# cistrans_coffee # # Interactive cis-trans eQTL plot # # In top figure, x-axis corresponds to marker location, y-axis is # genomic position of probes on a gene expression microarray Each # plotted point is an inferred eQTL with LOD > 10; opacity corresponds # to LOD score, though all LOD > 25 are made fully opaque. # # Hover over a point to see probe ID and LOD score; also highlighted # are any other eQTL for that probe. Click on the point to see LOD # curves below. # # If a clicked probe is in known gene, title in lower plot is a link # to the Mouse Genome Informatics (MGI) site at the Jackson Lab. # # Hover over markers in LOD curve plot to view marker names; click on # a marker to see the phenotype-vs-genotype plot to right. In # geno-vs-pheno plot, hover over average to view value, and hover over # points to view individual IDs. # # This is awful code; I just barely know what I'm doing. # function that does all of the work draw_cistrans = (data) -> # dimensions of panels w = [500, 300] h = [w[0], 200] pad = {left:60, top:40, right:40, bottom: 40, inner: 10} w = [w[0], w[0] + w[1] + pad.left + pad.right, w[1]] h = [h[0], h[1], h[0]] left = [pad.left, pad.left, pad.left + w[0] + pad.right + pad.left] top = [pad.top, pad.top + h[0] + pad.bottom + pad.top, pad.top] right = [] bottom = [] for i of left right[i] = left[i] + w[i] bottom[i] = top[i] + h[i] totalw = right[2] + pad.right totalh = bottom[1] + pad.bottom # Size of rectangles in top-left panel peakRad = 2 bigRad = 5 # gap between chromosomes in lower plot chrGap = 8 # transition speeds slowtime = 1000 fasttime = 250 # height of marker ticks in lower panel tickHeight = (bottom[1] - top[1])*0.02 # jitter amounts for PXG plot jitterAmount = (right[2] - left[2])/50 jitter = [] for i of data.individuals jitter[i] = (2.0*Math.random()-1.0) * jitterAmount nodig = d3.format(".0f") onedig = d3.format(".1f") twodig = d3.format(".2f") # colors definitions lightGray = d3.rgb(200, 200, 200) darkGray = d3.rgb(170, 170, 170) darkblue = "darkslateblue" darkgreen = "darkslateblue" # "darkgreen" # <- I didn't like having separate colors for each chr in cis/trans plot pink = "hotpink" altpink = "#E9CFEC" purple = "#8C4374" darkred = "crimson" labelcolor = "white" titlecolor = "Wheat" maincolor = "Wheat" # calculate X and Y scales, using cM positions totalChrLength = 0; for c in data.chrnames data.chr[c].length_cM = data.chr[c].end_cM - data.chr[c].start_cM totalChrLength += data.chr[c].length_cM chrXScale = {} chrYScale = {} curXPixel = left[0]+peakRad curYPixel = bottom[0]-peakRad for c in data.chrnames data.chr[c].length_pixel = Math.round((w[0]-peakRad*2) * data.chr[c].length_cM / totalChrLength) data.chr[c].start_Xpixel = curXPixel data.chr[c].end_Xpixel = curXPixel + data.chr[c].length_pixel - 1 data.chr[c].start_Ypixel = curYPixel data.chr[c].end_Ypixel = curYPixel - (data.chr[c].length_pixel - 1) chrXScale[c] = d3.scale.linear() .domain([data.chr[c].start_cM, data.chr[c].end_cM]) .range([data.chr[c].start_Xpixel, data.chr[c].end_Xpixel]) .clamp(true) chrYScale[c] = d3.scale.linear() .domain([data.chr[c].start_cM, data.chr[c].end_cM]) .range([data.chr[c].start_Ypixel, data.chr[c].end_Ypixel]) .clamp(true) curXPixel += data.chr[c].length_pixel curYPixel -= data.chr[c].length_pixel # slight adjustments top[0] = data.chr["X"].end_Ypixel-peakRad h[0] = bottom[0] - top[0] data.chr["1"].start_Xpixel = left[0] data.chr["1"].start_Ypixel = bottom[0] data.chr["X"].end_Xpixel = right[0] data.chr["X"].end_Ypixel = top[0] # chr scales in lower figure chrLowXScale = {} cur = Math.round(pad.left + chrGap/2) for c in data.chrnames data.chr[c].start_lowerXpixel = cur data.chr[c].end_lowerXpixel = cur + Math.round((w[1]-chrGap*(data.chrnames.length))/totalChrLength*data.chr[c].length_cM) chrLowXScale[c] = d3.scale.linear() .domain([data.chr[c].start_cM, data.chr[c].end_cM]) .range([data.chr[c].start_lowerXpixel, data.chr[c].end_lowerXpixel]) cur = data.chr[c].end_lowerXpixel + chrGap # X scales for PXG plot # autosome in intercross: 6 cases pxgXscaleA = d3.scale.ordinal() .domain(d3.range(6)) .rangePoints([left[2], right[2]], 1) # X chromosome in intercross (both sexes, one direction): 4 cases pxgXscaleX = d3.scale.ordinal() .domain(d3.range(4)) .rangePoints([left[2], right[2]], 1) # swap genotypes in females on X chromsome for m in data.markers continue unless data.pmark[m].chr == "X" for g,i in data.geno[m] if data.sex[i] == 0 newg = 0 switch g when -1 then newg = -2 when -2 then newg = -1 when +1 then newg = +2 when +2 then newg = +1 data.geno[m][i] = newg # create SVG svg = d3.select("div#cistrans").append("svg") .attr("width", totalw) .attr("height", totalh) # gray backgrounds for j of left svg.append("rect") .attr("x", left[j]) .attr("y", top[j]) .attr("height", h[j]) .attr("width", w[j]) .attr("class", "innerBox") # add dark gray rectangles to define chromosome boundaries as checkerboard checkerboard = svg.append("g").attr("id", "checkerboard") for ci,i in data.chrnames for cj,j in data.chrnames if((i + j) % 2 == 0) checkerboard.append("rect") .attr("x", data.chr[ci].start_Xpixel) .attr("width", data.chr[ci].end_Xpixel - data.chr[ci].start_Xpixel) .attr("y", data.chr[cj].end_Ypixel) .attr("height", data.chr[cj].start_Ypixel - data.chr[cj].end_Ypixel) .attr("stroke", "none") .attr("fill", darkGray) .style("pointer-events", "none") # same in lower panel checkerboard2 = svg.append("g").attr("id", "checkerboard2") for ci,i in data.chrnames if(i % 2 == 0) checkerboard2.append("rect") .attr("x", data.chr[ci].start_lowerXpixel - chrGap/2) .attr("width", data.chr[ci].end_lowerXpixel - data.chr[ci].start_lowerXpixel + chrGap) .attr("y", top[1]) .attr("height", h[1]) .attr("stroke", "none") .attr("fill", darkGray) .style("pointer-events", "none") # chromosome labels axislabels = svg.append("g").attr("id", "axislabels").style("pointer-events", "none") axislabels.append("g").attr("id", "topleftX").selectAll("empty") .data(data.chrnames) .enter() .append("text") .text((d) -> d) .attr("x", (d) -> (data.chr[d].start_Xpixel + data.chr[d].end_Xpixel)/2) .attr("y", bottom[0] + pad.bottom*0.3) .attr("fill", labelcolor) axislabels.append("g").attr("id", "topleftY") .selectAll("empty") .data(data.chrnames) .enter() .append("text") .text((d) -> d) .attr("x", left[0] - pad.left*0.15) .attr("y", (d) -> (data.chr[d].start_Ypixel + data.chr[d].end_Ypixel)/2) .style("text-anchor", "end") .attr("fill", labelcolor) axislabels.append("g").attr("id", "bottomX").selectAll("empty") .data(data.chrnames) .enter() .append("text") .text((d) -> d) .attr("x", (d) -> (data.chr[d].start_lowerXpixel + data.chr[d].end_lowerXpixel)/2) .attr("y", bottom[1] + pad.bottom*0.3) .attr("fill", labelcolor) axislabels.append("text") .text("Marker position (cM)") .attr("x", (left[0] + right[0])/2) .attr("y", bottom[0] + pad.bottom*0.75) .attr("fill", titlecolor) .attr("text-anchor", "middle") axislabels.append("text") .text("Position (cM)") .attr("x", (left[1] + right[1])/2) .attr("y", bottom[1] + pad.bottom*0.75) .attr("fill", titlecolor) .attr("text-anchor", "middle") xloc = left[0] - pad.left*0.65 yloc = (top[0] + bottom[0])/2 axislabels.append("text") .text("Probe position (cM)") .attr("x", xloc) .attr("y", yloc) .attr("transform", "rotate(270,#{xloc},#{yloc})") .style("text-anchor", "middle") .attr("fill", titlecolor) xloc = left[1] - pad.left*0.65 yloc = (top[1] + bottom[1])/2 axislabels.append("text") .text("LOD score") .attr("x", xloc) .attr("y", yloc) .attr("transform", "rotate(270,#{xloc},#{yloc})") .style("text-anchor", "middle") .attr("fill", titlecolor) # overall maximum lod score maxlod = d3.max(data.peaks, (d) -> d.lod) # sort peaks to have increasing LOD score data.peaks = data.peaks.sort (a,b) -> return if a.lod < b.lod then -1 else +1 # LOD score controls opacity Zscale = d3.scale.linear() .domain([0, 25]) .range([0, 1]) # tool tips using https://github.com/Caged/d3-tip # [slightly modified in https://github.com/kbroman/d3-tip] eqtltip = d3.svg.tip() .orient("right") .padding(3) .text((z) -> "#{z.probe} (LOD = #{onedig(z.lod)})") .attr("class", "d3-tip") .attr("id", "eqtltip") martip = d3.svg.tip() .orient("right") .padding(3) .text((z) -> z) .attr("class", "d3-tip") .attr("id", "martip") indtip = d3.svg.tip() .orient("right") .padding(3) .text((d,i) -> data.individuals[i]) .attr("class", "d3-tip") .attr("id", "indtip") efftip = d3.svg.tip() .orient("right") .padding(3) .text((d) -> twodig(d)) .attr("class", "d3-tip") .attr("id", "efftip") # create indices to lod scores, split by chromosome cur = 0 for c in data.chrnames for p in data.pmarknames[c] data.pmark[p].index = cur cur++ # function for drawing lod curve for probe draw_probe = (probe_data) -> # delete all related stuff svg.selectAll(".probe_data").remove() d3.select("text#pxgtitle").text("") svg.selectAll(".plotPXG").remove() # find marker with maximum LOD score maxlod = -1 maxlod_marker = null for m in data.markers lod = probe_data.lod[data.pmark[m].index] if maxlod < lod maxlod = lod maxlod_marker = m # y-axis scale lodcurve_yScale = d3.scale.linear() .domain([0, maxlod*1.05]) .range([bottom[1], top[1]]) # y-axis yaxis = svg.append("g").attr("class", "probe_data").attr("id", "loweryaxis") ticks = lodcurve_yScale.ticks(6) yaxis.selectAll("empty") .data(ticks) .enter() .append("line") .attr("y1", (d) -> lodcurve_yScale(d)) .attr("y2", (d) -> lodcurve_yScale(d)) .attr("x1", left[1]) .attr("x2", right[1]) .attr("stroke", "white") .attr("stroke-width", "1") yaxis.selectAll("empty") .data(ticks) .enter() .append("text") .text((d) -> return if maxlod > 10 then nodig(d) else onedig(d)) .attr("y", (d) -> lodcurve_yScale(d)) .attr("x", left[1] - pad.left*0.1) .style("text-anchor", "end") .attr("fill", labelcolor) yaxis.append("line") .attr("y1", lodcurve_yScale(5)) .attr("y2", lodcurve_yScale(5)) .attr("x1", left[1]) .attr("x2", right[1]) .attr("stroke", purple) .attr("stroke-width", "1") .attr("stroke-dasharray", "2,2") # lod curves by chr lodcurve = (c) -> d3.svg.line() .x((p) -> chrLowXScale[c](data.pmark[p].pos_cM)) .y((p) -> lodcurve_yScale(probe_data.lod[data.pmark[p].index])) curves = svg.append("g").attr("id", "curves").attr("class", "probe_data") for c in data.chrnames curves.append("path") .datum(data.pmarknames[c]) .attr("d", lodcurve(c)) .attr("class", "thickline") .attr("stroke", darkblue) .style("pointer-events", "none") .attr("fill", "none") # title titletext = probe_data.probe probeaxes = svg.append("g").attr("id", "probe_data_axes").attr("class", "probe_data") gene = data.probes[probe_data.probe].gene ensembl = "http://www.ensembl.org/Mus_musculus/Search/Details?db=core;end=1;idx=Gene;species=Mus_musculus;q=#{gene}" mgi = "http://www.informatics.jax.org/searchtool/Search.do?query=#{gene}" if gene isnt null titletext += " (#{gene})" xlink = probeaxes.append("a").attr("xlink:href", mgi) xlink.append("text") .text(titletext) .attr("x", (left[1]+right[1])/2) .attr("y", top[1] - pad.top/2) .attr("fill", maincolor) .style("font-size", "18px") else probeaxes.append("text") .text(titletext) .attr("x", (left[1]+right[1])/2) .attr("y", top[1] - pad.top/2) .attr("fill", maincolor) .style("font-size", "18px") # black border svg.append("rect").attr("class", "probe_data") .attr("x", left[1]) .attr("y", top[1]) .attr("height", h[1]) .attr("width", w[1]) .attr("class", "outerBox") # point at probe svg.append("circle") .attr("class", "probe_data") .attr("id", "probe_circle") .attr("cx", chrLowXScale[data.probes[probe_data.probe].chr](data.probes[probe_data.probe].pos_cM)) .attr("cy", top[1]) .attr("r", bigRad) .attr("fill", pink) .attr("stroke", darkblue) .attr("stroke-width", 1) .attr("opacity", 1) svg.append("text") .attr("id", "pxgtitle") .attr("x", (left[2]+right[2])/2) .attr("y", pad.top/2) .text("") .attr("fill", maincolor) # keep track of clicked marker markerClick = {} for m in data.markers markerClick[m] = 0 lastMarker = "" # dots at markers on LOD curves svg.append("g").attr("id", "markerCircle").attr("class", "probe_data") .selectAll("empty") .data(data.markers) .enter() .append("circle") .attr("class", "probe_data") .attr("id", (td) -> "marker_#{td}") .attr("cx", (td) -> chrLowXScale[data.pmark[td].chr](data.pmark[td].pos_cM)) .attr("cy", (td) -> lodcurve_yScale(probe_data.lod[data.pmark[td].index])) .attr("r", bigRad) .attr("fill", purple) .attr("stroke", "none") .attr("stroke-width", "2") .attr("opacity", 0) .on("mouseover", (td) -> d3.select(this).attr("opacity", 1) unless markerClick[td] martip.call(this,td)) .on "mouseout", (td) -> d3.select(this).attr("opacity", markerClick[td]) d3.selectAll("#martip").remove() .on "click", (td) -> pos = data.pmark[td].pos_cM chr = data.pmark[td].chr title = "#{td} (chr #{chr}, #{onedig(pos)} cM)" d3.select("text#pxgtitle").text(title) if lastMarker is "" plotPXG td else markerClick[lastMarker] = 0 d3.select("circle#marker_#{lastMarker}").attr("opacity", 0).attr("fill",purple).attr("stroke","none") revPXG td lastMarker = td markerClick[td] = 1 d3.select(this).attr("opacity", 1).attr("fill",altpink).attr("stroke",purple) draw_pxgXaxis = (means, genotypes, chr, sexcenter, male) -> pxgXaxis.selectAll("line.PXGvert") .data(means) .enter() .append("line") .attr("class", "PXGvert") .attr("y1", top[2]) .attr("y2", bottom[2]) .attr("x1", (d,i) -> return if(chr is "X") then pxgXscaleX(i) else pxgXscaleA(i)) .attr("x2", (d,i) -> return if(chr is "X") then pxgXscaleX(i) else pxgXscaleA(i)) .attr("stroke", darkGray) .attr("fill", "none") .attr("stroke-width", "1") pxgXaxis.selectAll("line.PXGvert") .data(means) .transition().duration(fasttime) .attr("x1", (d,i) -> return if(chr is "X") then pxgXscaleX(i) else pxgXscaleA(i)) .attr("x2", (d,i) -> return if(chr is "X") then pxgXscaleX(i) else pxgXscaleA(i)) pxgXaxis.selectAll("line.PXGvert") .data(means) .exit().remove() pxgXaxis.selectAll("text.PXGgeno") .data(genotypes) .enter() .append("text") .attr("class", "PXGgeno") .text((d) -> d) .attr("y", bottom[2] + pad.bottom*0.25) .attr("x", (d,i) -> return if(chr is "X") then pxgXscaleX(i) else pxgXscaleA(i)) .attr("fill", labelcolor) pxgXaxis.selectAll("text.PXGgeno") .data(genotypes) .transition().duration(fasttime) .text((d) -> d) .attr("x", (d,i) -> return if(chr is "X") then pxgXscaleX(i) else pxgXscaleA(i)) pxgXaxis.selectAll("text.PXGgeno") .data(genotypes) .exit().remove() pxgXaxis.selectAll("text.PXGsex") .data(["Female", "Male"]) .enter() .append("text") .attr("class", "PXGsex") .attr("id", "sextext") .text((d) -> d) .attr("y", bottom[j] + pad.bottom*0.75) .attr("x", (d, i) -> sexcenter[i]) .attr("fill", labelcolor) pxgXaxis.selectAll("text.PXGsex") .data(["Female", "Male"]) .transition().duration(fasttime) .attr("x", (d, i) -> sexcenter[i]) pxgXaxis.selectAll("text.PXGsex") .data(["Female", "Male"]) .exit().remove() # add line segments meanmarks.selectAll("line.PXGmeans") .data(means) .enter() .append("line") .attr("class", "PXGmeans") .attr("x1", (d,i) -> return if chr is "X" then pxgXscaleX(i)-jitterAmount*3 else pxgXscaleA(i)-jitterAmount*3) .attr("x2", (d,i) -> return if chr is "X" then pxgXscaleX(i)+jitterAmount*3 else pxgXscaleA(i)+jitterAmount*3) .attr("y1", (d) -> pxgYscale(d)) .attr("y2", (d) -> pxgYscale(d)) .attr("stroke", (d,i) -> return if male[i] then darkblue else darkred) .attr("stroke-width", 4) .on("mouseover", efftip) .on("mouseout", -> d3.selectAll("#efftip").remove()) meanmarks.selectAll("line.PXGmeans") .data(means) .transition().duration(slowtime) .attr("x1", (d,i) -> return if chr is "X" then pxgXscaleX(i)-jitterAmount*3 else pxgXscaleA(i)-jitterAmount*3) .attr("x2", (d,i) -> return if chr is "X" then pxgXscaleX(i)+jitterAmount*3 else pxgXscaleA(i)+jitterAmount*3) .attr("y1", (d) -> pxgYscale(d)) .attr("y2", (d) -> pxgYscale(d)) .attr("stroke", (d,i) -> return if male[i] then darkblue else darkred) meanmarks.selectAll("line.PXGmeans") .data(means).exit().remove() pxgYscale = null pxgXaxis = svg.append("g").attr("class", "probe_data").attr("id", "pxg_xaxis") pxgYaxis = svg.append("g").attr("class", "probe_data").attr("class", "plotPXG").attr("id", "pxg_yaxis") meanmarks = svg.append("g").attr("id", "pxgmeans").attr("class", "probe_data") plotPXG = (marker) -> pxgYscale = d3.scale.linear() .domain([d3.min(probe_data.pheno), d3.max(probe_data.pheno)]) .range([bottom[2]-pad.inner, top[2]+pad.inner]) pxgticks = pxgYscale.ticks(8) pxgYaxis.selectAll("empty") .data(pxgticks) .enter() .append("line") .attr("y1", (d) -> pxgYscale(d)) .attr("y2", (d) -> pxgYscale(d)) .attr("x1", left[2]) .attr("x2", right[2]) .attr("stroke", "white") .attr("stroke-width", "1") pxgYaxis.selectAll("empty") .data(pxgticks) .enter() .append("text") .text((d) -> twodig(d)) .attr("y", (d) -> pxgYscale(d)) .attr("x", left[2] - pad.left*0.1) .style("text-anchor", "end") .attr("fill", labelcolor) # calculate group averages chr = data.pmark[marker].chr if(chr is "X") means = [0,0,0,0] n = [0,0,0,0] male = [0,0,1,1] genotypes = ["BR", "RR", "BY", "RY"] sexcenter = [(pxgXscaleX(0) + pxgXscaleX(1))/2, (pxgXscaleX(2) + pxgXscaleX(3))/2] else means = [0,0,0,0,0,0] n = [0,0,0,0,0,0] male = [0,0,0,1,1,1] genotypes = ["BB", "BR", "RR", "BB", "BR", "RR"] sexcenter = [pxgXscaleA(1), pxgXscaleA(4)] for i of data.individuals g = Math.abs(data.geno[marker][i]) sx = data.sex[i] if(data.pmark[marker].chr is "X") x = sx*2+g-1 else x = sx*3+g-1 means[x] += probe_data.pheno[i] n[x]++ for i of means means[i] /= n[i] draw_pxgXaxis(means, genotypes, chr, sexcenter, male) svg.append("g").attr("id", "plotPXG").attr("class", "probe_data").attr("id","PXGpoints").selectAll("empty") .data(probe_data.pheno) .enter() .append("circle") .attr("class", "plotPXG") .attr("cx", (d,i) -> g = Math.abs(data.geno[marker][i]) sx = data.sex[i] if(data.pmark[marker].chr is "X") return pxgXscaleX(sx*2+g-1)+jitter[i] pxgXscaleA(sx*3+g-1)+jitter[i]) .attr("cy", (d) -> pxgYscale(d)) .attr("r", peakRad) .attr("fill", (d,i) -> g = data.geno[marker][i] return pink if g < 0 darkGray) .attr("stroke", (d,i) -> g = data.geno[marker][i] return purple if g < 0 "black") .attr("stroke-width", (d,i) -> g = data.geno[marker][i] return "2" if g < 0 "1") .on "mouseover", (d,i) -> d3.select(this).attr("r", bigRad) indtip.call(this, d, i) .on "mouseout", -> d3.selectAll("#indtip").remove() d3.select(this).attr("r", peakRad) revPXG = (marker) -> # calculate group averages chr = data.pmark[marker].chr if(chr is "X") means = [0,0,0,0] n = [0,0,0,0] male = [0,0,1,1] genotypes = ["BR", "RR", "BY", "RY"] sexcenter = [(pxgXscaleX(0) + pxgXscaleX(1))/2, (pxgXscaleX(2) + pxgXscaleX(3))/2] else means = [0,0,0,0,0,0] n = [0,0,0,0,0,0] male = [0,0,0,1,1,1] genotypes = ["BB", "BR", "RR", "BB", "BR", "RR"] sexcenter = [pxgXscaleA(1), pxgXscaleA(4)] for i of data.individuals g = Math.abs(data.geno[marker][i]) sx = data.sex[i] if(data.pmark[marker].chr is "X") x = sx*2+g-1 else x = sx*3+g-1 means[x] += probe_data.pheno[i] n[x]++ for i of means means[i] /= n[i] draw_pxgXaxis(means, genotypes, chr, sexcenter, male) svg.selectAll("circle.plotPXG") .transition().duration(slowtime) .attr("cx", (d,i) -> g = Math.abs(data.geno[marker][i]) sx = data.sex[i] if(data.pmark[marker].chr is "X") return pxgXscaleX(sx*2+g-1)+jitter[i] pxgXscaleA(sx*3+g-1)+jitter[i]) .attr("fill", (d,i) -> g = data.geno[marker][i] return pink if g < 0 darkGray) .attr("stroke", (d,i) -> g = data.geno[marker][i] return purple if g < 0 "black") .attr("stroke-width", (d,i) -> g = data.geno[marker][i] return "2" if g < 0 "1") # initially select the marker with maximum LOD lastMarker = maxlod_marker markerClick[lastMarker] = 1 d3.select("circle#marker_#{lastMarker}").attr("opacity", 1).attr("fill",altpink).attr("stroke",purple) pos = data.pmark[lastMarker].pos_cM chr = data.pmark[lastMarker].chr title = "#{lastMarker} (chr #{chr}, #{onedig(pos)} cM)" d3.select("text#pxgtitle").text(title) plotPXG(lastMarker) chrindex = {} for c,i in data.chrnames chrindex[c] = i # circles at eQTL peaks peaks = svg.append("g").attr("id", "peaks") .selectAll("empty") .data(data.peaks) .enter() .append("circle") .attr("class", (d) -> "probe_#{d.probe}") .attr("cx", (d) -> chrXScale[d.chr](d.pos_cM)) .attr("cy", (d) -> chrYScale[data.probes[d.probe].chr](data.probes[d.probe].pos_cM)) .attr("r", peakRad) .attr("stroke", "none") .attr("fill", (d) -> return if(chrindex[d.chr] % 2 is 0) then darkblue else darkgreen) .attr("opacity", (d) -> Zscale(d.lod)) .on "mouseover", (d) -> d3.selectAll("circle.probe_#{d.probe}") .attr("r", bigRad) .attr("fill", pink) .attr("stroke", darkblue) .attr("stroke-width", 1) .attr("opacity", 1) eqtltip.call(this,d) .on "mouseout", (d) -> d3.selectAll("circle.probe_#{d.probe}") .attr("r", peakRad) .attr("fill", (d) -> return if(chrindex[d.chr] % 2 is 0) then darkblue else darkgreen) .attr("stroke", "none") .attr("opacity", (d) -> Zscale(d.lod)) d3.selectAll("#eqtltip").remove() .on "click", (d) -> d3.json("Data/probe_data/probe#{d.probe}.json", draw_probe) # initial set of LOD curves at the bottom d3.json("Data/probe_data/probe517761.json", draw_probe) # black borders for j of left svg.append("rect") .attr("x", left[j]) .attr("y", top[j]) .attr("height", h[j]) .attr("width", w[j]) .attr("class", "outerBox") # load json file and call draw function d3.json("Data/islet_eqtl.json", draw_cistrans)