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Sample network clustering vis
Raw
base.css
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;
}
Raw
d3.netClust.js
/* 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;
};
})();
Raw
index.html
<!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>
Raw
test.json
{"nodes": [{"cluster": "0", "entrez": "1780", "id": "node_0"}, {"cluster": "1", "entrez": "133", "id": "node_1"}, {"cluster": "2", "entrez": "27286", "id": "node_2"}, {"cluster": "2", "entrez": "5507", "id": "node_4"}, {"cluster": "0", "entrez": "10100", "id": "node_5"}, {"cluster": "3", "entrez": "5274", "id": "node_6"}, {"cluster": "0", "entrez": "253982", "id": "node_7"}, {"cluster": "1", "entrez": "8507", "id": "node_8"}, {"cluster": "2", "entrez": "4147", "id": "node_10"}, {"cluster": "1", "entrez": "1839", "id": "node_11"}, {"cluster": "3", "entrez": "6185", "id": "node_12"}, {"cluster": "2", "entrez": "4907", "id": "node_13"}, {"cluster": "0", "entrez": "547", "id": "node_15"}, {"cluster": "3", "entrez": "25941", "id": "node_16"}, {"cluster": "3", "entrez": "10745", "id": "node_17"}, {"cluster": "0", "entrez": "51778", "id": "node_18"}, {"cluster": "0", "entrez": "286", "id": "node_19"}, {"cluster": "3", "entrez": "9270", "id": "node_20"}, {"cluster": "3", "entrez": "1312", "id": "node_21"}, {"cluster": "1", "entrez": "84981", "id": "node_22"}, {"cluster": "3", "entrez": "6990", "id": "node_23"}, {"cluster": "3", "entrez": "9532", "id": "node_24"}, {"cluster": "2", "entrez": "7045", "id": "node_25"}, {"cluster": "0", "entrez": "51560", "id": "node_27"}, {"cluster": "0", "entrez": "22925", "id": "node_28"}, {"cluster": "0", "entrez": "8534", "id": "node_29"}, {"cluster": "0", "entrez": "3641", "id": "node_30"}, {"cluster": "1", "entrez": "8870", "id": "node_31"}, {"cluster": "2", "entrez": "9902", "id": "node_32"}, {"cluster": "0", "entrez": "7252", "id": "node_33"}, {"cluster": "2", "entrez": "4131", "id": "node_34"}, {"cluster": "2", "entrez": "10217", "id": "node_36"}, {"cluster": "2", "entrez": "81035", "id": "node_38"}, {"cluster": "3", "entrez": "55757", "id": "node_39"}, {"cluster": "3", "entrez": "824", "id": "node_40"}, {"cluster": "0", "entrez": "827", "id": "node_41"}, {"cluster": "0", "entrez": "25891", "id": "node_42"}, {"cluster": "3", 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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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0.05806898205556403, "cluster": "3", "term": "GO:2001251", "name": "negative regulation of chromosome organization", "p_value": 0.02443443958463739}, {"q_value": 0.06311270413415228, "cluster": "3", "term": "GO:0032271", "name": "regulation of protein polymerization", "p_value": 0.029334943540615124}, {"q_value": 0.0762682026131076, "cluster": "3", "term": "GO:0051258", "name": "protein polymerization", "p_value": 0.04627924915843908}, {"q_value": 0.04000813608507474, "cluster": "3", "term": "GO:1901222", "name": "regulation of NIK/NF-kappaB signaling", "p_value": 0.004758249194584132}, {"q_value": 0.04003816707078479, "cluster": "3", "term": "GO:0038061", "name": "NIK/NF-kappaB signaling", "p_value": 0.005598934570662842}, {"q_value": 0.05806898205556403, "cluster": "3", "term": "GO:0008154", "name": "actin polymerization or depolymerization", "p_value": 0.02443443958463739}, {"q_value": 0.09876801852786513, "cluster": "3", "term": "GO:0018209", "name": "peptidyl-serine modification", "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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