31:T1992,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 NCC sequences and ${comma_number_format(network_summary.cluster_statistics.NCC_LANL)} clusters containing both NCC and LANL sequences. The ${comma_number_format(network_summary.cluster_statistics.NCC)} NCC-only clusters comprised ${value_and_fraction_int(network_summary.sequences.NCC_NCC, sequence_count['NCC'], 'NCC sequences', 'of all NCC sequences')}. The ${comma_number_format(network_summary.cluster_statistics.NCC_LANL)} NCC-LANL clusters included ${value_and_fraction_int(network_summary.sequences.NCC_LANL, sequence_count['NCC'], 'NCC sequences', 'of all NCC 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, ${value_and_fraction_int(REPORT_link_summary.ncc_linked, network_summary.sequences.NCC_LANL+network_summary.sequences.NCC_NCC)} were directly linked to other NCC sequences. The majority (${text_float_format(d3.bisectLeft (network_summary.cluster_sizes, 14)/network_summary.cluster_statistics.total)}) of clusters contained 2 to 13 sequences, with ${network_summary.cluster_statistics.total-d3.bisectLeft (network_summary.cluster_sizes, 14)} large clusters containing 14 or more sequences. Of the ${_.size (_.filter (REPORT_cluster_summary.cluster_descriptions, c=>c[2] >= 14 && c[4] + c[5]))} of the largest clusters with both NCC and LANL sequences, ${_.size (_.filter (REPORT_cluster_summary.cluster_descriptions, c=> c[1] != 'B' && c[2] >= 14 && c[4] + c[5]))} contained non-B viruses (Figure 1A), and most clusters also contained primarily LANL sequences, with only a few NCC sequences per cluster (Figure 1B, median NCC proportion ${text_float_format (d3.median (_.map (_.filter (REPORT_cluster_summary.cluster_descriptions, (c) => c[2] >= 14 && c[4] + c[5]), (c) => (c[3] / c[2])).sort()))}). The distribution of TN93 distance for the edges connecting NCC sequences to one another (${distance_descriptor(ncc_links)}) had a significantly lower median than distribution of TN93 distances for the edges connecting NCC 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 NCC and LANL sequences is depicted in Figure 2. The median difference in sample years between linked NCC 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)} NCC-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 NCC sequences. Of the ${REPORT_link_summary.total} NCC 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 U.S. 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-U.S. sequences, ${REPORT_link_summary.both} (${text_float_format(REPORT_link_summary.both/REPORT_link_summary.total)}) linked to both U.S. and non-U.S. 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)}) NCC sequences were directly connected to one or more non-U.S. sequences via ${REPORT_link_summary.total_links_outside_us} links. Of these, ${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, ${text_float_format(REPORT_link_summary.binned_links['Africa']/REPORT_link_summary.total_links_outside_us)} to Africa and ${text_float_format(REPORT_link_summary.binned_links['Former USSR']/REPORT_link_summary.total_links_outside_us)} to countries from the former Soviet Union. The intricate web of interconnectivity between the NCC and LANL sequences is illustrated in Figure 3. 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:Tf72,md`### Subtype B and non-B sequences Of the ${comma_number_format(sequence_count.NCCnonB)} NCC non-B sequences, ${value_and_fraction_int(ncc_nonb.clustered.length, sequence_count.NCCnonB)} were clustered with one or more LANL sequences, ${comma_number_format(ncc_nonb.linked.length)} of these were directly linked to a sequence in the LANL dataset, and ${_.filter (network_json.Nodes, (n) => get_node_attribute (n, "Source") == kp_tag && get_node_attribute (n, "Subtype") != 'B' && network_summary.ncc_to_ncc[n.id]).length} were directly linked to other NCC sequences. Of these ${comma_number_format(ncc_nonb.linked.length)} sequences ${value_and_fraction_int(ncc_nonb.linked_non_us.length, ncc_nonb.linked.length)} were directly linked to a sequence in one or more non-U.S. countries – a proportion significantly greater than the ${value_and_fraction_int(ncc_subtype_b.linked_non_us.length, ncc_subtype_b.linked.length)} of the ${comma_number_format(ncc_subtype_b.clustered.length)} clustered subtype B sequences with direct links to a non-U.S. country (${format_p_value(contingency_table_test ([[ncc_subtype_b.linked_non_us.length,ncc_subtype_b.linked.length-ncc_subtype_b.linked_non_us.length],[ncc_nonb.linked_non_us.length,ncc_nonb.linked.length-ncc_nonb.linked_non_us.length]]).p)}). The proportions of NCC sequences directly linked to LANL sequences was not significantly different between subtype B and non-B sequences ( ${fraction_format(ncc_subtype_b.linked.length/(sequence_count.NCC-sequence_count.NCCnonB))} vs ${fraction_format(ncc_nonb.linked.length/(sequence_count.NCCnonB))}; ${format_p_value(contingency_table_test ([[ncc_subtype_b.linked.length,sequence_count.NCC-sequence_count.NCCnonB-ncc_subtype_b.linked.length],[ncc_nonb.linked.length,(sequence_count.NCCnonB-ncc_nonb.linked.length)]]).p)}, 𝛘2 test). Of the ${_.filter (REPORT_cluster_summary.cluster_descriptions, (c) => (c[1] != 'B' && c[4] + c[5])).length} non-B clusters with LANL sequences, ${_.filter (REPORT_cluster_summary.cluster_descriptions, (c) => (c[1] != 'B' && c[4] + c[5] && (c[8].indexOf (',') > 0 || c[8].indexOf ('USA') != 0))).length} included one or more sequences from outside of the U.S (Table 2). Compared to the ${comma_number_format(sequence_count.NCCnonB-ncc_nonb.clustered.length)} NCC non-B sequences that did not cluster with LANL sequences, the ${ncc_nonb.clustered.length} NCC sequences that did cluster were significantly more likely to be from persons who were male (77.8% vs. 58.9%; p=0.032), Asian (40.0% vs. 17.7%; p=0.003), MSM (44.4% vs. 26.6%; p=0.035), or intravenous drug users (6.8% vs. 0%; p=0.01), and less likely to be African American (22.2% vs. 54.4%; p<0.001). Of the ${comma_number_format(network_summary.cluster_statistics.NCC_LANL)} NCC-LANL clusters within the inferred transmission network, ${value_and_fraction_int(_.filter (REPORT_cluster_summary.cluster_descriptions, (c) => (c[1] && c[1] =='B' && c[4] + c[5])).length, network_summary.cluster_statistics.NCC_LANL)} contained subtype B sequences and ${value_and_fraction_int(_.filter (REPORT_cluster_summary.cluster_descriptions, (c) => (c[1] && c[1] !='B' && c[4] + c[5])).length, network_summary.cluster_statistics.NCC_LANL)} contained non-B subtype sequences including ${spell_out_number(count_clusters_by_subtype ('01_AE'))} CRF01_AE clusters, ${spell_out_number(count_clusters_by_subtype ('C'))} subtype C clusters, ${spell_out_number(count_clusters_by_subtype ('D'))} subtype D clusters, and 11 additional clusters each containing sequences belonging to a different subtype, circulating recombinant form (CRF), or unique recombinant form. The sizes and spatiotemporal characteristics of the 28 subtype B clusters with ten or more sequences as summarized in Table 1. Table 2 summarizes the sizes and the spatiotemporal characteristics of the ${comma_number_format(network_summary.cluster_statistics.NCC_LANL-count_clusters_by_subtype('B'))} non-B clusters. ` 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:T6f9,{ var data = []; _.each ([[x => {return x[3]}, 'NCC'], [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: 0.2, 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:T529,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', 'missing': '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, 'US' : true, 'Asia' : true,'Former USSR' : true,'Canada' : true,'Europe' : true}, "map_code" : (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:T1389,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" in c);})); var ncc_sequences_in_ncc_clusters = d3.sum (_.map (_.filter (by_cluster, (c) => {return ! ("LANL" 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" in c);}), (c) => {return c["NCC"].length;})); var lanl_sequences = d3.sum (_.map (_.filter (by_cluster, (c) => {return ("LANL" 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:T1700,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, (typess, node) => { var node_type = get_node_attribute_index (node, "Source"); var types = _.clone (typess); delete types["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 get_node_attribute_index (v, "Year"); }), (c) => c).sort(); var years_lanl = _.filter (_.map (network_summary_data.by_cluster[cd[0]][lanl_tag], (v, i) => { return get_node_attribute_index (v, "Year"); }), (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 ("US" in countries) { has_us = countries["US"]; } 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:Tdba,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['NCC']).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 ('US' in linked_countries) { us_only += 1; } else { non_us_only += 1; country_count.push (1); } } else { if (!('US' 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['US']; 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:[] 2f:{"title":"National and International Dimensions of HIV-1 Sequence Clusters in a Northern California Clinical Cohort ","cells":[{"id":0,"value":"md`# National and International Dimensions of HIV-1 Sequence Clusters in a Northern California Clinical Cohort \n>Rhee SY, Magalis BR, Hurley L, Silverberg MJ, Marcus JL, Slome S, Kosakovsky Pond SL, Shafer RW \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 : 'NCC'\n },\n { key: \"4\",\n name : 'LANL direct'\n },\n { key: \"5\",\n name : 'LANL indirect'\n },\n { key: \"6\",\n name : 'Median Year NCC'\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 : 'NCC'\n },\n { key: \"4\",\n name : 'LANL direct'\n },\n { key: \"5\",\n name : 'LANL indirect'\n },\n { key: \"6\",\n name : 'Median Year NCC'\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 NCC sequences interconnected?\n\nThis figure uses a flow chart to show how many LANL and NCC sequences are connected in the joint network\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"md`**Figure 3** LANL and NCC 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 NCC nodes.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":740,"value":"distance_bars (ncc_links, \"NCC-NCC links\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":776,"value":"distance_bars (ncc_lanl_links, \"NCC-LANL links\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":287,"value":"md`**Figure S2** Distance between linked NCC 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`**NCC to LANL links**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":301,"value":"md`**Figure S3** Distance between linked NCC 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`## NCC 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/knrftzib5wbsyub/network.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":283,"value":"ncc_links = edge_data.filter (d => {return d.Years != null && d.From == \"NCC\" && d.To == \"NCC\";}).map (d => {return [+d.Years, +d.Length];})","pinned":false,"hidden":false,"mode":"ojs"},{"id":808,"value":"kp_tag = \"NCC\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":117,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1004,"value":"lanl_tag = \"LANL\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":998,"value":"sequence_count = { return {\n \"NCC\" : 4553, \n \"LANL\" : 139060,\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":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":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":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":1294,"value":"plotly=require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1297,"value":"edge_data = {\n var result = [];\n _.each (network_json [\"Edges\"], (e, i) => {\n var year1 = get_node_attribute_index (e.source, \"Year\");\n var year2 = get_node_attribute_index (e.target, \"Year\");\n var years;\n \n if (year1 && year2) {\n years = Math.abs (year1-year2);\n } else {\n years = null;\n }\n \n result.push (\n {\n 'ID1' : network_json[\"Nodes\"][e.source],\n 'ID2' : network_json[\"Nodes\"][e.target],\n 'From' : get_node_attribute_index (e.source, \"Source\"),\n 'To' : get_node_attribute_index (e.target, \"Source\"),\n 'Length' : e.length,\n 'Country1' : get_node_attribute_index (e.source, \"Country\"),\n 'Country2' : get_node_attribute_index (e.target, \"Country\"),\n 'Years' : years \n }\n );\n });\n return result;\n}","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"}],"files":"$W3c","stdlib":"1","titling":"auto"}