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Sample network clustering vis
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body | |
{ | |
/*font-family: sans-serif;*/ | |
font-family: 'Helvetica', 'Arial', sans-serif; | |
font-size: 12px; | |
height:100%; | |
} | |
body a:link | |
{ | |
text-decoration: none; | |
} | |
html | |
{ | |
height:100%; | |
} | |
svg | |
{ | |
display:block; | |
} | |
#graphholder | |
{ | |
height: 600px; | |
} | |
/* styling of the network */ | |
.edge line | |
{ | |
stroke: #999; | |
stroke-opacity: 0.6; | |
} | |
.node circle | |
{ | |
stroke: #000; | |
stroke-width: 1.5px; | |
} | |
.hull { | |
fill: steelblue; | |
stroke: black; | |
stroke-width: 2px; | |
stroke-linejoin: round; | |
} | |
/* tool tip for node labels */ | |
.toolTip | |
{ | |
pointer-events: none; | |
position: absolute; | |
display: none; | |
min-width: 50px; | |
height: auto; | |
background: none repeat scroll 0 0 #ffffff; | |
padding: 9px 14px 6px 14px; | |
border-radius: 4px; | |
text-align: left; | |
line-height: 1.3; | |
color: #5B6770; | |
box-shadow: 0px 3px 9px rgba(0, 0, 0, .15); | |
} | |
/* info bubble for the clusters */ | |
.clustTip | |
{ | |
pointer-events: none; | |
position: absolute; | |
display: none; | |
min-width: 50px; | |
height: auto; | |
background: none repeat scroll 0 0 #ffffff; | |
padding: 9px 14px 6px 14px; | |
border-radius: 4px; | |
text-align: left; | |
line-height: 1.3; | |
color: #5B6770; | |
box-shadow: 0px 3px 9px rgba(0, 0, 0, .15); | |
} | |
.ctname | |
{ | |
font-weight:700; | |
font-size:12px; | |
margin:0; | |
margin-bottom:.5em; | |
padding-bottom: .5em; | |
border-bottom:1px dashed #ccc; | |
} | |
.ctnum | |
{ | |
font-weight:800; | |
font-size: 12px; | |
} | |
.ctContain | |
{ | |
width:100%; | |
padding-right:5px; | |
padding-left:5px; | |
} | |
.ctContain ul | |
{ | |
list-style: none; | |
padding:0px; | |
font-size:10px; | |
} | |
.qval | |
{ | |
color: #e85c86; | |
} | |
.ctItems li | |
{ | |
border-bottom:1px dashed #ccc; | |
line-height:1.5; | |
margin-bottom:.25em; | |
} |
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/* Reusable d3 force cluster layout for visualizing communities in | |
* large gene networks | |
* | |
* Follows the design pattern suggested by Mike Bostock: | |
* http://bost.ocks.org/mike/chart/ | |
*/ | |
(function() { | |
d3.netClust = function() { | |
// declare everything | |
// colors for the communities | |
const colors = { | |
'0': '#27ae60', | |
'1': '#2980b9', | |
'2': '#8e44ad', | |
'3': '#e67e22', | |
'4': '#e74c3c', | |
'5': '#34495e', | |
'6': '#f39c12', | |
'7': '#7f8c8d', | |
'8': '#bdc3c7', | |
}; | |
let selection; | |
let genes = []; | |
let edges = []; | |
let enrichData = []; | |
let normdegree = 1; | |
let clusters = []; | |
const force = d3.forceSimulation(); | |
const zoomba = d3.zoom(); | |
const edgesByCluster = {}; | |
const interClusterEdges = []; | |
const cNodeIds = {}; | |
const degree = {}; | |
const enrich = {}; | |
const eCurrMin = {}; | |
const k = 5; // parameter that we should be able to set | |
const enrichwc = {}; | |
const enrichtitle = {}; | |
const enrichAll = {}; | |
const stopwords = [ | |
'_positive', | |
'_negative', | |
'_process', | |
'_activity', | |
'_pathway', | |
'_from', | |
'_via', | |
'_a', | |
'_an', | |
'_and', | |
'_the', | |
'_in', | |
'_regulation', | |
'_to', | |
'_of', | |
'_response', | |
'_cell', | |
'_signaling', | |
'_stimulus', | |
'_cellular', | |
'_protein', | |
'_localization', | |
'_modification', | |
'_organization', | |
]; | |
const buff = 5.0; // should be able to set?? | |
const w = 800; // should be able to set | |
const h = 600; // should be able to set | |
// vars for zoom | |
let selected = false; | |
let lockzoom = false; | |
let lockzoomk = 0; | |
// tooltip stuff | |
const tooltip = d3 | |
.select('body') | |
.append('div') | |
.attr('class', 'toolTip'); | |
const clustTip = d3 | |
.select('body') | |
.append('div') | |
.attr('class', 'clustTip'); | |
const clustTip2 = d3 | |
.select('body') | |
.append('div') | |
.attr('class', 'clustTip'); | |
// core of the generation stuff | |
function chart(_selection) { | |
selection = _selection; // make selection global | |
console.info(selection); | |
const svg = selection | |
.append('svg') | |
.attr('width', w) | |
.attr('height', h) | |
.append('g') | |
.attr('transform', `translate(${w / 2},${h / 2})`) | |
.attr('class', 'network'); | |
const forceCollide = d3 | |
.forceCollide() | |
.radius(function(d) { | |
return degree[d.id] / normdegree * 7 + 4; | |
}) | |
.iterations(1); | |
// define cluster centers - temp but will be the last element of cluster | |
clusters = new Array(d3.max(genes, d => parseInt(d.cluster, 10))); | |
genes.forEach(d => { | |
// init case | |
if (clusters[parseInt(d.cluster, 10)] != null) { | |
if (degree[clusters[parseInt(d.cluster, 10)].id] < degree[d.id]) | |
// set the center to be the highest degree elm per cluster | |
clusters[parseInt(d.cluster, 10)] = d; | |
} else { | |
clusters[parseInt(d.cluster, 10)] = d; | |
} | |
}); | |
function forceCluster(alpha) { | |
for ( | |
let i = 0, n = genes.length, node, cluster, k2 = alpha * 1; | |
i < n; | |
i += 1 | |
) { | |
node = genes[i]; | |
cluster = clusters[node.cluster]; | |
node.vx -= (node.x - cluster.x) * k2; | |
node.vy -= (node.y - cluster.y) * k2; | |
} | |
} | |
// grouping for the cluster path | |
const filtered = genes.filter(d => d.cluster < 99); // REMOVE BUT ONLY 9 CLUSTER COLORS RIGHT NOW | |
const hclusters = d3 | |
.nest() | |
.key(function(d) { | |
return d.cluster; | |
}) | |
.entries(filtered); | |
const groupPath = function(d) { | |
// console.log("ddddddd"); | |
// console.log(d); | |
if (d.values.length === 2) { | |
const arr = d.values.map(i => [i.x, i.y]); | |
arr.push([arr[0][0], arr[0][1]]); | |
return `M${d3.polygonHull(arr).join('L')}Z`; | |
} | |
return `M${d3.polygonHull(d.values.map(i => [i.x, i.y])).join('L')}Z`; | |
}; | |
svg.append('g').attr('class', 'edgeContainer'); | |
const circle = svg | |
.selectAll('circle') | |
.data(genes) | |
.enter() | |
.append('circle') | |
.attr('r', function(d) { | |
return degree[d.id] / normdegree * 7 + 3; | |
}) | |
.attr('fill-opacity', 0.9) | |
.attr('class', function(d) { | |
return `node cluster${d.cluster}`; | |
}) | |
.attr('fill', function(d) { | |
return colors[d.cluster]; | |
}) | |
.attr('stroke', '#fff') | |
.attr('stroke-width', 1.5) | |
.on('mouseover', mouseOver) | |
.on('mouseout', mouseOut) | |
.on('click', clusterSelect); | |
function tick() { | |
circle | |
.attr('cx', function(d) { | |
return d.x; | |
}) | |
.attr('cy', function(d) { | |
return d.y; | |
}); | |
// add the cluster paths | |
svg | |
.selectAll('path') | |
.data(hclusters) | |
.attr('d', groupPath) | |
.enter() | |
.insert('path', 'circle') | |
// .style("fill", function(d){return colors[d.key];}) | |
.style('fill', 'none') | |
.style('stroke', '#00') | |
.style('stroke-width', 40) | |
.style('stroke-linejoin', 'round') | |
.style('opacity', 0.2) | |
.attr('d', groupPath) | |
.attr('class', function(d) { | |
return `pathc${d.key}`; | |
}); | |
} | |
force | |
.force('link', d3.forceLink().id(d => d.id)) | |
.nodes(genes) | |
.force('center', d3.forceCenter()) | |
.force('collide', forceCollide) | |
.force('cluster', forceCluster) | |
.force('gravity', d3.forceManyBody(5)) | |
.force('x', d3.forceX().strength(0.5)) | |
.force('y', d3.forceY().strength(0.5)) | |
.on('tick', tick); | |
// .stop(); | |
// force.force("link") | |
// .links(edges); | |
function zoomed() { | |
if (!lockzoom) svg.attr('transform', d3.event.transform); | |
else | |
svg.attr( | |
'transform', | |
`translate(${d3.event.transform.x}, ${ | |
d3.event.transform.y | |
}) scale(${lockzoomk})`, | |
); | |
} | |
zoomba.on('zoom', zoomed); | |
d3 | |
.select('svg') | |
.call(zoomba) | |
.call( | |
zoomba.transform, | |
d3.zoomIdentity.translate(w / 2.5, h / 2.5).scale(0.4), | |
); | |
} | |
// getter and setter functions | |
chart.genes = function(x) { | |
if (!arguments.length) return genes; | |
genes = x; | |
x.forEach(d => { | |
cNodeIds[d.id] = d.cluster; | |
}); | |
return chart; | |
}; | |
chart.edges = function(x) { | |
if (!arguments.length) return edges; | |
edges = x; | |
x.forEach(edge => { | |
if (!(edge.source in degree)) { | |
degree[edge.source] = 0; | |
} | |
if (!(edge.target in degree)) { | |
degree[edge.target] = 0; | |
} | |
degree[edge.source] += 1; | |
degree[edge.target] += 1; | |
if (cNodeIds[edge.source] === cNodeIds[edge.target]) { | |
// check that the edge is within the cluster | |
if (!edgesByCluster[cNodeIds[edge.source]]) | |
edgesByCluster[cNodeIds[edge.source]] = []; | |
edgesByCluster[cNodeIds[edge.source]].push(edge); | |
} else { | |
interClusterEdges.push(edge); | |
} | |
}); | |
console.info('inter cluster edges'); | |
console.info(interClusterEdges); | |
normdegree = d3.max(d3.values(degree)); | |
return chart; | |
}; | |
chart.enrich = function(x) { | |
if (!arguments.length) return enrichData; | |
x.forEach(d => { | |
if (!enrich[d.cluster]) { | |
enrich[d.cluster] = []; | |
eCurrMin[d.cluster] = 0.0; | |
enrichtitle[d.cluster] = ''; | |
enrichwc[d.cluster] = {}; | |
enrichAll[d.cluster] = []; | |
} | |
if (d.q_value < 0.05) { | |
// if significant count words | |
const words = d.name.split(/\b/); | |
for (let i = 0; i < words.length; i += 1) { | |
if (words[i] !== ' ' && words[i] !== '-') | |
enrichwc[d.cluster][`_${words[i]}`] = | |
(enrichwc[d.cluster][`_${words[i]}`] || 0) + 1; | |
} | |
enrichAll[d.cluster].push(d); | |
} | |
if (enrich[d.cluster].length < k || d.q_value < eCurrMin[d.cluster]) { | |
enrich[d.cluster].push(d); | |
if (enrich[d.cluster].length > k) { | |
// need to take an element off | |
let needle = -1; | |
let newMin = d.q_value; | |
for (let i = 0; i < enrich[d.cluster].length; i += 1) { | |
if (enrich[d.cluster][i].q_value === eCurrMin[d.cluster]) { | |
needle = i; | |
} else { | |
newMin = Math.max(newMin, enrich[d.cluster][i].q_value); | |
} | |
} | |
if (needle === -1) | |
console.error( | |
'something went horribly wrong during enrichment title calculation!', | |
); | |
else { | |
enrich[d.cluster].splice(needle, 1); | |
eCurrMin[d.cluster] = newMin; | |
} | |
} | |
eCurrMin[d.cluster] = Math.max(d.q_value, eCurrMin[d.cluster]); | |
} | |
}); | |
// second pass through the data to expand the top seeds | |
Object.keys(enrichwc).forEach(clust => { | |
// sort by word frequency | |
let tmpwc = []; | |
tmpwc = Object.keys(enrichwc[clust]).map(key => ({ | |
name: key, | |
count: enrichwc[clust][key], | |
})); | |
tmpwc.sort((a, b) => b.count - a.count); | |
// get most common word that is not a stop word as seed | |
let seed = ''; | |
let freq = 0; | |
for (let i = 0; i < tmpwc.length; i += 1) { | |
if ($.inArray(tmpwc[i].name, stopwords) === -1) { | |
seed = tmpwc[i].name; | |
freq = tmpwc[i].count; | |
break; | |
} | |
} | |
// console.log("cluster" + clust); | |
// console.log(seed + " " + freq); | |
// expand on either side of seed to get candidate titles | |
const enrichTs = []; | |
// console.log(enrichAll[clust]); | |
Object.keys(enrichAll[clust]).forEach(elm => { | |
const d = enrichAll[clust][elm]; | |
if (d.name.search(seed.substr(1)) !== -1) { | |
// check if term in name | |
// console.log("in substring"); | |
// console.log(d); | |
let words = d.name.split(/\b/); | |
// remove the spaces and the dashes or underscores | |
const clean = []; | |
Object.keys(words).forEach(word => { | |
if ( | |
words[word] !== '-' && | |
words[word] !== ' ' && | |
words[word] !== '_' | |
) | |
clean.push(words[word]); | |
}); | |
words = clean; | |
// console.log("words after"); | |
// console.log(words); | |
const ind = $.inArray(seed.substr(1), words); | |
if (ind !== -1) { | |
let title = seed.substr(1); | |
let sum = freq; | |
let marker = ind; | |
// console.log("marker: " + marker + " sum: " + sum + " title: " + title); | |
while ( | |
marker < words.length && | |
enrichwc[clust][`_${words[marker + 1]}`] > | |
Math.max(freq - buff, 2) // expand to the right | |
) { | |
title += ` ${words[marker + 1]}`; | |
if ($.inArray(`_${words[marker + 1]}`, stopwords) !== -1) | |
sum += 1; | |
else sum += enrichwc[clust][`_${words[marker + 1]}`]; | |
marker += 1; | |
} | |
const pre = []; | |
marker = ind; | |
while ( | |
marker > 0 && | |
enrichwc[clust][`_${words[marker - 1]}`] > | |
Math.max(freq - buff, 2) // expand to the left | |
) { | |
// title += " " + words[marker-1]; | |
if ($.inArray(`_${words[marker - 1]}`, stopwords) !== -1) | |
sum += 1; | |
else sum += enrichwc[clust][`_${words[marker - 1]}`]; | |
marker -= 1; | |
pre.push(words[marker - 1]); | |
} | |
pre.reverse(); | |
title = `${pre.join(' ')} ${title}`; | |
if (title.charAt(0) === ' ') title = title.substr(1); | |
enrichTs.push({ title, count: sum }); | |
} | |
} | |
}); | |
// take top seed | |
enrichTs.sort((a, b) => b.count - a.count); | |
// console.log("enrichTs"); | |
// console.log(enrichTs); | |
enrichtitle[clust] = enrichTs[0].title; | |
}); | |
console.info('enrichment'); | |
console.info(enrichtitle); | |
console.info(enrich); | |
enrichData = enrichAll; | |
return chart; | |
}; | |
function mouseOver(d) { | |
if (!selected) { | |
// console.log("mouseover"); | |
d3.selectAll(`.cluster${d.cluster}`).attr('fill-opacity', 1.0); | |
// label the enrichment | |
let htmltxt = `<div class='ctname'><span class='ctnum' style='color:${ | |
colors[d.cluster] | |
}'>${parseInt(d.cluster, 10) + 1}</span> - ${ | |
enrichtitle[d.cluster] | |
}</div>`; | |
htmltxt += "<div class='ctContain'><ul>"; | |
Object.keys(enrich[d.cluster]).forEach(i => { | |
htmltxt += `<li>${ | |
enrich[d.cluster][i].name | |
} (<span class='qval'>${enrich[d.cluster][i].q_value.toFixed( | |
3, | |
)}</span>)</li>`; | |
}); | |
htmltxt += '</div>'; | |
clustTip | |
.style('left', `${d3.event.pageX}px`) | |
.style('top', `${d3.event.pageY - 150}px`) | |
.style('display', 'inline-block') | |
.html(htmltxt); | |
// change the opacity of all other clusters | |
for (let i = 0; i < clusters.length; i += 1) { | |
if (i !== parseInt(d.cluster, 10)) { | |
d3.selectAll(`.cluster${i}`).attr('fill-opacity', 0.15); | |
} | |
} | |
} else { | |
// highlight gene | |
// console.log("in mouseover zoom"); | |
// console.log(d3.select(this)); | |
d3.select(this).attr('stroke', 'orange'); | |
// add node label as tooltip | |
tooltip | |
.style('left', `${d3.event.pageX - 25}px`) | |
.style('top', `${d3.event.pageY - 45}px`) | |
.style('display', 'inline-block') | |
.html(d.entrez); | |
} | |
} | |
// undo mouseover effects | |
function mouseOut() { | |
// console.log("mouseout"); | |
clustTip.style('display', 'none'); | |
if (!selected) { | |
d3.selectAll('.node').attr('fill-opacity', 0.9); | |
} else { | |
// console.log("in mouseout zoom"); | |
d3.select(this).attr('stroke', 'white'); | |
tooltip.style('display', 'none'); | |
} | |
} | |
function resetClick(elm) { | |
// console.log("in reset click"); | |
d3.select(elm).attr('stroke', 'white'); | |
tooltip.style('display', 'none'); | |
clustTip2.style('display', 'none'); | |
d3.selectAll('.node').attr('fill-opacity', 0.9); | |
d3.selectAll('.edge').remove(); | |
d3.selectAll('.clusterLabel').remove(); | |
d3 | |
.select('.network') | |
.transition() | |
.duration(750) | |
.call( | |
zoomba.transform, | |
d3.zoomIdentity.translate(w / 2.5, h / 2.5).scale(0.4), | |
); // updated for d3 v4 | |
selected = false; | |
lockzoomk = 0.4; | |
lockzoom = false; | |
// setTimeout(function(){ lockzoom = false;}, 750); // time > duration of transition | |
} | |
// zoom to cluster! draw edges! list enrichment! | |
function clusterSelect(node) { | |
clustTip.style('display', 'none'); | |
console.info(`node clicked: ${node}`); | |
if (!selected) { | |
const bbox = d3 | |
.select(`.pathc${node.cluster}`) | |
.node() | |
.getBBox(); | |
// console.log("bounds: " + bbox.x + " " + bbox.y + " " + bbox.width + " " + bbox.height); | |
const scale = Math.min( | |
w / 2 / bbox.width, | |
h / 2 / bbox.height, | |
w / 2 / bbox.height, | |
h / 2 / bbox.width, | |
); | |
const translate = [ | |
w / 2 - w / 2.5 - (bbox.x - bbox.width / 2) * scale, | |
h / 2 - h / 2.5 - (bbox.y - bbox.height / 2) * scale, | |
]; | |
d3 | |
.select('.network') | |
.transition() | |
.duration(750) | |
.call( | |
zoomba.transform, | |
d3.zoomIdentity.translate(translate[0], translate[1]).scale(scale), | |
); | |
selected = true; | |
lockzoomk = scale; | |
// lockzoom = true; | |
setTimeout(() => { | |
lockzoom = true; | |
}, 750); // time > duration | |
force.force('link').links(edgesByCluster[node.cluster]); | |
d3 | |
.select('.edgeContainer') | |
.append('g') | |
.attr('class', 'edge') | |
.selectAll('line') | |
.data(edgesByCluster[node.cluster]) | |
.enter() | |
.append('line') | |
.attr('stroke', '#999') | |
.attr('stroke-opacity', function(d) { | |
return +d.weight / 500; | |
}) | |
.attr('x1', function(d) { | |
return d.source.x; | |
}) | |
.attr('y1', function(d) { | |
return d.source.y; | |
}) | |
.attr('x2', function(d) { | |
return d.target.x; | |
}) | |
.attr('y2', function(d) { | |
return d.target.y; | |
}); | |
const bcr = d3 | |
.select(`.pathc${node.cluster}`) | |
.node() | |
.getBoundingClientRect(); | |
console.info('bcr'); | |
console.info(d3.select(`.pathc${node.cluster}`).node()); | |
console.info(bcr); | |
let htmltxt = `<div class='ctname'><span class='ctnum' style='color:${ | |
colors[node.cluster] | |
}'>${parseInt(node.cluster, 10) + 1}</span> - ${ | |
enrichtitle[node.cluster] | |
}</div>`; | |
htmltxt += "<div class='ctContain'><ul>"; | |
Object.keys(enrichAll[node.cluster]).forEach(i => { | |
htmltxt += `<li>${ | |
enrichAll[node.cluster][i].name | |
} (<span class='qval'>${enrichAll[node.cluster][i].q_value.toFixed( | |
3, | |
)}</span>)</li>`; | |
}); | |
htmltxt += '</div>'; | |
clustTip2 | |
.html(htmltxt) | |
.attr('id', 'ct2') | |
.style('left', `${w - $('#ct2').width()}px`) | |
.style('top', `${0}px`) | |
.style('display', 'inline-block'); | |
} else { | |
resetClick(this); | |
} | |
} | |
return chart; | |
}; | |
})(); |
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<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta http-equiv="content-type" content="text/html" charset="utf-8"> | |
<meta name="author" content="Ruth Dannenfelser"> | |
<meta name="description" content="clustering"> | |
<meta name="keywords" content="Princeton University clustering disease genes prediction tissue specific function lab functionlab troyanskaya computational biology"> | |
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
<title>d3-netClust</title> | |
<link rel="stylesheet" href= "base.css"/> | |
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script> | |
<script src="https://d3js.org/d3.v4.js"></script> | |
<script src="d3.netClust.js" charset="utf-8"></script> | |
</head> | |
<body> | |
<div id="graphholder"></div> | |
</body> | |
<script> | |
function constructGraph(g) { | |
console.info(g); | |
const chart = d3 | |
.netClust() | |
.genes(g.nodes) | |
.edges(g.edges) | |
.enrich(g.enrichment); | |
d3.select('#graphholder').call(chart); | |
} | |
// allows direct vis of JSON file in current directory | |
function testVis(fn) { | |
$.ajax({ | |
type: 'GET', | |
contentType: 'application/json; charset=utf-8', | |
url: fn, | |
dataType: 'json', | |
async: true, | |
success(data) { | |
constructGraph(data); | |
}, | |
error(result) { | |
console.error(`ERROR ${result}`); | |
}, | |
}); | |
} | |
$(document).ready(() => { | |
testVis('test.json'); // build network from JSON file | |
}); | |
</script> | |
</html> |
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"term": "GO:0006732", "name": "coenzyme metabolic process", "p_value": 0.03996912043855833}, {"q_value": 0.04297534991895664, "cluster": "3", "term": "GO:0006733", "name": "oxidoreduction coenzyme metabolic process", "p_value": 0.009179200953563555}, {"q_value": 0.04128382862180766, "cluster": "3", "term": "GO:0072524", "name": "pyridine-containing compound metabolic process", "p_value": 0.008128065424148553}, {"q_value": 0.0891902028098982, "cluster": "3", "term": "GO:0051186", "name": "cofactor metabolic process", "p_value": 0.05888285234051532}, {"q_value": 0.04003816707078479, "cluster": "3", "term": "GO:0019362", "name": "pyridine nucleotide metabolic process", "p_value": 0.006499377663855046}, {"q_value": 0.04003816707078479, "cluster": "3", "term": "GO:0046496", "name": "nicotinamide nucleotide metabolic process", "p_value": 0.006499377663855046}, {"q_value": 0.07146338785958, "cluster": "3", "term": "GO:0097435", "name": "supramolecular fiber organization", "p_value": 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"p_value": 0.06856226528876073}, {"q_value": 0.01737069000454932, "cluster": "3", "term": "GO:0045747", "name": "positive regulation of Notch signaling pathway", "p_value": 0.00032874873497631393}, {"q_value": 0.04000813608507474, "cluster": "3", "term": "GO:0008593", "name": "regulation of Notch signaling pathway", "p_value": 0.0030393372964830465}, {"q_value": 0.04003816707078479, "cluster": "3", "term": "GO:0007219", "name": "Notch signaling pathway", "p_value": 0.006812494642576741}, {"q_value": 0.061054852737100845, "cluster": "3", "term": "GO:0090150", "name": "establishment of protein localization to membrane", "p_value": 0.027667601079328887}]} |
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