figure1 = vegalite (plot_specs[plot_type])
fig1data = {
let in_set = new Set (_.map (table1, (d)=>d.Codon));
return _.filter (siteTableData[0], (x)=>in_set.has (x.Codon));
}
plot_options = [["Joy Plot", (d)=>1], ["Evidence ratio for ω>1 (constrained)", (d)=>1], ["Evidence ratio for ω>1 (optimized)", (d)=>1], ["Synonymous rates", (d)=>srv_rate_classes > 0], ["Support for positive selection", (d)=>1], ["Support for 2H", (d)=>mh_rates['DH']], ["Support for 3H", (d)=>mh_rates['TH']], ["Support for 2H+3H", (d)=>mh_rates['DH'] && mh_rates['TH']], ["LogL difference", (d)=>results_json && results_json_compare], ["LogL difference scatter", (d)=>results_json && results_json_compare], ["LR difference", (d)=>results_json && results_json_compare], ["LR difference scatter", (d)=>results_json && results_json_compare], ["Compare ER", (d)=>results_json && results_json_compare], ["Compare BS support", (d)=>results_json && results_json_compare], ["LR difference CDS", (d)=>results_json && results_json_compare]]
plot_specs = (
{
"Evidence ratio for ω>1 (constrained)" : {
"width": 800, "height": 150,
"vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {
return ERPlot (fig1data, d, 70, "ER (constrained)")
})},
"Evidence ratio for ω>1 (optimized)" : {
"width": 800, "height": 150,
"vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {
return ERPlot (fig1data, d, 70, "ER (optimized null)")
})},
"Synonymous rates" : {
"width": 800, "height": 150,
"vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {
return SRVPlot (fig1data, d, 70, "SRV posterior mean", srv_hmm ? "SRV viterbi" : null)
})},
"Support for positive selection" : {
"vconcat" : _.map (_.range (1, results_json.input["number of sites"], 1000), (d)=> {
return BSPosteriorPlot (bsPositiveSelection, d, 1000)
})},
"Joy Plot" : {
"vconcat" : _.map (_.range (1, results_json.input["number of sites"], 7000), (d)=> {
return BSPosteriorPlot_Joy (bsPositiveSelection, d, 7000)
})},
"Support for 2H" : {
"vconcat" : _.map (_.range (1, results_json.input["number of sites"], 70), (d)=> {
return BSPosteriorPlot (twoHBranchSite, d, 70)
})},
"Support for 3H" : {
"vconcat" : _.map (_.range (1, results_json.input["number of sites"], 70), (d)=> {
return BSPosteriorPlot (threeHBranchSite, d, 70)
})},
"Support for 2H+3H" : {
"vconcat" : _.map (_.range (1, results_json.input["number of sites"], 70), (d)=> {
return BSPosteriorPlot (multiHBranchSite, d, 70)
})},
"Compare ER" : ERPlotComp (fig1data, scatter_plot[0], scatter_plot[1], scatter_plot[2]),
"Compare BS support" : BSPlotComp (compBSModels, scatter_plot[2]),
"LR difference CDS" : cdsQuant (fig1data, "Diff LR"),
"LogL difference" : {
"width": 800, "height": 150,
"vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {
return ERPlot (fig1data, d, 70, "Diff logl")
})},
"LogL difference scatter" : ScatterPlotComp (fig1data, "LogL ref", "LogL comp", scatter_plot[2]),
"LR difference scatter" : ScatterPlotComp (fig1data, "LR ref", "LR comp", scatter_plot[2]),
"LR difference" : {
"width": 800, "height": 150,
"vconcat" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {
return ERPlot (fig1data, d, 70, "Diff LR")
})},
}
)
mh_rates = ({
'DH' : _.get (results_json, ['fits', 'Unconstrained model','Rate Distributions', 'rate at which 2 nucleotides are changed instantly within a single codon']),
'TH' : _.get (results_json, ['fits', 'Unconstrained model','Rate Distributions', 'rate at which 3 nucleotides are changed instantly within a single codon'])
})
BSPosteriorPlot = (data, from, step)=> {
//console.log(data);
const branch_order = treeNodeOrdering (0);
let spec = {
"width": 2400,
"height" : 2400,
"data" : {"values" :
_.filter (data, (d,i)=> d.Codon >= from && d.Codon <= from + step),
},
"encoding": {
"x": {
"field": "Codon",
"type" : "nominal",
"axis": {"grid" : false,
"titleFontSize" : 14,
"title" : "Codon",
"labelFontSize" : 8}
},
"y": {
"field": "Branch",
"scale" : {"domain" : branch_order},
"type" : "nominal",
"axis": {"grid" : false,
"titleFontSize" : 14,
"title" : "Branch",
"labelFontSize" : 8}
}
},
"layer": [
{
"mark": {"type": "rect",
"size" : 2,
"color" : "black",
"opacity" : 1.0,
"tooltip" : true},
"encoding": {
"color": {
"field": "ER",
"type" : "quantitative",
"legend" : {"orient" : "top"},
"scale" : {"type" : "log",
"scheme": "redblue"}
}
}
}
]
};
return spec;
}
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
