js/solGS/gebvsComparison.js

   1 /**
   2 * visualize and compare gebvs of a training population
   3 * and a selection population.
   4 * normal distribution plotting using d3.
   5 * uses methods from solGS.normalDistribution and solGS.linePlot js libraries
   6 * Isaak Y Tecle <iyt2@cornell.edu>
   7 *
   8 */
   9
  10
  11 jQuery(document).ready(function () {
  12     jQuery('#compare_gebvs').on('click', function () {
  13         gebvsComparison();
  14     });
  15 });
  16
  17
  18 function gebvsComparison () {
  19
  20     var gebvParams = getGebvsParams();
  21
  22     var trainingGEBVs  = '';
  23     var selectionGEBVs = '';
  24
  25     var missing;
  26     if (!gebvParams.training_pop_id) {
  27         missing = 'training population id';
  28     }
  29
  30     if (!gebvParams.selection_pop_id) {
  31         missing += ', selection population id';
  32     }
  33
  34     if (!gebvParams.trait_id) {
  35         missing += ', trait id';
  36     }
  37
  38     if (missing) {
  39         jQuery('#compare_gebvs_message')
  40             .html('Can not compare GEBVs. I am missing ' + missing + '.')
  41             .show();
  42     }
  43     else {
  44         getTrainingPopulationGEBVs(gebvParams);
  45     }
  46
  47
  48     function getGebvsParams () {
  49
  50         var trainingPopId  = jQuery('#training_pop_id').val();
  51         var selectionPopId = jQuery('#selection_pop_id').val();
  52         var traitId        = jQuery('#trait_id').val();
  53
  54         var gebvParams = {
  55             'training_pop_id'  : trainingPopId,
  56             'selection_pop_id' : selectionPopId,
  57             'trait_id'         : traitId
  58         }
  59
  60         return gebvParams;
  61
  62     }
  63
  64     function getTrainingPopulationGEBVs (gebvParams) {
  65
  66         jQuery.ajax({
  67             type: 'POST',
  68             dataType: 'json',
  69             data: gebvParams,
  70             url : '/solgs/get/gebvs/training/population',
  71             success: function (res) {
  72                 if (res.gebv_exists) {
  73                     jQuery('#compare_gebvs_message').empty();
  74                     trainingGEBVs = res.gebv_arrayref;
  75
  76                     if (trainingGEBVs) {
  77                         getSelectionPopulationGEBVs(gebvParams)
  78                     }
  79
  80                 } else {
  81                     jQuery('#compare_gebvs_message')
  82                         .html('There is no GEBV data for the training population.')
  83                         .show();
  84                 }
  85             },
  86             error: function () {
  87                 jQuery('#compare_gebvs_message')
  88                     .html('Error occured checking for GEBV data for the training population.')
  89                     .show();
  90             }
  91         });
  92
  93     }
  94
  95
  96     function getSelectionPopulationGEBVs (gebvParams) {
  97
  98         jQuery.ajax({
  99             type: 'POST',
 100             dataType: 'json',
 101             data: gebvParams,
 102             url : '/solgs/get/gebvs/selection/population',
 103             success: function (res) {
 104                 if (res.gebv_exists) {
 105                     jQuery('#compare_gebvs_message').empty();
 106
 107                     selectionGEBVs = res.gebv_arrayref;
 108
 109                     if (selectionGEBVs && trainingGEBVs) {
 110                         jQuery('#compare_gebvs_message')
 111                             .html('Please wait... plotting gebvs')
 112                             .show();
 113
 114                         plotGEBVs(trainingGEBVs, selectionGEBVs);
 115
 116                         jQuery('#compare_gebvs_message').empty();
 117                         jQuery('#compare_gebvs').hide();
 118                     }
 119                 } else {
 120                     jQuery('#compare_gebvs_message')
 121                         .html('There is no GEBV data for the selection population.')
 122                         .show();
 123                 }
 124             },
 125             error: function () {
 126                 jQuery('#compare_gebvs_message')
 127                     .html('Error occured checking for GEBV data for the selection population.')
 128                     .show();
 129             }
 130         });
 131
 132     }
 133
 134
 135     function plotGEBVs (trainingGEBVs, selectionGEBVs) {
 136
 137         var normalDistTraining = new solGS.normalDistribution();
 138
 139         var trainingNormalDistData  = normalDistTraining
 140             .getNormalDistData(trainingGEBVs);
 141
 142         var gebvZScoresT = normalDistTraining
 143             .getYValuesZScores(trainingNormalDistData);
 144
 145         var yValuesT = normalDistTraining
 146             .getPValues(trainingNormalDistData);
 147
 148         var zScoresPT = normalDistTraining
 149             .getZScoresP(trainingNormalDistData);
 150
 151         var xYT =  normalDistTraining
 152             .getYValuesP(trainingNormalDistData);
 153
 154         var xValuesT =  normalDistTraining
 155             .getYValues(trainingGEBVs);
 156
 157         var trMean = ss.mean(xValuesT);
 158
 159         var stdT = trMean <= 0 ? -1.0 : 1.0;
 160
 161         var xMT = normalDistTraining.getObsValueZScore(gebvZScoresT, stdT);
 162
 163         var normalDistSelection = new solGS.normalDistribution();
 164
 165         var selectionNormalDistData = normalDistSelection
 166             .getNormalDistData(selectionGEBVs);
 167
 168         var gebvZScoresS = normalDistSelection
 169             .getYValuesZScores(selectionNormalDistData);
 170
 171         var yValuesS = normalDistSelection
 172             .getPValues(selectionNormalDistData);
 173
 174         var zScoresPS = normalDistSelection
 175             .getZScoresP(selectionNormalDistData);
 176
 177         var xYS = normalDistSelection
 178             .getYValuesP(selectionNormalDistData);
 179
 180         var xValuesS = normalDistSelection
 181             .getYValues(selectionGEBVs);
 182
 183         var slMean = ss.mean(xValuesS);
 184
 185         var stdS = slMean <= 0 ? -1.0 : 1.0;
 186
 187         var xMS = normalDistTraining.getObsValueZScore(gebvZScoresS, stdS);
 188
 189         var svgId  = '#compare_gebvs_canvas';
 190         var plotId = '#compare_gebvs_plot';
 191
 192         var trColor      = '#02bcff';
 193         var slColor      = '#ff1302';
 194         var axLabelColor = '#ff8d02';
 195         var yLabel       = 'Probability';
 196         var xLabel       = 'GEBVs';
 197
 198         var title = 'Normal distribution curves of GEBVs '
 199             + 'for the training and selection populations.';
 200
 201         var allData =  {
 202             'div_id': svgId,
 203             'plot_title': title,
 204             'x_axis_label': xLabel,
 205             'y_axis_label': yLabel,
 206             'axis_label_color': axLabelColor,
 207             'lines' :
 208             [
 209                 {
 210                     'data'  : xYT,
 211                     'legend': 'Training population' ,
 212                     'color' : trColor,
 213                 },
 214                 {
 215                     'data'  : xYS,
 216                     'legend': 'Selection population',
 217                     'color' : slColor,
 218                 },
 219
 220             ]
 221         };
 222
 223
 224         var linePlot  = solGS.linePlot(allData);
 225
 226         var trainingMidlineData  = [
 227             [trMean, 0],
 228             [trMean, d3.max(yValuesT)]
 229         ];
 230
 231         var selectionMidlineData = [
 232             [slMean, 0],
 233             [slMean, d3.max(yValuesS)]
 234         ];
 235
 236         var midLine = d3.svg.line()
 237             .x(function(d) {
 238                 return linePlot.xScale(d[0]);
 239             })
 240             .y(function(d) {
 241                 return linePlot.yScale(d[1]);
 242             });
 243
 244         linePlot.graph.append("path")
 245             .attr("d", midLine(trainingMidlineData))
 246             .attr("stroke", trColor)
 247             .attr("stroke-width", "3")
 248             .attr("fill", "none")
 249             .on("mouseover", function (d) {
 250                 if (d = trMean) {
 251                     linePlot.graph.append("text")
 252                         .attr("id", "tr_mean")
 253                         .text(d3.format(".2f")(trMean))
 254                         .style({
 255                             "fill"       : trColor,
 256                             "font-weight": "bold"
 257                         })
 258                         .attr("x", linePlot.xScale(xMT[0]))
 259                         .attr("y", linePlot.yScale(d3.max(yValuesT) * 0.5))
 260                 }
 261             })
 262             .on("mouseout", function() {
 263                 d3.selectAll("text#tr_mean").remove();
 264             });
 265
 266         linePlot.graph.append("path")
 267             .attr("d", midLine(selectionMidlineData))
 268             .attr("stroke", slColor)
 269             .attr("stroke-width", "3")
 270             .attr("fill", "none")
 271             .on("mouseover", function (d) {
 272                 if (d = slMean) {
 273                     linePlot.graph.append("text")
 274                         .attr("id", "sl_mean")
 275                         .text(d3.format(".2f")(slMean))
 276                         .style({
 277                             "fill"       : slColor,
 278                             "font-weight": "bold"
 279                         })
 280                         .attr("x", linePlot.xScale(xMS[0]))
 281                         .attr("y", linePlot.yScale(d3.max(yValuesS) * 0.5))
 282
 283                 }
 284             })
 285             .on("mouseout", function() {
 286                 d3.selectAll("text#sl_mean").remove();
 287             });
 288     }
 289
 290 //////////
 291 }
 292 /////////
 293
 294