31:T197a,md`### The transmission network The inferred transmission network contained ${network_summary.cluster_statistics.total} clusters ranging in size from ${d3.min (network_summary.cluster_sizes)} to ${comma_number_format(d3.max (network_summary.cluster_sizes))} sequences including ${comma_number_format(network_summary.cluster_statistics.NCC)} clusters containing just ${kp_tag} sequences and ${comma_number_format(network_summary.cluster_statistics.NCC_LANL)} clusters containing both ${kp_tag} and LANL sequences. The ${comma_number_format(network_summary.cluster_statistics.NCC)} ${kp_tag}-only clusters comprised ${value_and_fraction_int(network_summary.sequences.NCC_NCC, sequence_count['NCC'], kp_tag + ' sequences', 'of all ' + kp_tag +' sequences')}. The ${comma_number_format(network_summary.cluster_statistics.NCC_LANL)} ${kp_tag}-LANL clusters included ${value_and_fraction_int(network_summary.sequences.NCC_LANL, sequence_count['NCC'], kp_tag + ' sequences', 'of all ' + kp_tag +' sequences')} and ${value_and_fraction_int(network_summary.sequences.LANL, sequence_count['LANL'], 'LANL sequences', 'of all LANL database sequences')}. Overall ${value_and_fraction_int(network_summary.sequences.NCC_LANL+network_summary.sequences.NCC_NCC, sequence_count['NCC'], ' of ' + comma_number_format (sequence_count['NCC']) + ' NCC sequences')} were in a sequence cluster, and of those, ${value_and_fraction_int(REPORT_link_summary.ncc_linked, network_summary.sequences.NCC_LANL+network_summary.sequences.NCC_NCC)} were directly linked to other ${kp_tag} sequences. The majority (${text_float_format(d3.bisectLeft (network_summary.cluster_sizes, 10)/network_summary.cluster_statistics.total)}) of clusters contained 2 to 10 sequences, with ${network_summary.cluster_statistics.total-d3.bisectLeft (network_summary.cluster_sizes, 10)} large clusters containing 11 or more sequences. Of the ${_.size (_.filter (REPORT_cluster_summary.cluster_descriptions, c=>c[2] >= 11 && c[4] + c[5]))} of the largest clusters with both Condesa and LANL sequences, ${_.size (_.filter (REPORT_cluster_summary.cluster_descriptions, c=> c[1] != 'B' && c[2] >= 11 && c[4] + c[5]))} contained non-B viruses (Figure 1A), and most clusters also contained primarily LANL sequences, with only a few Condesa sequences per cluster (Figure 1B, median Condesa proportion ${text_float_format (d3.median (_.map (_.filter (REPORT_cluster_summary.cluster_descriptions, (c) => c[2] >= 11 && c[4] + c[5]), (c) => (c[3] / c[2])).sort()))}). The distribution of TN93 distance for the edges connecting ${kp_tag} sequences to one another (${distance_descriptor(ncc_links)}) had a significantly lower median than distribution of TN93 distances for the edges connecting ${kp_tag} and LANL sequences (${distance_descriptor(ncc_lanl_links)}; p<0.001 two-tailed Wilcoxon Rank Sum Test). The relationship between the TN93 distance and the difference in sample years between linked ${kp_tag} and LANL sequences is depicted in Figure 2. The median difference in sample years between linked ${kp_tag} and LANL sequences was ${year_reporter(ncc_lanl_links, (c) => c <= 0.005)} for sequence pairs with a genetic distance up to 0.5%, ${year_reporter(ncc_lanl_links, (c) => c < 0.01 && c > 0.005)} for sequence pairs with a genetic distance between 0.5% and 1.0%, and ${year_reporter(ncc_lanl_links, (c) => c > 0.01)} for sequence pairs with a genetic distance greater 1.0% (Figure 2). Of the ${comma_number_format(network_summary.sequences.NCC_LANL)} sequences in the ${comma_number_format(network_summary.cluster_statistics.NCC_LANL)} ${kp_tag}-LANL clusters, ${value_and_fraction_int(REPORT_link_summary.total, network_summary.sequences.NCC_LANL)} were directly linked to a LANL sequence, whereas ${value_and_fraction_int(network_summary.sequences.NCC_LANL-REPORT_link_summary.total, network_summary.sequences.NCC_LANL)} were directly linked only to other ${kp_tag} sequences. Of the ${REPORT_link_summary.total} ${kp_tag} sequences directly linked to an LANL sequence, ${REPORT_link_summary.us_only} (${text_float_format(REPORT_link_summary.us_only/REPORT_link_summary.total)}) linked solely to one or more Mexico sequences, ${REPORT_link_summary.non_us_only} (${text_float_format(REPORT_link_summary.non_us_only/REPORT_link_summary.total)}) linked solely to one or more non-Mexico sequences, ${REPORT_link_summary.both} (${text_float_format(REPORT_link_summary.both/REPORT_link_summary.total)}) linked to both Mexico and non-Mexico sequences. Overall, ${REPORT_link_summary.non_us_only + REPORT_link_summary.both} (${text_float_format((REPORT_link_summary.non_us_only + REPORT_link_summary.both)/REPORT_link_summary.total)}) ${kp_tag} sequences were directly connected to one or more non-Mexico sequences via ${REPORT_link_summary.total_links_outside_us} links. Of these, ${text_float_format(REPORT_link_summary.binned_links['US']/REPORT_link_summary.total_links_outside_us)} were to the U.S., ${text_float_format(REPORT_link_summary.binned_links['Europe']/REPORT_link_summary.total_links_outside_us)} were to Europe, ${text_float_format(REPORT_link_summary.binned_links['Asia']/REPORT_link_summary.total_links_outside_us)} to Asia, ${text_float_format(REPORT_link_summary.binned_links['Canada']/REPORT_link_summary.total_links_outside_us)} to Canada, ${text_float_format(REPORT_link_summary.binned_links['Americas']/REPORT_link_summary.total_links_outside_us)} to Latin America. The intricate web of interconnectivity between the NCC and LANL sequences is illustrated in Figure 1. We further categorized ${comma_number_format(network_summary.cluster_statistics.three_or_more)} clusters with three or more sequences into broad geographic classes: ${value_and_fraction_int(REPORT_cluster_summary.cluster_by_type.Local, network_summary.cluster_statistics.three_or_more)} **local** clusters (over 60% of cluster members were from NCC), ${value_and_fraction_int(REPORT_cluster_summary.cluster_by_type.National, network_summary.cluster_statistics.three_or_more)} **national** clusters (over 50% of cluster members were from the US, but not from NCC), and ${value_and_fraction_int(REPORT_cluster_summary.cluster_by_type.International, network_summary.cluster_statistics.three_or_more)} **international** (over 50% of cluster members were from outside the US). ${value_and_fraction_int(REPORT_cluster_summary.cluster_by_type[""], network_summary.cluster_statistics.three_or_more)} of clusters with three or more sequences did not belong to either of the three classes. `32:Tf5e,md` ` 33:T6c9,{ var data = []; _.each ([[x => {return x[1] == 'B' || !x[1]}, 'Subtype B'], [x => {return x[1] && x[1] != 'B'}, "Other subtypes"]], (desc, i) => { var x = []; var y = []; _.each ( _.groupBy (REPORT_cluster_summary.cluster_descriptions, "2"), (v,idx) => { x.push (idx); y.push (v); }); y = _.map (y, r => {return _.filter (r, desc[0]).length;}); data.push( { x: x, y: y, type: 'bar', mode: 'markers', marker : { color : i ? '#5a3dff' : '#ffab3d', line: { color: 'rgb(50,50,50)', width: 0.5 } }, //text: y, //textposition: i ? 'inside' : 'outside', textfont: { size: '8', color : i ? 'white' : 'black' }, name : desc[1], } ); }); var layout = { showlegend: true, xaxis: { tickangle: -90, type: "category", autorange : true, //title: "Cluster size", titlefont: { size : 24 }, tickfont: { size : 14 } }, yaxis: { gridwidth: 0, //type: "log", nticks : 3, tickmode : "auto", title: "Number of clusters", titlefont: { size : 24 }, tickfont: { size : 14 } }, legend: { x: 0.6, y: 0.8, xanchor: 'left', yanchor: 'top', borderwidth : 0.5, font: { size : 24 } }, bargap :0.2, barmode: 'stack', width : 800, height: 800 }; const div = DOM.element('div'); plotly.newPlot(div, data, layout); return div; }34:T6fa,{ var data = []; _.each ([[x => {return x[3]}, kp_tag], [x => {return x[4] + x[5]}, "LANL"]], (desc, i) => { var x = []; var y = []; _.each ( _.groupBy (REPORT_cluster_summary.cluster_descriptions, "2"), (v,idx) => { x.push (idx); y.push (v); }); y = _.map (_.map (y, r => {return _.map (r, desc[0]);}), (c) => d3.sum (c)); data.push( { x: x, y: y, type: 'bar', mode: 'markers', marker : { color : i ? '#cccccc' : '#333333', line: { color: 'rgb(50,50,50)', width: 0.5 } }, //text: y, //textposition: i ? 'inside' : 'outside', textfont: { size: '8', color : i ? 'white' : 'black' }, name : desc[1], } ); }); var layout = { /*title: 'B', font:{ family: 'Arial', size : 48 },*/ showlegend: true, xaxis: { tickangle: -90, type: "category", autorange : true, title: "Cluster size", titlefont: { size : 24 }, tickfont: { size : 14 } }, yaxis: { gridwidth: 0, //type: "log", nticks : 3, tickmode : "auto", title: "Number of sequences", titlefont: { size : 24 }, tickfont: { size : 14 } }, legend: { x: 0.4, y: 0.8, xanchor: 'right', yanchor: 'top', borderwidth : 0.5, font: { size : 24 } }, bargap :0.2, barmode: 'stack', width : 800, height: 800 }; const div = DOM.element('div'); plotly.newPlot(div, data, layout); return div; }35:T5a5,country_info = { var country_data = await d3.csv ("https://dl.dropbox.com/s/jwmtfdae9dg7mcg/countries.csv"); var country_map = {}; _.each (country_data, (l) => {country_map[l["alpha-2"]] = l;}); country_map['missing'] = ['Unknown']; var callout = {'US' : 'US', 'CA' : 'Canada', 'MX' : 'Mexico', 'missing': 'Unknown', '-' : 'Unknown'}; country_map['-'] = ['Unknown']; _.each (['RU','UA','AM','AZ','BY','EE','KZ','KG','LV','LT','MD','TJ','TM','UZ','GE'], (v) => { callout[v] = "Former USSR"; }); var secondary_binning = {}; _.each (['Western Europe','Northern Europe','Southern Europe','Eastern Europe'], (k) => { secondary_binning[k] = 'Europe'; }); _.each (['Eastern Asia', 'South-eastern Asia'], (k) => { secondary_binning[k] = 'Asia'; }); return { "mapping" : country_map, "callout" : callout, "secondary_binning" : secondary_binning, "binning" : {null : true, 'Mexico' : true, 'US' : true, 'Asia' : true, 'Canada' : true,'Europe' : true}, "map_code" : (code) => { //console.log (code); if (code in callout) { code = callout[code]; } else { code = country_map[code]['region']; } if (code in secondary_binning) { code = secondary_binning[code]; } return code; } }; }36:T9ad,hexbins = (points) => { // data => array of [x,y] coordinates var svg_dim = [600,500]; var svg = d3.select(DOM.svg(svg_dim[0], svg_dim[1])), margin = {top: 20, right: 20, bottom: 50, left: 80}, width = svg_dim[0] - margin.left - margin.right, height = svg_dim[1] - margin.top - margin.bottom, g = svg.append("g").attr("transform", "translate(" + margin.left + "," + margin.top + ")"); svg.attr ("width", svg_dim[0]).attr ("height", svg_dim[1]); var color = d3.scaleSequential(d3.interpolateLab("white", "red")) .domain([0, 50]); var x_domain = d3.extent (points, r => {return r[0];}); var y_domain = d3.extent (points, r => {return r[1];}); var bin_radius = 12; var hexbin = d3_hexbin.hexbin() .radius(bin_radius) .size(width, height); var x = d3.scaleLinear() .domain(x_domain) .range([0, width]).nice(); var y = d3.scaleLinear() .domain(y_domain) .range([height, 0]).nice(); g.append("clipPath") .attr("id", "clip-rectangle") .append("rect") .attr("width", width) .attr("height", height); g.append("g") .attr("class", "hexagon") .style("clip-path", "url(#clip-rectangle)") .style("-webkit-clip-path", "url(#clip-rectangle)") .selectAll("path") .data(hexbin(points.map (d => {return [x(d[0]), y(d[1])];}))) .enter().append("path") .attr("d", hexbin.hexagon()) //.attr("d", function (d) {return hexbin.hexagon(radius(d.length));}) .attr("transform", function(d) { return "translate(" + (d.x+bin_radius) + "," + (d.y-bin_radius) + ")"; }) .attr("fill", function(d) { return color(d.length); }); g.append("g") .attr("class", "axis axis--y legend-text") .call(d3.axisLeft(y).tickSizeOuter(-width).tickFormat (d3.format(".2~p"))); g.append("g") .attr("class", "axis axis--x legend-text") .attr("transform", "translate(0," + height + ")") .call(d3.axisBottom(x).tickSizeOuter(-height)); g.append ("text").text ("Time difference, years").attr ("class", "legend-text").attr ("x", width).attr ("y", height).attr ("dy", "2.5em").attr ("text-anchor", "end"); g.append ("text").text ("TN93 distance").attr ("class", "legend-text").attr ("x", 0).attr ("y", 0).attr ("dy", "-3.5em").attr ("text-anchor", "end").attr ("transform", "rotate (-90)"); /*var corr = new spearman_rho (points.map (r => {return r[0];}), points.map (r => {return r[1];})); corr.calc().then (value => {console.log (value);}); */ return svg.node(); }37:T1392,generate_network_summary = (filter) => { var connections_by_node = {}; var countries_by_node = {}; var unique_countries = {}; var detailed_countries_by_ncc_node = {}; var detailed_subtypes_by_ncc_node = {}; var has_link_to_ncc = {}; var by_cluster = {}; var lanl_by_country = {}; _.each (network_json["Nodes"], (n, i) => { if (!filter (n)) return; connections_by_node[i] = {}; var cluster_id = n["cluster"]; if (!(cluster_id in by_cluster)) { by_cluster[cluster_id] = {}; } var source = get_node_attribute (n, "Source"); var id = n["id"]; if (! (source in by_cluster[cluster_id])) { by_cluster[cluster_id][source] = []; } by_cluster[cluster_id][source].push (i); connections_by_node[i]['source'] = source; if (source != kp_tag) { var node_country = get_node_attribute (n, "Country"); unique_countries[node_country] = true; node_country = country_info.map_code (node_country); if (!(node_country in lanl_by_country)) { lanl_by_country[node_country] = 1; } else { lanl_by_country[node_country] += 1; } if (!(node_country in country_info.binning)) { node_country = "Other/Unknown"; } n["patient_attributes"]["tag"] = node_country; } else { detailed_countries_by_ncc_node [id] = {}; detailed_subtypes_by_ncc_node [id] = {}; has_link_to_ncc[id] = false; } }); _.each (network_json["Edges"], (e, i) => { if (filter (network_json["Nodes"][e["source"]])) { var ends = [e["source"], e["target"]]; var tags = _.map (ends, (idx) => { return get_node_attribute_index (idx, "tag"); }); var sources = _.map (ends, (idx) => { return get_node_attribute_index (idx, "Source"); }); _.each (ends, (n,i) => { //_.each (sources, (s) => { connections_by_node[n][sources[1-i]] = true; //}); if (! (n in countries_by_node)) { countries_by_node [n] = {}; } countries_by_node[n] [tags[1-i]] = true; if (! (tags[i])) { // NCC node if (tags[1-i]) { // linked to a non-NCC node var country_description = get_node_attribute_index (ends [1-i], "Country"); var db_node_id = network_json["Nodes"][n]["id"]; if (country_description in detailed_countries_by_ncc_node[db_node_id]) { detailed_countries_by_ncc_node[db_node_id][country_description] += 1; } else { detailed_countries_by_ncc_node[db_node_id][country_description] = 1; } var subtype = get_node_attribute_index (ends [1-i], "Subtype"); if (subtype in detailed_subtypes_by_ncc_node[db_node_id]) { detailed_subtypes_by_ncc_node[db_node_id][subtype] += 1; } else { detailed_subtypes_by_ncc_node[db_node_id][subtype] = 1; } } else { has_link_to_ncc[network_json["Nodes"][n]["id"]] = true; } } }); }}); var cluster_count = _.size (by_cluster); var ncc_only_clusters = _.size (_.filter (by_cluster, (c) => {return ! (lanl_tag in c);})); var ncc_sequences_in_ncc_clusters = d3.sum (_.map (_.filter (by_cluster, (c) => {return ! (lanl_tag in c);}), (c) => {return d3.sum (_.map (_.values (c), (s) => {return s.length}))})); var ncc_sequences_in_ncc_lanl_clusters = d3.sum (_.map (_.filter (by_cluster, (c) => {return (lanl_tag in c);}), (c) => {return c[kp_tag].length;})); var lanl_sequences = d3.sum (_.map (_.filter (by_cluster, (c) => {return (lanl_tag in c);}), (c) => {return c["LANL"].length;})); var sizes = _.map (by_cluster, (c) => {return _.reduce (c, (sum, v) => {return sum + v.length;},0)}).sort((a,b) => {return (+a) - (+b)}); return {"detailed_subtypes_by_ncc_node" : detailed_subtypes_by_ncc_node, "detailed_countries_by_ncc_node" : detailed_countries_by_ncc_node, "countries_by_node": countries_by_node, "by_cluster" : by_cluster, "connections_by_node" : connections_by_node, 'ncc_to_ncc' : has_link_to_ncc, "cluster_sizes" : sizes, "cluster_statistics" : { "total" : cluster_count, "three_or_more" : _.size (_.filter (sizes, (c) => c > 2)), "NCC" : ncc_only_clusters, "NCC_LANL" : cluster_count - ncc_only_clusters }, "sequences": { "NCC_NCC" : ncc_sequences_in_ncc_clusters, "NCC_LANL" : ncc_sequences_in_ncc_lanl_clusters, "LANL" : lanl_sequences } } }38:T519,distance_bars = (plot_data, plot_title) => { var data = []; var bins = 6; var by_year = _.map (new Array (bins), x => {return { y : [], boxpoints: 'suspectedoutliers', jitter: 0.3, pointpos: 0, type: 'box', boxmean : true, opacity : 0.5, notched : true, marker:{ color: 'rgb(50,50,50)', } }}); _.each (plot_data, datum => { by_year [Math.min (datum[0], bins-1)].y.push (datum[1]); if (datum[0] >= bins) { by_year[bins-1].name = "≥ " + (bins-1) + " years"; } else { by_year[datum[0]].name = "" + datum[0] + "-" + (datum[0] + 1) + " yrs"; } }); var layout = { title: plot_title, font:{ family: 'Arial', size : 24 }, showlegend: false, xaxis: { tickangle: 0, type: "category", autorange : true, title: "Difference between isolation years", titlefont: { size : 24 }, tickfont: { size : 18 } }, yaxis: { gridwidth: 0, nticks : 5, tickmode : "auto", title: "TN93 distance", titlefont: { size : 24 }, tickfont: { size : 18 } }, width : 1000, height: 600 }; const div = DOM.element('div'); plotly.newPlot(div, by_year, layout); return div; }39:T17ab,generate_cluster_summary = (network_summary_data) => { var node_types = {}; var by_clusters = {}; var unique_keys = {}; var subtype_by_cluster = {}; var countries_by_cluster = {}; _.each (network_summary_data.connections_by_node, (types_orig, node) => { var types = _.clone (types_orig); delete types['source']; var node_type = get_node_attribute_index (node, "Source"); if (_.size (types) == 2) { node_type += "|Dual"; } else { node_type += "|" + _.keys (types)[0]; } var cluster_id = network_json["Nodes"][node]["cluster"]; if (! (cluster_id in by_clusters)) { by_clusters[cluster_id] = {}; } _.each ([node_types, by_clusters[cluster_id] ], (dict_obj) => { if (! (node_type in dict_obj)) { dict_obj[node_type] = 0; } dict_obj[node_type] += 1; unique_keys [node_type] = 1; }); }); var cluster_descriptions = []; _.each (by_clusters, (info, cluster) => { var size = d3.sum (_.values (info)); if (size >= 2) { var ncc_sequences = d3.sum (_.map (unique_keys, (v, k) => { if (k in info && k.indexOf (kp_tag) == 0) { return info[k]; } return 0; })); var lanl_sequences = d3.sum (_.map (unique_keys, (v, k) => { if (k in info && k.indexOf (lanl_tag) == 0) { return info[k]; } return 0; })); if (lanl_sequences >= 0) { var lanl_lanl = "LANL|LANL" in info ? info["LANL|LANL"] : 0; cluster_descriptions.push ([cluster, null, size, ncc_sequences, lanl_sequences - lanl_lanl, lanl_lanl]); } } }); cluster_descriptions.sort ((a,b) => b[2] - a[2]); _.each (cluster_descriptions, (cd, cid) => { var years = _.filter (_.map (network_summary_data.by_cluster[cd[0]][kp_tag], (v, i) => { return parseInt( (get_node_attribute_index (v, "hiv_aids_dx_dt")||get_node_attribute_index (v, "Baseline")).substr (0,4)); }), (c) => c).sort(); var years_lanl = _.filter (_.map (network_summary_data.by_cluster[cd[0]][lanl_tag], (v, i) => { return parseInt(get_node_attribute_index (v, "hiv_aids_dx_dt").substr (0,4)); }), (c) => c).sort(); var countries = _.countBy (_.filter (_.map (network_summary_data.by_cluster[cd[0]][lanl_tag], (v, i) => { return get_node_attribute_index (v, "Country"); }), (c) => c)); countries_by_cluster [cd[0]] = _.clone (countries); var has_missing = "missing" in countries; var has_us = 0; if (home_country in countries) { has_us = countries[home_country]; } if (has_missing) { has_missing = countries["missing"]; delete countries["missing"]; } countries = _.map (countries, (c,v) => [v,c]).sort ( (a,b) => b[1] - a[1]); var subtypes = _.countBy (_.filter (_.map (network_summary_data.by_cluster[cd[0]][lanl_tag], (v, i) => { return get_node_attribute_index (v, "Subtype"); }), (c) => c)); _.mergeWith(subtypes, _.countBy (_.filter (_.map (network_summary_data.by_cluster[cd[0]][kp_tag], (v, i) => { return get_node_attribute_index (v, "Subtype"); }), (c) => c)), (a,b) => {if (a && b) {return a+b}}); if (_.size (subtypes) > 0) { cd [1] = _.maxBy (_.map (subtypes, (i,c) => [c,i]), (a) => a[1])[0]; subtype_by_cluster[cd[0]] = cd[1]; } cd.push (d3.median (years)); if (years_lanl.length > 0) { cd.push (d3.median (years_lanl)); } var cc = countries.length; if (cc > 6) { countries.splice (6, cc); cd.push (_.map (countries, (c) => { return country_info.mapping[c[0]].name + " (" + comma_number_format (c[1]) + ")"; }).join (", ") + ", and " + (cc-countries.length) + " other countries"); } else { cd.push (_.map (countries, (c) => { return country_info.mapping[c[0]].name + " (" + comma_number_format (c[1]) + ")"; }).join (", ")); } if (has_missing) { cd[cd.length-1] += ", and " + has_missing + " with no country data"; } var cluster_type = "2"; if (cd[2] > 2) { cluster_type = ""; if (cd[3]/cd[2] > 0.6) { cluster_type = "Local"; } else { if (has_us / cd[2] > 0.5) { cluster_type = "National"; } else { if ((cd[2] - cd[3] - has_us - has_missing) / cd[2] > 0.5) { cluster_type = "International"; } } } } cd.push (cluster_type); }); return { 'cluster_descriptions' : cluster_descriptions, 'unique_keys' : unique_keys, 'node_types' : node_types, 'by_clusters' : by_clusters, 'subtype_by_cluster' : subtype_by_cluster, 'countries_by_cluster' : countries_by_cluster, 'cluster_by_type' : _.countBy(cluster_descriptions, (c)=>c[c.length-1]) }; } 3a:Tdd3,report_linkages = (filter, network_summary) => { var lanl_linked = 0; var us_only = 0; var ncc_linked = _.filter (network_summary.connections_by_node, (c) => c['source']==kp_tag && c[kp_tag]).length; var non_us_only = 0; var missing_only = 0; var both = 0; var country_count = []; var link_binning = {}; _.each (network_summary.detailed_countries_by_ncc_node, (linked_countries, ncc_node) => { var country_list = _.keys ( linked_countries); if (country_list.length == 0 || country_list.length == 1 && undefined in country_list) { return; } try { delete linked_countries['missing']; } catch (e) { // no error if no 'missing' } try { delete network_summary.detailed_subtypes_by_ncc_node[ncc_node][undefined]; } catch (e) { } if (filter (network_summary.detailed_subtypes_by_ncc_node[ncc_node])) { lanl_linked += 1; _.each (linked_countries, (c, k) => { var node_country = country_info.map_code(k); if (node_country in link_binning) { link_binning[node_country] += c; } else { link_binning[node_country] = c; } }); if (country_list.length == 0) { missing_only += 1; } else { if (country_list.length == 1) { if (home_country in linked_countries) { us_only += 1; } else { non_us_only += 1; country_count.push (1); } } else { if (!(home_country in linked_countries)) { non_us_only += 1; } else { both += 1; } country_count.push (country_list.length); } } } }); country_count.sort (); var binning =[] _.each (country_info.binning, (v, k) => { if (k != "null") { binning.push (k); } else { binning.push (kp_tag); } }); binning.push ("Other/Unknown"); var undef_to_ncc = (n) => {return (n == "undefined" || !n) ? kp_tag : n}; var counts = _.map (binning, (v) => {return _.map (binning, (c) => {return 0});}); _.each (network_summary.countries_by_node, (v,n) => { try { var r = binning.indexOf (undef_to_ncc(get_node_attribute_index (n, "tag"))); _.each (v, (ignored, c) => { var r2 = binning.indexOf (undef_to_ncc(c)); counts[r][r2] += 1; }); } catch (e) { //console.log (e); } }); /* for n,v in countries_by_node.items(): try: r = binning.index(get_node_attribute (network_json, n, "tag")) for c in v: v = binning.index(c) counts[r][v] += 1 except: pass binning[0] = kp_tag */ delete link_binning[home_country]; var total_links = d3.sum (_.values(link_binning)); return {total : lanl_linked, ncc_linked : ncc_linked, us_only: us_only, non_us_only: non_us_only, both : both, missing_only : missing_only, non_us_country_count : country_count, total_links_outside_us : total_links, binned_links : link_binning, edge_associations_labels : binning, edge_associations_counts : counts}; } 3b:Tc73,chord_diagram = (chord_data, labels) => { var svg_dim = [1024,1024]; var svg = d3.select(DOM.svg(svg_dim[0], svg_dim[1])), outerRadius = Math.min(svg_dim[0], svg_dim[1]) * 0.5 - 100, innerRadius = outerRadius - 40; svg.attr ("width", svg_dim[0]).attr ("height", svg_dim[1]); var formatValue = d3.formatPrefix(",.0", 1e2); var chord = d3.chord() .padAngle(0.1) .sortSubgroups(d3.descending); var arc = d3.arc() .innerRadius(innerRadius) .outerRadius(outerRadius); var ribbon = d3.ribbon() .radius(innerRadius); var color = d3.scaleOrdinal(d3.schemeAccent); var g = svg.append("g") .attr("transform", "translate(" + svg_dim[0] / 2 + "," + svg_dim[1] / 2 + ")") .datum(chord(chord_data)); var group = g.append("g") .attr("class", "groups") .selectAll("g") .data(function(chords) { return chords.groups; }) .enter().append("g"); group.append("path") .style("fill", function(d) { return color(d.index); }) .style("stroke", function(d) { return d3.rgb(color(d.index)).darker(); }) .attr("d", arc); var groupTick = group.selectAll(".group-tick") .data(function(d) { return groupTicks(d, 500); }) .enter().append("g") .attr("class", "group-tick") .attr("transform", function(d) { return "rotate(" + (d.angle * 180 / Math.PI - 90) + ") translate(" + outerRadius + ",0)"; }); groupTick.append("line") .attr("x2", 6); groupTick .filter(function(d) { return d.value % 1e3 === 0; }) .append("text") .attr("x", 8) .attr("dy", ".35em") .attr("transform", function(d) { return d.angle > Math.PI ? "rotate(180) translate(-16)" : null; }) .style("text-anchor", function(d) { return d.angle > Math.PI ? "end" : null; }) .classed ("label-text", true) .text(function(d) { return formatValue(d.value); }); g.append("g") .attr("class", "ribbons") .selectAll("path") .data(function(chords) { return chords; }) .enter().append("path") .attr("d", ribbon) .style("fill", function(d) { return color(d.target.index); }) .style("stroke", function(d) { return d3.rgb(color(d.target.index)).darker(); }); // Returns an array of tick angles and values for a given group and step. function groupTicks(d, step) { var k = (d.endAngle - d.startAngle) / d.value; return d3.range(0, d.value, step).map(function(value) { return {value: value, angle: value * k + d.startAngle}; }); } if (labels) { var label_boxes_groups = svg.selectAll (".legend-text").data (labels.map ((l,i) => {return [[l,i]]})).enter ().append ("g").classed ("legend-text", true); //label_boxes = svg.selectAll (".legend-text"); var label_text = label_boxes_groups.selectAll ("text").data (d => {return d;}).enter().append ("text"). text ((d) => {return d[0];}).attr ("x",25).attr("y", (d) => {return 25 + d[1] * 20;}); var label_boxes = label_boxes_groups.selectAll ("rect").data (d => {return d;}).enter().append ("rect") .attr ("x", 5).attr ("y", d => {return 25 + d[1] * 20 - 14;}).attr ("width", 16).attr ("height",16).style ("fill", d=> {return color(d[1]);}).style("stroke", function(d) { return d3.rgb(color(d[1])).darker(); }); } return svg.node(); }3c:T418,edge_data = { var result = []; _.each (network_json ["Edges"], (e, i) => { var year1 = get_year_for_node (e.source); var year2 = get_year_for_node (e.target); var years; if (year1 && year2) { years = Math.abs (year1-year2); } else { years = null; } result.push ( { 'ID1' : network_json["Nodes"][e.source], 'ID2' : network_json["Nodes"][e.target], 'From' : get_node_attribute_index (e.source, "Source"), 'To' : get_node_attribute_index (e.target, "Source"), 'Length' : e.length, 'Country1' : get_node_attribute_index (e.source, "Country"), 'Country2' : get_node_attribute_index (e.target, "Country"), 'Years' : years } ); /*if (get_node_attribute_index (e.source, "Source") == kp_tag && get_node_attribute_index (e.target, "Source") == kp_tag){ console.log (result.length-1); }*/ }); console.log (result[1153]); return result; }3d:[] 2f:{"title":"Preliminary analysis of the San Diego PIRC molecular transmission network","cells":[{"id":0,"value":"md`# Preliminary analysis of the San Diego PIRC molecular transmission network\n>Kosakovsky Pond SL\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1246,"value":"md`This notebook provides interpretation scripts with text and figures generated _live_ from network data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":898,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1440,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1162,"value":"md`## Cluster descriptions`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1231,"value":"md`**Table 1** All subtype B clusters with 10 more members`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1233,"value":"table(_.filter (REPORT_cluster_summary.cluster_descriptions, (data) => data[1] == \"B\" && data[2] >= 10 && data[4]+data[5]), {columns :[\n { key: \"0\",\n name : 'Cluster ID'\n },\n { key : \"9\",\n name : \"Class\"\n },\n { key: \"1\",\n name : 'Subtype'\n },\n { key: \"2\",\n name : 'Size'\n },\n { key: \"3\",\n name : kp_tag\n },\n { key: \"4\",\n name : 'LANL direct'\n },\n { key: \"5\",\n name : 'LANL indirect'\n },\n { key: \"6\",\n name : 'Median Year ' + kp_tag\n },\n { key: \"7\",\n name : 'Median Year LANL'\n },\n { key: \"8\",\n name : 'Countries (LANL)'\n }\n ]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1198,"value":"md`**Table 2** All non-subtype B clusters`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1165,"value":"table(_.filter (REPORT_cluster_summary.cluster_descriptions, (data) => data[1] != \"B\" && data[4]+data[5]), {columns :[\n { key: \"0\",\n name : 'Cluster ID'\n },\n { key: \"1\",\n name : 'Subtype'\n },\n { key: \"9\",\n name : 'Class'\n },\n { key: \"2\",\n name : 'Size'\n },\n { key: \"3\",\n name : 'Condesa'\n },\n { key: \"4\",\n name : 'LANL direct'\n },\n { key: \"5\",\n name : 'LANL indirect'\n },\n { key: \"6\",\n name : 'Median Year Condesa'\n },\n { key: \"7\",\n name : 'Median Year LANL'\n },\n { key: \"8\",\n name : 'Countries (LANL)'\n }\n ]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":108,"value":"md`## How are LANL and ${kp_tag} sequences interconnected?\n\nThis figure uses a flow chart to show how many LANL and ${kp_tag} sequences are connected in the joint network\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"md`**Figure 2** LANL and ${kp_tag} sequence connections in the joint network`","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"chord_diagram(REPORT_link_summary.edge_associations_counts, REPORT_link_summary.edge_associations_labels)","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"md`## Visualizing the distribution of edge lengths\n\nThe following hex-bin plots depict the density of edge lengths as a function of time separating the dates of isolation for the two connected sequences. The plots are stratified by the types of nodes being connected.\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":788,"value":"md`**Figure S1.** The relationship between temporal sampling differences and TN93 distances for the connected ${kp_tag} nodes.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":740,"value":"distance_bars (ncc_links, kp_tag + \"-\" + kp_tag + \" links\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":776,"value":"distance_bars (ncc_lanl_links, kp_tag + \"-LANL links\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":287,"value":"md`**Figure S2** Distance between linked ${kp_tag} sequences: _${distance_descriptor(ncc_links)}_`","pinned":false,"hidden":false,"mode":"ojs"},{"id":145,"value":"hexbins(ncc_links)","pinned":false,"hidden":false,"mode":"ojs"},{"id":264,"value":"md`**${kp_tag} to LANL links**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":301,"value":"md`**Figure S3** Distance between linked ${kp_tag} and LANL sequences: _${distance_descriptor(ncc_lanl_links)}_`","pinned":false,"hidden":false,"mode":"ojs"},{"id":266,"value":"hexbins(ncc_lanl_links)","pinned":false,"hidden":false,"mode":"ojs"},{"id":325,"value":"md`> Do NCC-NCC links have singificantly different lengths than NCC-LANL links? Wilcoxson 2-sided test **p=${d3.format (\".3~g\")(genstat.wilcoxon(ncc_links.map (d=>{return d[1];}),ncc_lanl_links.map (d=>{return d[1];})).p * 2)}**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":268,"value":"md`**LANL to LANL links**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":314,"value":"lanl_to_lanl=edge_data.filter (d => {return d.Years != null && d.From == \"LANL\" && d.To == \"LANL\";}).map (d => {return [+d.Years, +d.Length];})","pinned":false,"hidden":false,"mode":"ojs"},{"id":317,"value":"md`**Figure S3** Distance between linked LANL sequences: _${distance_descriptor(lanl_to_lanl)}_`","pinned":false,"hidden":false,"mode":"ojs"},{"id":270,"value":"hexbins(lanl_to_lanl)","pinned":false,"hidden":false,"mode":"ojs"},{"id":365,"value":"md`### Descriptive data on the clusters\n\nIn this section, I plot descriptive statistics about clusters, and comparison of cluster sizes in NCC and NCC-LANL networks. \n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":432,"value":"md`**Figure 1.** Distribution and composition of cluster sizes in the NCC plus LANL transmission network. Figure 1A shows the numbers of clusters according to their sizes. Subtype B sequences are indicated in orange and non-subtype B sequences are indicated in purple. Figure 1B shows the counts of NCC and LANL sequences in clusters grouped by their sizes with NCC sequences indicated in dark grey and LANL sequences indicated in light grey. ` ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1396,"value":"md`# A`","pinned":false,"hidden":false,"mode":"ojs"},{"id":376,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1402,"value":"md`# B`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1392,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1928,"value":"md`## ${kp_tag} node list`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1930,"value":"table(_.map (_.filter (network_json.Nodes, (n) => get_node_attribute (n, \"Source\") == kp_tag), (n) =>\n {\n return {'id' : n.id,\n 'cluster' : n.cluster,\n 'cluster type' : REPORT_cluster_summary.cluster_descriptions.find((ci)=> (+ci[0]) == n.cluster).slice(-1)[0] ,\n 'Subtype' : get_node_attribute (n, \"Subtype\"),\n 'Cluster subtype' : REPORT_cluster_summary.subtype_by_cluster [n.cluster],\n 'Clustered with LANL' : (\"LANL\" in network_summary.by_cluster[n.cluster]) ? \"Yes\" : \"No\",\n 'Linked to LANL' : _.size (network_summary.detailed_countries_by_ncc_node[n.id]) > 1 ? \"Yes\" : \"No\",\n 'Linked to countries' : _.keys (network_summary.detailed_countries_by_ncc_node[n.id]).join (', ')\n }\n }), {limit : 5000}\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":700,"value":"md`## Visualizing individual cluster connectivity`","pinned":false,"hidden":false,"mode":"ojs"},{"id":702,"value":"md`#### \"HXB2 cluster\" -- old subtype B sequences`","pinned":false,"hidden":false,"mode":"ojs"},{"id":706,"value":"chord_diagram(REPORT_link_summary_cluster_2.edge_associations_counts, REPORT_link_summary_cluster_2.edge_associations_labels)","pinned":false,"hidden":false,"mode":"ojs"},{"id":792,"value":"network_json = {\n return await d3.json (\"https://dl.dropbox.com/s/1tvdlnwhyua8azi/pirc-lanl-filtered.json\") \n //return await d3.json (\"https://dl.dropbox.com/s/weq0pwrcbhb2jby/network-unfiltered.json\")\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":795,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":304,"value":"distance_descriptor = (array) => {\n var formatter = d3.format (\".3~p\");\n var accessor = r => {return r[1];};\n array.sort ((a,b) => {return a[1]-b[1];});\n return \"median \" + formatter (d3.median (array, accessor)) + \", IQR [\" + formatter (d3.quantile (array, 0.25, accessor)) + \", \" + formatter (d3.quantile (array, 0.75, accessor)) + \"]\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":147,"value":"d3_hexbin=require(\"d3-hexbin\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2311,"value":"home_country = 'US'","pinned":false,"hidden":false,"mode":"ojs"},{"id":1004,"value":"lanl_tag = \"LANL\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":808,"value":"kp_tag = \"PIRC\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":117,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":998,"value":"sequence_count = { return {\n \"NCC\" : 1212, \n \"LANL\" : 199137,\n\"NCCnonB\" : 203 };\n }","pinned":false,"hidden":false,"mode":"ojs"},{"id":822,"value":"get_node_attribute_index = (idx, attr) => {return network_json [\"Nodes\"][idx][\"patient_attributes\"][attr];}","pinned":false,"hidden":false,"mode":"ojs"},{"id":805,"value":"get_node_attribute = (n, attr) => {return n[\"patient_attributes\"][attr];}","pinned":false,"hidden":false,"mode":"ojs"},{"id":802,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":731,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":815,"value":"network_summary = generate_network_summary ((x,a) => {return true;})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1088,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":1167,"value":"REPORT_cluster_summary = generate_cluster_summary (network_summary)","pinned":false,"hidden":false,"mode":"ojs"},{"id":847,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":879,"value":"REPORT_link_summary = report_linkages ((x)=>{return true;}, network_summary)","pinned":false,"hidden":false,"mode":"ojs"},{"id":886,"value":"d3 = require(\"https://d3js.org/d3.v5.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":890,"value":"genstat = require('https://bundle.run/genstats@0.0.1')","pinned":false,"hidden":false,"mode":"ojs"},{"id":297,"value":"ncc_lanl_links = edge_data.filter (d => {return d.Years != null && d.From != d.To;}).map (d => {return [+d.Years, +d.Length];})","pinned":false,"hidden":false,"mode":"ojs"},{"id":283,"value":"ncc_links = edge_data.filter (d => {return d.Years != null && d.From == kp_tag && d.To == kp_tag;}).map (d => {return [+d.Years, +d.Length];})","pinned":false,"hidden":false,"mode":"ojs"},{"id":893,"value":"spearman_rho = require('https://bundle.run/spearman-rho@1.0.6')","pinned":false,"hidden":false,"mode":"ojs"},{"id":896,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":908,"value":"text_float_format = d3.format (\".3p\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":988,"value":"comma_number_format = d3.format(\",\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":900,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":1026,"value":"value_and_fraction_int = (value, total, descriptor, extra) => {return comma_number_format (value) + (descriptor ? \" \" + descriptor : \"\") + \" (\" + text_float_format (value / total) + (extra ? \" \" + extra : \"\") + \")\";}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1137,"value":"_=require('lodash')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1160,"value":"import { table } from \"@gampleman/table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2211,"value":"get_year_for_node = (n) => {\n const d = get_node_attribute_index (n, \"hiv_aids_dx_dt\");\n if (d) {\n return parseInt (d.substr (0,4)); \n }\n return d;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1294,"value":"plotly=require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1297,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":1308,"value":"year_reporter = (data, f) => {\n var distances = _.map (_.filter (data, (c) => f (c[1])), (c) => c[0]).sort( (a,b) => (a-b));\n \n return \"\" + d3.median (distances) + \" years (IQR: \" + d3.quantile (distances, 0.25) + \" to \" + d3.quantile (distances, 0.75) + \")\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1451,"value":"ncc_nonb = generate_sequence_linkage_proportions ((n) => get_node_attribute (n, \"Subtype\") != \"B\", (n) => {return REPORT_cluster_summary.subtype_by_cluster[n.cluster] != \"B\";})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1541,"value":" ncc_subtype_b = generate_sequence_linkage_proportions ((n) => get_node_attribute (n, \"Subtype\") == \"B\", (n) => {return REPORT_cluster_summary.subtype_by_cluster[n.cluster]==\"B\";})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1535,"value":"generate_sequence_linkage_proportions = (filter, filter2) => \n{\n var non_b = [];\n _.each (network_json[\"Nodes\"], (n, i) => {\n if (/*get_node_attribute (n, \"Subtype\") != \"B\"*/ filter (n) && get_node_attribute (n, \"Source\") == \"NCC\") {\n non_b.push ([n,i]);\n }\n });\n non_b = _.filter (non_b, (n) => {\n return \"LANL\" in network_summary.by_cluster[n[0].cluster] && filter2 (n[0]) /*REPORT_cluster_summary.subtype_by_cluster[n.cluster] != \"B\"*/;\n });\n \n var linked = _.filter (non_b, (n) => {\n return \"LANL\" in network_summary.connections_by_node [n[1]];\n });\n \n var linked_non_us = _.filter (linked, (n) => {\n return (_.size (network_summary.detailed_countries_by_ncc_node [n[0].id]) - (\"US\" in network_summary.detailed_countries_by_ncc_node [n[0].id] ? 1 : 0)) > 0;\n });\n \n return {\n 'clustered' : non_b,\n 'linked' : linked,\n 'linked_non_us' : linked_non_us\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1560,"value":"jstat = require('jstat')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1572,"value":"contingency_table_test = (table) => {\n var total = table[0][0] + table[0][1] + table[1][0] + table[1][1];\n var row_sum = _.map (table, (r) => r[0] + r[1]);\n var col_sum = [table[0][0] + table[1][0], table[0][1] + table[1][1]];\n var expected_counts = [\n row_sum[0]*col_sum[0] / total, row_sum[0]*col_sum[1] / total,\n row_sum[1]*col_sum[0] / total, row_sum[1]*col_sum[1] / total\n ];\n \n var stat_value = d3.sum (_.map (_.flatten (table), (e,i) => {\n return (e-expected_counts[i])*(e-expected_counts[i])/expected_counts[i];\n }));\n return {'stat' : stat_value, 'p': 1-jstat.chisquare.cdf (stat_value, 1)};\n};","pinned":false,"hidden":false,"mode":"ojs"},{"id":1593,"value":"format_p_value = (p) => {\n if (p < 0.001) {\n return \"p < 0.001\"; \n }\n return \"p = \" + (d3.format (\".2g\"))(p);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1611,"value":"fraction_format = d3.format (\".4p\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1860,"value":"count_clusters_by_subtype = (st) => {\n return _.filter (REPORT_cluster_summary.cluster_descriptions, (c) => {return c[1] == st && c[4] + c[5];}).length;\n};","pinned":false,"hidden":false,"mode":"ojs"},{"id":1867,"value":"spell_out_number = (number) => {\n if (number <= 10) {\n return {\n '0' : 'zero',\n '1' : 'one',\n '2' : 'two',\n '3' : 'three',\n '4' : 'four',\n '5' : 'five',\n '6' : 'six',\n '7' : 'seven',\n '8' : 'eight',\n '9' : 'nine',\n '10' : 'ten' \n } [number];\n }\n return number;\n}; ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1880,"value":"cluster_2 = generate_network_summary ((n) => {return n.cluster == 2;})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2065,"value":"REPORT_link_summary_cluster_2 = report_linkages ((x)=>{return true;}, cluster_2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2092,"value":"ncc_lanl_links_A6 = _.map (_.groupBy (edge_data.filter (d => {return d.Years != null && d.From == d.To && d.From == \"LANL\" && d.ID1.cluster == 476;}).map (d => {return {'id' : d.ID1.patient_attributes.tag < d.ID2.patient_attributes.tag ? d.ID1.patient_attributes.tag + \"|\" +d.ID2.patient_attributes.tag : d.ID2.patient_attributes.tag + \"|\" +d.ID1.patient_attributes.tag, 'length' : +d.Length}}), \"id\"), (v,k)=>[k, d3.median(_.map (v, v=>v.length)), _.size (v)])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2130,"value":"{\n var year_map = (y) => {\n if (y < 4) {\n return \"\" + y + \"-\" + (y+1) + \" yrs\"; \n }\n return \"≥5 yrs\";\n };\n \n return _.map (_.groupBy (_.map (edge_data.filter (d => {return d.Years != null && d.From == d.To && (d.From = 'LANL') && d.ID1.cluster == 476;}), (e) => {return {'year' : year_map(Math.abs(e.ID1.patient_attributes.Year - e.ID2.patient_attributes.Year)), 'length' : e.Length}}), 'year'), (v, k) => [k, d3.median (_.map (v, e=>e.length)), _.size (v)]);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2294,"value":"genstat.wilcoxon (_.map (ncc_links, (l) => l[0]), _.map (ncc_lanl_links, (l) => l[0]))","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W3d","stdlib":"1","titling":"auto"}