":"md `Please run [hivnetworkcsv](https://github.com/veg/hivclustering/tree/develop) on the TN93 distance file using the following options.\n\n\nhivnetworkcsv -i yourtn93file.csv -A 0 > report.tsv\n\n\nor \n\n\nhivnetworkcsv -i yourtn93file.csv -A small_step > report.tsv\n\n\nwhere **small_step** is something like 0.0001 or 0.0005, i.e. the increment in which to step through the genetic distance cutoff.\n\nMake sure that when you compute TN93 distances, the threshold you select is greater than the likely optimal value; 3% should be enough for most cases, but if the threshold diagnostic fails, try increasing it.\n\n---`","pinned":false,"hidden":false,"mode":"ojs"},{"id":477,"value":"md`Load the report file file`","pinned":false,"hidden":false,"mode":"ojs"},{"id":465,"value":"viewof profile = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":584,"value":"The best score of ${best_score.Score} is achieved at **D**=${best_score.Threshold}. R₁₂ = ${fmt(best_score.LargestCluster/best_score.SecondLargestCluster)}, F_max = ${fmt (best_score.Clusters/max_clusters.Clusters)}. There are ${best_score.Nodes} nodes and ${best_score.Clusters} clusters in the network.","pinned":false,"hidden":false,"mode":"md"},{"id":618,"value":"viewof comp = Inputs.text ({\"label\" : \"Compare\", \"value\" : 0.015, submit : true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":630,"value":"md`>Reference score of ${ref_score.Score} is achieved at **D**=${ref_score.Threshold}. R₁₂ = ${fmt(ref_score.LargestCluster/ref_score.SecondLargestCluster)}, F_max = ${fmt (ref_score.Clusters/max_clusters.Clusters)}. There are ${ref_score.Nodes} nodes and ${ref_score.Clusters} clusters in the network.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":390,"value":"md`**Figure 1.** Network properties as a function of the distance thershold from **${profile.name}**. The **black** line on the both panels shows the relative weight of each distance threshold; higher values are preferred.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":513,"value":"network_chart (network_profile)","pinned":false,"hidden":false,"mode":"ojs"},{"id":610,"value":"viewof thresholds = Inputs.table (network_profile, {format : {\n \"Threshold\" : (d)=>d.toFixed (7)\n}})","pinned":false,"hidden":false,"mode":"ojs"},{"id":527,"value":"md`--- \n#### Supporing code from here on out`","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"best_score = _.maxBy (network_profile, (d)=>d.Score)","pinned":false,"hidden":false,"mode":"ojs"},{"id":626,"value":"ref_score = _.minBy (network_profile, (d)=>Math.abs (d.Threshold - comp))","pinned":false,"hidden":false,"mode":"ojs"},{"id":604,"value":"max_clusters = _.maxBy (network_profile, (d)=>d.Clusters)","pinned":false,"hidden":false,"mode":"ojs"},{"id":600,"value":"fmt = d3.format (\"4g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":487,"value":"network_profile = await (d3.tsvParse (await (Files.text(profile)),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"d3=require('d3')","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"_=require (\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":151,"value":"vegalite = require(\"vega-embed@3\")","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}