js/source/legacy/solGS/geneticGain.js

   1 /**
   2 * visualize and compare gebvs of a training population
   3 * and a selection population (genetic gain).
   4 * normal distribution plotting using d3.
   5 * uses methods from solGS.normalDistribution and solGS.linePlot js libraries
   6
   7 * Isaak Y Tecle <iyt2@cornell.edu>
   8 *
   9 */
  10
  11 var solGS = solGS || function solGS() {};
  12
  13 solGS.geneticGain = {
  14   canvas: "#gg_canvas",
  15   ggPlotDivPrefix: "#gg_plot",
  16   ggMsgDiv: "#gg_message",
  17   ggPopsDiv: "#gg_pops_select_div",
  18   ggPopsSelectMenuId: "#gg_pops_select",
  19
  20   gebvsComparison: function () {
  21     var gebvParams = this.getGeneticGainArgs();
  22
  23     var missing;
  24     if (!gebvParams.training_pop_id) {
  25       missing = "training population id";
  26     }
  27
  28     if (!gebvParams.selection_pop_id) {
  29       missing += ", selection population id";
  30     }
  31
  32     if (!gebvParams.trait_id) {
  33       missing += ", trait id";
  34     }
  35
  36     var ggMsgDiv = this.ggMsgDiv;
  37     if (missing) {
  38       jQuery(ggMsgDiv)
  39         .html("Can not compare GEBVs. I am missing " + missing + ".")
  40         .show();
  41     } else {
  42       this.plotGeneticGainBoxplot(gebvParams);
  43     }
  44   },
  45
  46   getGeneticGainArgs: function () {
  47     var canvas = this.canvas;
  48
  49     var trainingPopId = jQuery(`${canvas} #training_pop_id`).val();
  50     var trainingPopName = jQuery(`${canvas} #training_pop_name`).val();
  51     var selectionPopId = jQuery("#gg_selected_pop_id").val() || jQuery("#selection_pop_id").val();
  52     var trainingTraitsIds = jQuery(canvas).find("#training_traits_ids").val();
  53     var traitId = jQuery("#trait_id").val();
  54     var protocolId = jQuery("#genotyping_protocol_id").val();
  55
  56     if (document.URL.match(/solgs\/traits\/all\/population\/|solgs\/models\/combined\/trials\//)) {
  57       if (trainingTraitsIds) {
  58         trainingTraitsIds = trainingTraitsIds.split(",");
  59       }
  60     }
  61
  62     var ggArgs = {
  63       training_pop_id: trainingPopId,
  64       training_pop_name: trainingPopName,
  65       selection_pop_id: selectionPopId,
  66       training_traits_ids: trainingTraitsIds,
  67       trait_id: traitId,
  68       genotyping_protocol_id: protocolId,
  69     };
  70
  71     return ggArgs;
  72   },
  73
  74   plotGeneticGainBoxplot: function (ggArgs) {
  75     var canvas = this.canvas;
  76     var ggMsgDiv = this.ggMsgDiv;
  77
  78
  79         ggArgs = JSON.stringify(ggArgs)
  80     jQuery(` ${canvas} .multi-spinner-container`).show();
  81     jQuery("#check_genetic_gain").hide();
  82     jQuery(ggMsgDiv).html("Please wait... plotting genetic gain").show();
  83
  84     jQuery.ajax({
  85       type: "POST",
  86       dataType: "json",
  87       data: {'arguments': ggArgs},
  88       url: "/solgs/genetic/gain/boxplot",
  89       success: function (res) {
  90         if (res.Error) {
  91           jQuery(`${canvas} .multi-spinner-container`).hide();
  92           jQuery(ggMsgDiv).empty();
  93
  94           solGS.showMessage(ggMsgDiv, res.Error);
  95
  96           if (
  97             document.URL.match(
  98               /\/solgs\/traits\/all\/population\/|solgs\/models\/combined\/trials\//
  99             )
 100           ) {
 101             jQuery("#check_genetic_gain").show();
 102           }
 103         } else {
 104           var boxplot = res.boxplot;
 105           var boxplotData = res.boxplot_data;
 106           var plot = '<img  src= "' + boxplot + '">';
 107
 108           if (boxplot) {
 109             var fileNameBoxplot = boxplot.split("/").pop();
 110             boxplotFile = '<a href="' + boxplot + '" download=' + fileNameBoxplot + ">boxplot</a>";
 111
 112             var fileNameData = boxplotData.split("/").pop();
 113             var dataFile = '<a href="' + boxplotData + '" download=' + fileNameData + ">Data</a>";
 114             jQuery("#gg_plot")
 115               .prepend(
 116                 '<div style="margin-top: 20px">' +
 117                   plot +
 118                   "</div>" +
 119                   "<br /> <strong>Download:</strong> " +
 120                   boxplotFile +
 121                   " | " +
 122                   dataFile
 123               )
 124               .show();
 125
 126             jQuery(`${canvas} .multi-spinner-container`).hide();
 127             jQuery("#gg_message").empty();
 128
 129             if (
 130               document.URL.match(
 131                 /\/solgs\/traits\/all\/population\/|solgs\/models\/combined\/trials\//
 132               )
 133             ) {
 134               jQuery("#check_genetic_gain").show();
 135             }
 136           } else {
 137             jQuery(`${canvas} .multi-spinner-container`).hide();
 138             showMessage("There is no genetic gain plot for this dataset.");
 139             jQuery("#check_genetic_gain").show();
 140           }
 141         }
 142       },
 143       error: function (res) {
 144         jQuery(`${canvas} .multi-spinner-container`).hide();
 145         solGS.showMessage(ggMsgDiv, "Error occured plotting the genetic gain.");
 146         jQuery("#check_genetic_gain").show();
 147       },
 148     });
 149   },
 150
 151   getTrainingPopulationGEBVs: function (gebvParams) {
 152     var ggMsgDiv = this.ggMsgDiv;
 153     jQuery.ajax({
 154       type: "POST",
 155       dataType: "json",
 156       data: gebvParams,
 157       url: "/solgs/get/gebvs/training/population",
 158       success: function (res) {
 159         if (res.gebv_exists) {
 160           jQuery(ggMsgDiv).empty();
 161           trainingGEBVs = res.gebv_arrayref;
 162
 163           if (trainingGEBVs) {
 164             solGS.geneticGain.getSelectionPopulationGEBVs(gebvParams);
 165           }
 166         } else {
 167           jQuery(ggMsgDiv).html("There is no GEBV data for the training population.").show();
 168         }
 169       },
 170       error: function () {
 171         jQuery(ggMsgDiv)
 172           .html("Error occured checking for GEBV data for the training population.")
 173           .show();
 174       },
 175     });
 176   },
 177
 178   getSelectionPopulationGEBVs: function (gebvParams) {
 179     var ggMsgDiv = this.ggMsgDiv;
 180
 181     jQuery.ajax({
 182       type: "POST",
 183       dataType: "json",
 184       data: gebvParams,
 185       url: "/solgs/get/gebvs/selection/population",
 186       success: function (res) {
 187         if (res.gebv_exists) {
 188           jQuery(ggMsgDiv).empty();
 189
 190           selectionGEBVs = res.gebv_arrayref;
 191
 192           if (selectionGEBVs && trainingGEBVs) {
 193             jQuery(ggMsgDiv).html("Please wait... plotting gebvs").show();
 194
 195             solGS.geneticGain.plotGEBVs(trainingGEBVs, selectionGEBVs);
 196
 197             jQuery(ggMsgDiv).empty();
 198             jQuery("#check_genetic_gain").hide();
 199           }
 200         } else {
 201           jQuery(ggMsgDiv).html("There is no GEBV data for the selection population.").show();
 202         }
 203       },
 204       error: function () {
 205         jQuery(ggMsgDiv)
 206           .html("Error occured checking for GEBV data for the selection population.")
 207           .show();
 208       },
 209     });
 210   },
 211
 212   populateGeneticGainMenu: function (newPop) {
 213     var ggArgs = this.getGeneticGainArgs();
 214
 215     var ggPops = [];
 216     if (ggArgs.training_pop_id.match(/list/) == null) {
 217       var trialSelPopsList = solGS.selectionPopulation.getPredictedTrialTypeSelectionPops();
 218       if (trialSelPopsList) {
 219         ggPops.push(trialSelPopsList);
 220       }
 221     }
 222
 223     var menu = new SelectMenu(this.ggPopsDiv, this.ggPopsSelectMenuId);
 224
 225     if (newPop){
 226         menu.updateOptions(newPop);
 227     } else {
 228       menu.populateMenu(ggPops);
 229     }
 230
 231   },
 232
 233   getSelPopPredictedTraits: function (ggArgs) {
 234     var ggMsgDiv = this.ggMsgDiv;
 235         var canvas = this.canvas;
 236
 237         var args = JSON.stringify(ggArgs);
 238     jQuery.ajax({
 239       type: "POST",
 240       dataType: "json",
 241       data: {'arguments': args},
 242       url: "/solgs/selection/population/predicted/traits",
 243       success: function (res) {
 244         if (res.selection_traits) {
 245           var ggArgs = solGS.geneticGain.getGeneticGainArgs();
 246           jQuery(`${canvas} #selection_traits_ids`).val(res.selection_traits);
 247           solGS.geneticGain.plotGeneticGainBoxplot(ggArgs);
 248         } else {
 249           jQuery(ggMsgDiv).html("This selection population has no predicted traits.").show();
 250         }
 251       },
 252       error: function () {
 253         jQuery(ggMsgDiv)
 254           .html(
 255             "Error occured checking for predicted traits for the selection population " +
 256               selectionPopId
 257           )
 258           .show();
 259       },
 260     });
 261   },
 262
 263   plotGEBVs: function (trainingGEBVs, selectionGEBVs) {
 264     var normalDistTraining = new solGS.normalDistribution();
 265
 266     var trainingNormalDistData = normalDistTraining.getNormalDistData(trainingGEBVs);
 267
 268     var gebvZScoresT = normalDistTraining.getYValuesZScores(trainingNormalDistData);
 269
 270     var yValuesT = normalDistTraining.getPValues(trainingNormalDistData);
 271
 272     var zScoresPT = normalDistTraining.getZScoresP(trainingNormalDistData);
 273
 274     var xYT = normalDistTraining.getYValuesP(trainingNormalDistData);
 275
 276     var xValuesT = normalDistTraining.getYValues(trainingGEBVs);
 277
 278     var trMean = ss.mean(xValuesT);
 279
 280     var stdT = trMean <= 0 ? -1.0 : 1.0;
 281
 282     var xMT = normalDistTraining.getObsValueZScore(gebvZScoresT, stdT);
 283
 284     var normalDistSelection = new solGS.normalDistribution();
 285
 286     var selectionNormalDistData = normalDistSelection.getNormalDistData(selectionGEBVs);
 287
 288     var gebvZScoresS = normalDistSelection.getYValuesZScores(selectionNormalDistData);
 289
 290     var yValuesS = normalDistSelection.getPValues(selectionNormalDistData);
 291
 292     var zScoresPS = normalDistSelection.getZScoresP(selectionNormalDistData);
 293
 294     var xYS = normalDistSelection.getYValuesP(selectionNormalDistData);
 295
 296     var xValuesS = normalDistSelection.getYValues(selectionGEBVs);
 297
 298     var slMean = ss.mean(xValuesS);
 299
 300     var stdS = slMean <= 0 ? -1.0 : 1.0;
 301
 302     var xMS = normalDistTraining.getObsValueZScore(gebvZScoresS, stdS);
 303
 304     var svgId = "#compare_gebvs_canvas";
 305     var plotId = "#compare_gebvs_plot";
 306
 307     var trColor = "#02bcff";
 308     var slColor = "#ff1302";
 309     var axLabelColor = "#ff8d02";
 310     var yLabel = "Probability";
 311     var xLabel = "GEBVs";
 312
 313     var title =
 314       "Normal distribution curves of GEBVs " + "for the training and selection populations.";
 315
 316     var allData = {
 317       div_id: svgId,
 318       plot_title: title,
 319       x_axis_label: xLabel,
 320       y_axis_label: yLabel,
 321       axis_label_color: axLabelColor,
 322       lines: [
 323         {
 324           data: xYT,
 325           legend: "Training population",
 326           color: trColor,
 327         },
 328         {
 329           data: xYS,
 330           legend: "Selection population",
 331           color: slColor,
 332         },
 333       ],
 334     };
 335
 336     var linePlot = solGS.linePlot(allData);
 337
 338     var trainingMidlineData = [
 339       [trMean, 0],
 340       [trMean, d3.max(yValuesT)],
 341     ];
 342
 343     var selectionMidlineData = [
 344       [slMean, 0],
 345       [slMean, d3.max(yValuesS)],
 346     ];
 347
 348     var midLine = d3.svg
 349       .line()
 350       .x(function (d) {
 351         return linePlot.xScale(d[0]);
 352       })
 353       .y(function (d) {
 354         return linePlot.yScale(d[1]);
 355       });
 356
 357     linePlot.graph
 358       .append("path")
 359       .attr("d", midLine(trainingMidlineData))
 360       .attr("stroke", trColor)
 361       .attr("stroke-width", "3")
 362       .attr("fill", "none")
 363       .on("mouseover", function (d) {
 364         if ((d = trMean)) {
 365           linePlot.graph
 366             .append("text")
 367             .attr("id", "tr_mean")
 368             .text(d3.format(".2f")(trMean))
 369             .style({
 370               fill: trColor,
 371               "font-weight": "bold",
 372             })
 373             .attr("x", linePlot.xScale(xMT[0]))
 374             .attr("y", linePlot.yScale(d3.max(yValuesT) * 0.5));
 375         }
 376       })
 377       .on("mouseout", function () {
 378         d3.selectAll("text#tr_mean").remove();
 379       });
 380
 381     linePlot.graph
 382       .append("path")
 383       .attr("d", midLine(selectionMidlineData))
 384       .attr("stroke", slColor)
 385       .attr("stroke-width", "3")
 386       .attr("fill", "none")
 387       .on("mouseover", function (d) {
 388         if ((d = slMean)) {
 389           linePlot.graph
 390             .append("text")
 391             .attr("id", "sl_mean")
 392             .text(d3.format(".2f")(slMean))
 393             .style({
 394               fill: slColor,
 395               "font-weight": "bold",
 396             })
 397             .attr("x", linePlot.xScale(xMS[0]))
 398             .attr("y", linePlot.yScale(d3.max(yValuesS) * 0.5));
 399         }
 400       })
 401       .on("mouseout", function () {
 402         d3.selectAll("text#sl_mean").remove();
 403       });
 404   },
 405
 406   //////////
 407 };
 408 /////////
 409
 410 jQuery(document).ready(function () {
 411   jQuery("#check_genetic_gain").on("click", function () {
 412     var page = document.URL;
 413
 414     if (page.match(/solgs\/selection\//)) {
 415       solGS.geneticGain.gebvsComparison();
 416     } else {
 417       var selectedPopId = jQuery("#gg_selected_pop_id").val();
 418       var selectedPopType = jQuery("#gg_selected_pop_type").val();
 419       var selectedPopName = jQuery("#gg_selected_pop_name").val();
 420
 421       jQuery("#gg_message")
 422         .css({ "padding-left": "0px" })
 423         .html("checking predicted traits for selection population " + selectedPopName);
 424
 425       var ggArgs = solGS.geneticGain.getGeneticGainArgs();
 426       solGS.geneticGain.getSelPopPredictedTraits(ggArgs);
 427     }
 428   });
 429 });
 430
 431 jQuery(document).ready(function () {
 432   var page = document.URL;
 433
 434   if (page.match(/solgs\/traits\/all\/|solgs\/models\/combined\/trials\//) != null) {
 435     setTimeout(function () {
 436       solGS.geneticGain.populateGeneticGainMenu();
 437     }, 5000);
 438   }
 439 });
 440
 441 jQuery(document).ready(function () {
 442         var ggPopsDiv = solGS.geneticGain.ggPopsDiv;
 443         jQuery(ggPopsDiv).change(function () {
 444
 445           var selectedPop = jQuery("option:selected", this).data("pop");
 446
 447           jQuery("#gg_selected_pop_name").val(selectedPop.name);
 448           jQuery("#gg_selected_pop_id").val(selectedPop.id);
 449           jQuery("#gg_selected_pop_type").val(selectedPop.pop_type);
 450         });
 451
 452   });