In [1]:

    

from om import base, settings
from om.components import *
from om.data import *
from om.util import *

import pandas as pd
import numpy as np
import math,cobra

ome = base.Session()
#model = cobra.io.load_matlab_model('/Volumes/extra/Dropbox/om_data/models/iJO1366.mat')

ged = GeneExpressionData
dged = DifferentialGeneExpressionData
cpge = ChIPPeakGeneExpression


    

    




cobra/io/__init__.py:17 UserWarning: cobra.io.sbml requires libsbml

In [8]:

    

ome.query(chip_peak).all()


    

    
Out[8]:





[(330, 2, u'Nac', u'anti-myc', 11, u'Nac8myc'),
 (376, 3, u'Crp', u'anti-myc', 4, u'Crp8myc'),
 (350, 7, u'Fur', u'anti-myc', 10, u'Fur8myc'),
 (336, 1, u'RpoD', u'anti-rpod', 1, u'wt'),
 (369, 2, u'PurR', u'anti-myc', 16, u'PurR8myc'),
 (347, 3, u'Crp', u'anti-crp', 8, u'delta-crp'),
 (327, 8, u'RpoD', u'anti-rpod', 1, u'wt'),
 (368, 13, u'Cra', u'anti-myc', 14, u'Cra8myc'),
 (370, 4, u'Cra', u'anti-myc', 14, u'Cra8myc'),
 (355, 13, u'Crp', u'anti-myc', 4, u'Crp8myc'),
 (333, 2, u'Cra', u'anti-myc', 14, u'Cra8myc'),
 (358, 9, u'Nac', u'anti-myc', 11, u'Nac8myc'),
 (354, 10, u'NtrC', u'anti-myc', 12, u'NtrC8myc'),
 (329, 1, u'Crp', u'anti-crp', 1, u'wt'),
 (340, 3, u'Crp', u'anti-crp', 7, u'delAr2'),
 (338, 2, u'RpoD', u'anti-rpod', 1, u'wt'),
 (372, 9, u'RpoN', u'anti-rpon', 1, u'wt'),
 (326, 2, u'Crp', u'anti-crp', 1, u'wt'),
 (371, 2, u'Crp', u'anti-myc', 4, u'Crp8myc'),
 (357, 2, u'ArgR', u'anti-myc', 13, u'ArgR8myc'),
 (373, 3, u'Crp', u'anti-crp', 6, u'delAr1delAr2'),
 (361, 2, u'RpoS', u'anti-rpos', 1, u'wt'),
 (331, 3, u'Crp', u'anti-crp', 1, u'wt'),
 (344, 8, u'Nac', u'anti-myc', 11, u'Nac8myc'),
 (345, 2, u'RpoN', u'anti-rpon', 1, u'wt'),
 (351, 4, u'Fnr', u'anti-myc', 9, u'Fnr8myc'),
 (352, 12, u'Fnr', u'anti-myc', 9, u'Fnr8myc'),
 (342, 12, u'ArcA', u'anti-myc', 2, u'ArcA8myc'),
 (356, 2, u'RpoA', u'anti-rpoA', 1, u'wt'),
 (366, 8, u'NtrC', u'anti-myc', 12, u'NtrC8myc'),
 (349, 8, u'RpoN', u'anti-rpon', 1, u'wt'),
 (363, 9, u'RpoD', u'anti-rpod', 1, u'wt'),
 (346, 10, u'RpoN', u'anti-rpon', 1, u'wt'),
 (328, 10, u'Nac', u'anti-myc', 11, u'Nac8myc'),
 (334, 2, u'Lrp', u'anti-myc', 15, u'Lrp8myc'),
 (332, 9, u'NtrC', u'anti-myc', 12, u'NtrC8myc'),
 (337, 1, u'Crp', u'anti-myc', 4, u'Crp8myc'),
 (360, 2, u'NtrC', u'anti-myc', 12, u'NtrC8myc'),
 (335, 4, u'ArcA', u'anti-myc', 2, u'ArcA8myc'),
 (343, 10, u'RpoD', u'anti-rpod', 1, u'wt'),
 (341, 3, u'Crp', u'anti-crp', 3, u'Ar3'),
 (348, 3, u'Crp', u'anti-crp', 5, u'delAr1'),
 (374, 4, u'ArcA', u'anti-myc', 2, u'ArcA8myc'),
 (359, 6, u'Fur', u'anti-myc', 10, u'Fur8myc'),
 (339, 3, u'RpoD', u'anti-rpod', 1, u'wt'),
 (353, 4, u'Fnr', u'anti-myc', 9, u'Fnr8myc'),
 (362, 1, u'Crp', u'anti-crp', 4, u'Crp8myc'),
 (365, 3, u'Crp', u'anti-crp', 4, u'Crp8myc'),
 (364, 5, u'Crp', u'anti-crp', 1, u'wt'),
 (367, 11, u'RpoN', u'anti-rpon', 1, u'wt'),
 (375, 11, u'RpoD', u'anti-rpod', 1, u'wt')]

In [7]:

    

ome.query(diff_exp).all()


    

    
Out[7]:





[(382, u'delta-crp', u'wt', 1, u'fructose', u'NH4Cl', u'O2', u''),
 (398, u'delta-crp', u'wt', 2, u'glucose', u'NH4Cl', u'O2', u''),
 (390, u'delAr2', u'wt', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (392, u'delAr1', u'wt', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (393, u'delAr1delAr2', u'wt', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (394, u'Ar3', u'wt', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (396, u'delta-crp', u'wt', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (400, u'delta-fur', u'wt', 6, u'glucose', u'NH4Cl', u'O2', u'dpd'),
 (384, u'delta-fur', u'wt', 7, u'glucose', u'NH4Cl', u'O2', u'FeCl2'),
 (394, u'wt', u'Ar3', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (392, u'wt', u'delAr1', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (393, u'wt', u'delAr1delAr2', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (390, u'wt', u'delAr2', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (382, u'wt', u'delta-crp', 1, u'fructose', u'NH4Cl', u'O2', u''),
 (398, u'wt', u'delta-crp', 2, u'glucose', u'NH4Cl', u'O2', u''),
 (396, u'wt', u'delta-crp', 3, u'glycerol', u'NH4Cl', u'O2', u''),
 (400, u'wt', u'delta-fur', 6, u'glucose', u'NH4Cl', u'O2', u'dpd'),
 (384, u'wt', u'delta-fur', 7, u'glucose', u'NH4Cl', u'O2', u'FeCl2'),
 (411, u'delta-fnr', u'wt', 18, u'glucose', u'NH4Cl', u'anaerobic', u'asv2'),
 (411, u'wt', u'delta-fnr', 18, u'glucose', u'NH4Cl', u'anaerobic', u'asv2')]

In [5]:

    

ome.query(Arra


    

    
Out[5]:





[Differential Expression (#377): RNAseq_bw25113_acetate/fumarate_NH4Cl_O2_thiamine,
 Differential Expression (#378): RNAseq_wt_glucose_NH4Cl/cytidine_O2,
 Differential Expression (#379): RNAseq_wt_glucose_cytidine/glutamine_O2,
 Differential Expression (#380): RNAseq_wt_glucose_cytosine/glutamine_O2,
 Differential Expression (#381): RNAseq_wt_glucose_NH4Cl/glutamine_O2,
 Differential Expression (#382): RNAseq_delta-crp/wt_fructose_NH4Cl_O2,
 Differential Expression (#383): RNAseq_wt_glucose_NH4Cl_O2_supp/FeCl2,
 Differential Expression (#384): RNAseq_delta-fur/wt_glucose_NH4Cl_O2_FeCl2,
 Differential Expression (#385): RNAseq_delta-fur_glucose_NH4Cl_O2_FeCl2/dpd,
 Differential Expression (#386): RNAseq_wt_glucose_cytidine/cytosine_O2,
 Differential Expression (#387): RNAseq_wt_glucose_NH4Cl/cytosine_O2,
 Differential Expression (#388): RNAseq_bw25113_fumarate/glucose_NH4Cl_O2_thiamine,
 Differential Expression (#389): RNAseq_bw25113_acetate/glucose_NH4Cl_O2_thiamine,
 Differential Expression (#390): RNAseq_delAr2/wt_glycerol_NH4Cl_O2,
 Differential Expression (#391): RNAseq_wt_fructose/glycerol_NH4Cl_O2,
 Differential Expression (#392): RNAseq_delAr1/wt_glycerol_NH4Cl_O2,
 Differential Expression (#393): RNAseq_delAr1delAr2/wt_glycerol_NH4Cl_O2,
 Differential Expression (#394): RNAseq_Ar3/wt_glycerol_NH4Cl_O2,
 Differential Expression (#395): RNAseq_wt_glucose/glycerol_NH4Cl_O2,
 Differential Expression (#396): RNAseq_delta-crp/wt_glycerol_NH4Cl_O2,
 Differential Expression (#397): RNAseq_wt_fructose/glucose_NH4Cl_O2,
 Differential Expression (#398): RNAseq_delta-crp/wt_glucose_NH4Cl_O2,
 Differential Expression (#399): RNAseq_wt_glucose_NH4Cl_O2_FeCl2/dpd,
 Differential Expression (#400): RNAseq_delta-fur/wt_glucose_NH4Cl_O2_dpd,
 Differential Expression (#401): RNAseq_wt_glucose_NH4Cl_O2_supp/dpd,
 Differential Expression (#402): RNAseq_delta-crp_fructose/glycerol_NH4Cl_O2,
 Differential Expression (#403): RNAseq_delta-crp_glucose/glycerol_NH4Cl_O2,
 Differential Expression (#404): RNAseq_bw25113_fumarate/pyruvate_NH4Cl_O2_thiamine,
 Differential Expression (#405): RNAseq_bw25113_glucose/pyruvate_NH4Cl_O2_thiamine,
 Differential Expression (#406): RNAseq_bw25113_acetate/pyruvate_NH4Cl_O2_thiamine,
 Differential Expression (#407): RNAseq_delta-crp_fructose/glucose_NH4Cl_O2,
 Differential Expression (#408): affyexp_wt_acetate/fructose_NH4Cl_O2,
 Differential Expression (#409): affyexp_delta-purR_glucose_NH4Cl/adenine_O2,
 Differential Expression (#410): affyexp_delta-fnr_glucose_NH4Cl_O2/anaerobic,
 Differential Expression (#411): affyexp_delta-fnr/wt_glucose_NH4Cl_anaerobic,
 Differential Expression (#412): affyexp_delta-cra_glucose_NH4Cl_O2/anaerobic]

In [ ]:

    

from IPython.display import display
from IPython.html import widgets as W
from IPython.utils import traitlets as T

class HeatmapWidget(W.DOMWidget):
    _view_name = T.Unicode('HeatmapView', sync=True)
    heatmap_data = T.List(sync=True)
    row_labels = T.List(sync=True)
    col_labels = T.List(sync=True)
    hcrow = T.List(sync=True)
    hccol = T.List(sync=True)
    minval = T.Float(sync=True)
    maxval = T.Float(sync=True)
    html_style = T.Unicode(sync=True)


    

In [ ]:

    

%%javascript

require.config({paths: {d3: "https://mpld3.github.io/js/d3.v3.min"}});

require(["widgets/js/widget", "d3"], function(WidgetManager, d3){
    var HeatmapView = IPython.DOMWidgetView.extend({

        render: function(){
            
            this.$el.append(this.model.get("html_style"));

            this.update();
        },
        update: function(){
            var margin = { top: 75, right: 10, bottom: 150, left: 400 },
                cellSize=18,
                 // - margin.top - margin.bottom,
                  //gridSize = Math.floor(width / 24),
                legendElementWidth = cellSize*2.5,
                colorBuckets = 21,
                colors = ['#005824','#1A693B','#347B53','#4F8D6B','#699F83','#83B09B','#9EC2B3','#B8D4CB','#D2E6E3','#EDF8FB','#FFFFFF','#F1EEF6','#E6D3E1','#DBB9CD','#D19EB9','#C684A4','#BB6990','#B14F7C','#A63467','#9B1A53','#91003F'];
           
            var rowLabel = this.model.get("row_labels");
            var colLabel = this.model.get("col_labels");
            var data = this.model.get("heatmap_data");
            var hcrow = this.model.get("hcrow");
            var hccol = this.model.get("hccol");
            
            var col_number = colLabel.length,
                row_number = rowLabel.length;
            
            var width = cellSize*col_number, // - margin.left - margin.right,
                height = cellSize*row_number;
            
            var maxval = JSON.parse(this.model.get("maxval"));
            var minval = JSON.parse(this.model.get("minval"));
            
            var colorScale = d3.scale.quantile()
                               .domain([ minval , 0, maxval])
                               .range(colors);
  
            this.svg = d3.select(this.el).append("svg")
                .attr("width", width + margin.left + margin.right)
                .attr("height", height + margin.top + margin.bottom)
                .append("g")
                .attr("transform", "translate(" + margin.left + "," + margin.top + ")");
            
            var svg = this.svg;

            var rowSortOrder=false;
            var colSortOrder=false;
            var rowLabels = svg.append("g")
                  .selectAll(".rowLabelg")
                  .data(rowLabel)
                  .enter()
                  .append("text")
                  .text(function (d) { return d; })
                  .attr("x", 0)
                  .attr("y", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })
                  .style("text-anchor", "end")
                  .attr("transform", "translate(-25," + cellSize / 1.5 + ")")
                  .attr("class", function (d,i) { return "rowLabel mono r"+i;} )
                  .on("mouseover", function(d) {d3.select(this).classed("text-hover",true);})
                  .on("mouseout" , function(d) {d3.select(this).classed("text-hover",false);})
                  .on("click", function(d,i) {rowSortOrder=!rowSortOrder; sortbylabel("r",i,rowSortOrder); this.$('#order').property("selectedIndex", 4).node().focus();;});

            var colLabels = svg.append("g")
                  .selectAll(".colLabelg")
                  .data(colLabel)
                  .enter()
                  .append("text")
                  .text(function (d) { return d; })
                  .attr("x", 0)
                  .attr("y", function (d, i) { return hccol.indexOf(i+1) * cellSize; })
                  .style("text-anchor", "left")
                  .attr("transform", "translate("+cellSize/2 + ",-25) rotate (-90)")
                  .attr("class",  function (d,i) { return "colLabel mono c"+i;} )
                  .on("mouseover", function(d) {d3.select(this).classed("text-hover",true);})
                  .on("mouseout" , function(d) {d3.select(this).classed("text-hover",false);})
                  .on("click", function(d,i) {colSortOrder=!colSortOrder;  sortbylabel("c",i,colSortOrder); this.$('#order').property("selectedIndex", 4).node().focus();;});

            var heatMap = svg.append("g").attr("class","g3")
                  .selectAll(".cellg")
                  .data(data,function(d){return d.row+":"+d.col;})
                  .enter()
                  .append("rect")
                  .attr("x", function(d) { return hccol.indexOf(d.col) * cellSize; })
        .attr("y", function(d) { return hcrow.indexOf(d.row) * cellSize; })
        .attr("class", function(d){return "cell cell-border cr"+(d.row-1)+" cc"+(d.col-1);})
        .attr("width", cellSize)
        .attr("height", cellSize)
        .style("fill", function(d) { return colorScale(d.value); })
        /* .on("click", function(d) {
               var rowtext=d3.select(".r"+(d.row-1));
               if(rowtext.classed("text-selected")==false){
                   rowtext.classed("text-selected",true);
               }else{
                   rowtext.classed("text-selected",false);
               }
        })*/
        .on("mouseover", function(d){
               //highlight text
               d3.select(this).classed("cell-hover",true);
               d3.selectAll(".rowLabel").classed("text-highlight",function(r,ri){ return ri==(d.row-1);});
               d3.selectAll(".colLabel").classed("text-highlight",function(c,ci){ return ci==(d.col-1);});
        
               //Update the tooltip position and value
               d3.select("#tooltip")
                 .style("left", (d3.event.pageX+10) + "px")
                 .style("top", (d3.event.pageY-10) + "px")
                 .select("#value")
                 .text(rowLabel[d.row-1]+","+colLabel[d.col-1]+"\ndata:"+d.value);  
               //Show the tooltip
               d3.select("#tooltip").classed("hidden", false);
        })
        .on("mouseout", function(){
               d3.select(this).classed("cell-hover",false);
               d3.selectAll(".rowLabel").classed("text-highlight",false);
               d3.selectAll(".colLabel").classed("text-highlight",false);
               d3.select("#tooltip").classed("hidden", true);
        })
        ;

  var legend = svg.selectAll(".legend")
      .data([-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10])
      .enter().append("g")
      .attr("class", "legend");
 
  console.log(height+(cellSize*2));
  legend.append("rect")
    .attr("x", function(d, i) { return legendElementWidth * i; })
    .attr("y", height+(cellSize*2))
    .attr("width", legendElementWidth)
    .attr("height", cellSize)
    .style("fill", function(d, i) { return colors[i]; });
 
  legend.append("text")
    .attr("class", "mono")
    .text(function(d) { return d; })
    .attr("width", legendElementWidth)
    .attr("x", function(d, i) { return legendElementWidth * i; })
    .attr("y", height + (cellSize*4));

// Change ordering of cells

  function sortbylabel(rORc,i,sortOrder){
       var t = svg.transition().duration(3000);
       var log2r=[];
       var sorted; // sorted is zero-based index
       d3.selectAll(".c"+rORc+i) 
         .filter(function(ce){
            log2r.push(ce.value);
          })
       ;
       if(rORc=="r"){ // sort log2ratio of a gene
         sorted=d3.range(col_number).sort(function(a,b){ if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});
         t.selectAll(".cell")
           .attr("x", function(d) { return sorted.indexOf(d.col-1) * cellSize; })
           ;
         t.selectAll(".colLabel")
          .attr("y", function (d, i) { return sorted.indexOf(i) * cellSize; })
         ;
       }else{ // sort log2ratio of a contrast
         sorted=d3.range(row_number).sort(function(a,b){if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});
         t.selectAll(".cell")
           .attr("y", function(d) { return sorted.indexOf(d.row-1) * cellSize; })
           ;
         t.selectAll(".rowLabel")
          .attr("y", function (d, i) { return sorted.indexOf(i) * cellSize; })
         ;
       }
  }

  //d3.select("#order").on("change",function(){
    this.$('#order').on("change",function(){
    order(this.value);
  });
  
  function order(value){
   if(value=="hclust"){
    var t = svg.transition().duration(3000);
    t.selectAll(".cell")
      .attr("x", function(d) { return hccol.indexOf(d.col) * cellSize; })
      .attr("y", function(d) { return hcrow.indexOf(d.row) * cellSize; })
      ;

    t.selectAll(".rowLabel")
      .attr("y", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })
      ;

    t.selectAll(".colLabel")
      .attr("y", function (d, i) { return hccol.indexOf(i+1) * cellSize; })
      ;

   }else if (value=="probecontrast"){
    var t = svg.transition().duration(3000);
    t.selectAll(".cell")
      .attr("x", function(d) { return (d.col - 1) * cellSize; })
      .attr("y", function(d) { return (d.row - 1) * cellSize; })
      ;

    t.selectAll(".rowLabel")
      .attr("y", function (d, i) { return i * cellSize; })
      ;

    t.selectAll(".colLabel")
      .attr("y", function (d, i) { return i * cellSize; })
      ;

   }else if (value=="probe"){
    var t = svg.transition().duration(3000);
    t.selectAll(".cell")
      .attr("y", function(d) { return (d.row - 1) * cellSize; })
      ;

    t.selectAll(".rowLabel")
      .attr("y", function (d, i) { return i * cellSize; })
      ;
   }else if (value=="contrast"){
    var t = svg.transition().duration(3000);
    t.selectAll(".cell")
      .attr("x", function(d) { return (d.col - 1) * cellSize; })
      ;
    t.selectAll(".colLabel")
      .attr("y", function (d, i) { return i * cellSize; })
      ;
   }
  }
  // 
  var sa=d3.select(".g3")
      .on("mousedown", function() {
          if( !d3.event.altKey) {
             d3.selectAll(".cell-selected").classed("cell-selected",false);
             d3.selectAll(".rowLabel").classed("text-selected",false);
             d3.selectAll(".colLabel").classed("text-selected",false);
          }
         var p = d3.mouse(this);
         sa.append("rect")
         .attr({
             rx      : 0,
             ry      : 0,
             class   : "selection",
             x       : p[0],
             y       : p[1],
             width   : 1,
             height  : 1
         })
      })
      .on("mousemove", function() {
         var s = sa.select("rect.selection");
      
         if(!s.empty()) {
             var p = d3.mouse(this),
                 d = {
                     x       : parseInt(s.attr("x"), 10),
                     y       : parseInt(s.attr("y"), 10),
                     width   : parseInt(s.attr("width"), 10),
                     height  : parseInt(s.attr("height"), 10)
                 },
                 move = {
                     x : p[0] - d.x,
                     y : p[1] - d.y
                 }
             ;
      
             if(move.x < 1 || (move.x*2<d.width)) {
                 d.x = p[0];
                 d.width -= move.x;
             } else {
                 d.width = move.x;       
             }
      
             if(move.y < 1 || (move.y*2<d.height)) {
                 d.y = p[1];
                 d.height -= move.y;
             } else {
                 d.height = move.y;       
             }
             s.attr(d);
      
                 // deselect all temporary selected state objects
             d3.selectAll('.cell-selection.cell-selected').classed("cell-selected", false);
             d3.selectAll(".text-selection.text-selected").classed("text-selected",false);

             d3.selectAll('.cell').filter(function(cell_d, i) {
                 if(
                     !d3.select(this).classed("cell-selected") && 
                         // inner circle inside selection frame
                     (this.x.baseVal.value)+cellSize >= d.x && (this.x.baseVal.value)<=d.x+d.width && 
                     (this.y.baseVal.value)+cellSize >= d.y && (this.y.baseVal.value)<=d.y+d.height
                 ) {
      
                     d3.select(this)
                     .classed("cell-selection", true)
                     .classed("cell-selected", true);

                     d3.select(".r"+(cell_d.row-1))
                     .classed("text-selection",true)
                     .classed("text-selected",true);

                     d3.select(".c"+(cell_d.col-1))
                     .classed("text-selection",true)
                     .classed("text-selected",true);
                 }
             });
         }
      })
      .on("mouseup", function() {
            // remove selection frame
         sa.selectAll("rect.selection").remove();
      
             // remove temporary selection marker class
         d3.selectAll('.cell-selection').classed("cell-selection", false);
         d3.selectAll(".text-selection").classed("text-selection",false);
      })
      .on("mouseout", function() {
         if(d3.event.relatedTarget.tagName=='html') {
                 // remove selection frame
             sa.selectAll("rect.selection").remove();
                 // remove temporary selection marker class
             d3.selectAll('.cell-selection').classed("cell-selection", false);
             d3.selectAll(".rowLabel").classed("text-selected",false);
             d3.selectAll(".colLabel").classed("text-selected",false);
         }
      });

            
        }
    });
    WidgetManager.register_widget_view("HeatmapView", HeatmapView);
});


    

In [ ]:

    

html_style = """
     <style>
      /* disable text selection */
      svg *::selection {
         background : transparent;
      }
     
      svg *::-moz-selection {
         background:transparent;
      } 
     
      svg *::-webkit-selection {
         background:transparent;
      }
      rect.selection {
        stroke          : #333;
        stroke-dasharray: 4px;
        stroke-opacity  : 0.5;
        fill            : transparent;
      }

      rect.cell-border {
        stroke: #eee;
        stroke-width:0.3px;   
      }

      rect.cell-selected {
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        position: fixed;
        width: 200px;
        height: auto;
        padding: 10px;
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        -moz-border-radius: 10px;
        border-radius: 10px;
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<div id="tooltip" class="hidden">
        <p><span id="value"></p>
</div>

 
  <select id="order">
  <option value="hclust">by cluster</option>
  <option value="probecontrast">by probe name and contrast name</option>
  <option value="probe">by probe name</option>
  <option value="contrast">by contrast name</option>
  <option value="custom">by log2 ratio</option>
  </select>
  </select>
<div id="chart" style='overflow:auto; width:960px; height:auto;'></div>
"""


    

In [ ]:

    

ome.query(GeneGroup).all()


    

In [ ]:

    

def gene_heatmap(gene_list, analysis_type=GeneExpressionData, dataset_type='%', 
                                                              strain1=ome.query(Strain).all(), strain2=None,
                                                              environments1=ome.query(InVivoEnvironment).all(), environments2=None):

    
    if analysis_type == GeneExpressionData:
        genes_data = {gene: ome.query(analysis_type).\
                              filter(and_(analysis_type.gene_id == gene.id,
                                          analysis_type.dataset_type.ilike(dataset_type))).all() for gene in gene_list}
        
        row_labels = ['_'.join([x.dataset_type,x.strain,x.carbon_source,x.nitrogen_source,x.electron_acceptor]) for x in genes_data.itervalues().next()]

    elif analysis_type == DifferentialGeneExpressionData:
        genes_data = {gene: ome.query(analysis_type).\
                              filter(and_(analysis_type.gene_id == gene.id,
                                          analysis_type.strain1.in_([x.name for x in strain1]),
                                          analysis_type.strain2.in_([x.name for x in strain2]),
                                          analysis_type.environment_id_1.in_([x.id for x in environments1]),
                                          analysis_type.environment_id_2.in_([x.id for x in environments2]))).all() for gene in gene_list}
        
        row_labels = ['_'.join([x.dataset_type,x.strain1,x.carbon_source1,x.nitrogen_source1,x.electron_acceptor1]) for x in genes_data.itervalues().next()]
    
    elif analysis_type == ChIPPeakGeneExpression:
        genes_data = {gene: ome.query(analysis_type).\
                              filter(and_(analysis_type.gene_id == gene.id,
                                          analysis_type.strain1.in_([x.name for x in strain1]),
                                          analysis_type.strain2.in_([x.name for x in strain2]),
                                          analysis_type.environment_id.in_([x.id for x in environments1]))).all() for gene in gene_list}
        
        genes_data = dict((k, v) for k, v in genes_data.iteritems() if v)
        row_labels = ['_'.join([x.dataset_type,x.strain1,x.carbon_source,x.nitrogen_source,x.electron_acceptor]) for x in genes_data.itervalues().next()]

        
    col_labels = [x.name for x in genes_data.keys()]

    heatmap_data = []
    for i,gene in enumerate(genes_data):
        for j,data in enumerate(genes_data[gene]):
            if data.value <= 0: value = 1.
            elif data.value > 1e6: value = 1e6
            else: value = data.value
            heatmap_data.append({"row": j+1, "col": i+1, "value": np.log(value)})

    hcrow = range(len(row_labels))
    hccol = range(len(col_labels))
    np.random.shuffle(hcrow)
    np.random.shuffle(hccol)
    
    return hcrow,hccol,row_labels,col_labels,heatmap_data
    #return {'hcrow': hcrow, 'hccol': hccol, 'row_labels':row_labels, 
    #                                        'col_labels':col_labels, 
    #                                        'heatmap_data':heatmap_data,
    #                                        'maxval' : max([x['value'] for x in heatmap_data]),
    #                                        'minval' : min([x['value'] for x in heatmap_data])}


    

In [ ]:

    

gene_list =  ome.query(GeneGroup).filter(GeneGroup.name.ilike('Peptid%')).first().genes
    
analysis_type = GeneExpressionData
dataset_type = '%'
all_data = ome.query(analysis_type).filter(and_(analysis_type.gene_name.in_(genes_data.index),
                                              analysis_type.dataset_type.ilike(dataset_type))).all()
    
datasets = set(['_'.join([x.dataset_type,x.strain,x.carbon_source,x.nitrogen_source,x.electron_acceptor]) for x in all_data])

genes_data = pd.DataFrame(index=[g.name for g in gene_list], columns=datasets)
for x in all_data:
    genes_data.ix[x.gene_name]['_'.join([x.dataset_type,x.strain,x.carbon_source,x.nitrogen_source,x.electron_acceptor])] = x.value

heatmap_data = []
for i,g in enumerate(genes_data.index):
    for j,c in enumerate(genes_data.columns):
        heatmap_data.append({"row": j+1, "col": i+1, "value": np.log(genes_data.ix[g][c])})


    

In [11]:

    

catabolic_genes = ome.query(GeneGroup).filter(GeneGroup.name.ilike('crp_cata%')).first().genes
hcrow,hccol,row_labels,col_labels,heatmap_data = gene_heatmap(catabolic_genes, 
                                                              analysis_type=ChIPPeakGeneExpression,
                                                              strain2=ome.query(Strain).all(),
                                                              environments2=ome.query(InVivoEnvironment).all())

##x = gene_heatmap(catabolic_genes, analysis_type=ChIPPeakGeneExpression,
#                                  strain2=ome.query(Strain).all(),
#                                  environments2=ome.query(InVivoEnvironment).all())


    

In [12]:

    

heatmap_data


    

    
Out[12]:





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 {'col': 1, 'row': 2, 'value': 0.034816799375362424},
 {'col': 1, 'row': 3, 'value': 0.60294344067060102},
 {'col': 1, 'row': 4, 'value': 0.94954367324764488},
 {'col': 1, 'row': 5, 'value': -0.39507576472420974},
 {'col': 1, 'row': 6, 'value': 0.52890912715626048},
 {'col': 2, 'row': 1, 'value': 0.0},
 {'col': 2, 'row': 2, 'value': 0.0},
 {'col': 2, 'row': 3, 'value': -1.4186935935665683},
 {'col': 2, 'row': 4, 'value': -1.4186935935665683},
 {'col': 2, 'row': 5, 'value': -0.10545274213277916},
 {'col': 2, 'row': 6, 'value': -0.10545274213277916},
 {'col': 2, 'row': 7, 'value': 0.0},
 {'col': 2, 'row': 8, 'value': 0.0},
 {'col': 2, 'row': 9, 'value': 0.0},
 {'col': 2, 'row': 10, 'value': 0.0},
 {'col': 2, 'row': 11, 'value': -1.4181318375686813},
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In [8]:

    

catabolic_heatmap_diagram = HeatmapWidget(html_style=html_style,
                                row_labels=row_labels, 
                                col_labels=col_labels,
                                hcrow=hcrow,
                                hccol=hccol,
                                minval= min([x['value'] for x in heatmap_data]),
                                maxval= max([x['value'] for x in heatmap_data]),
                                heatmap_data=heatmap_data)
display(catabolic_heatmap_diagram)


    

In [ ]:

    

catabolic_genes = ome.query(GeneGroup).filter(GeneGroup.name.ilike('Glycero%')).first().genes
hcrow,hccol,row_labels,col_labels,heatmap_data = gene_heatmap(catabolic_genes, 
                                                              analysis_type=DifferentialGeneExpressionData,
                                                              strain2=ome.query(Strain).all(),
                                                              environments2=ome.query(InVivoEnvironment).all())


    

In [9]:

    

catabolic_heatmap_diagram = HeatmapWidget(html_style=html_style,
                                row_labels=row_labels, 
                                col_labels=col_labels,
                                hcrow=hcrow,
                                hccol=hccol,
                                minval= min([x['value'] for x in heatmap_data]),
                                maxval= max([x['value'] for x in heatmap_data]),
                                heatmap_data=heatmap_data)
display(catabolic_heatmap_diagram)


    

In [10]:

    

anabolic_genes = ome.query(GeneGroup).filter(GeneGroup.name.ilike('crp_anab%')).first().genes
hcrow,hccol,row_labels,col_labels,heatmap_data = gene_heatmap(anabolic_genes,dataset_type='rna_seq_experiment')


    

In [11]:

    

anabolic_heatmap_diagram = HeatmapWidget(html_style=html_style,
                                row_labels=row_labels, 
                                col_labels=col_labels,
                                hcrow=hcrow,
                                hccol=hccol,
                                minval= min([x['value'] for x in heatmap_data]),
                                maxval= max([x['value'] for x in heatmap_data]),
                                heatmap_data=heatmap_data)
display(anabolic_heatmap_diagram)


    

In [6]:

    

chemiosmotic_genes = ome.query(GeneGroup).filter(GeneGroup.name.ilike('crp_chemi%')).first().genes
chem_hcrow,chem_hccol,chem_row_labels,chem_col_labels,chem_heatmap_data = gene_heatmap(chemiosmotic_genes,dataset_type='rna_seq_experiment')


    

In [7]:

    

chem_heatmap_diagram = HeatmapWidget(html_style=html_style,
                                row_labels=chem_row_labels, 
                                col_labels=chem_col_labels,
                                hcrow=chem_hcrow,
                                hccol=chem_hccol,
                                minval= min([x['value'] for x in chem_heatmap_data]),
                                maxval= max([x['value'] for x in chem_heatmap_data]),
                                heatmap_data=chem_heatmap_data)
display(chem_heatmap_diagram)


    

In [ ]: