-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathresearch.html
526 lines (459 loc) · 16.7 KB
/
research.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<title>Research</title>
<script src="site_libs/header-attrs-2.25/header-attrs.js"></script>
<script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="site_libs/bootstrap-3.3.5/css/bootstrap.min.css" rel="stylesheet" />
<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<style>h1 {font-size: 34px;}
h1.title {font-size: 38px;}
h2 {font-size: 30px;}
h3 {font-size: 24px;}
h4 {font-size: 18px;}
h5 {font-size: 16px;}
h6 {font-size: 12px;}
code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
pre:not([class]) { background-color: white }</style>
<script src="site_libs/navigation-1.1/tabsets.js"></script>
<link href="site_libs/highlightjs-9.12.0/default.css" rel="stylesheet" />
<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>
<style type="text/css">code{white-space: pre;}</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
img {
max-width:100%;
}
.tabbed-pane {
padding-top: 12px;
}
.html-widget {
margin-bottom: 20px;
}
button.code-folding-btn:focus {
outline: none;
}
summary {
display: list-item;
}
details > summary > p:only-child {
display: inline;
}
pre code {
padding: 0;
}
</style>
<style type="text/css">
.dropdown-submenu {
position: relative;
}
.dropdown-submenu>.dropdown-menu {
top: 0;
left: 100%;
margin-top: -6px;
margin-left: -1px;
border-radius: 0 6px 6px 6px;
}
.dropdown-submenu:hover>.dropdown-menu {
display: block;
}
.dropdown-submenu>a:after {
display: block;
content: " ";
float: right;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
border-width: 5px 0 5px 5px;
border-left-color: #cccccc;
margin-top: 5px;
margin-right: -10px;
}
.dropdown-submenu:hover>a:after {
border-left-color: #adb5bd;
}
.dropdown-submenu.pull-left {
float: none;
}
.dropdown-submenu.pull-left>.dropdown-menu {
left: -100%;
margin-left: 10px;
border-radius: 6px 0 6px 6px;
}
</style>
<script type="text/javascript">
// manage active state of menu based on current page
$(document).ready(function () {
// active menu anchor
href = window.location.pathname
href = href.substr(href.lastIndexOf('/') + 1)
if (href === "")
href = "index.html";
var menuAnchor = $('a[href="' + href + '"]');
// mark the anchor link active (and if it's in a dropdown, also mark that active)
var dropdown = menuAnchor.closest('li.dropdown');
if (window.bootstrap) { // Bootstrap 4+
menuAnchor.addClass('active');
dropdown.find('> .dropdown-toggle').addClass('active');
} else { // Bootstrap 3
menuAnchor.parent().addClass('active');
dropdown.addClass('active');
}
// Navbar adjustments
var navHeight = $(".navbar").first().height() + 15;
var style = document.createElement('style');
var pt = "padding-top: " + navHeight + "px; ";
var mt = "margin-top: -" + navHeight + "px; ";
var css = "";
// offset scroll position for anchor links (for fixed navbar)
for (var i = 1; i <= 6; i++) {
css += ".section h" + i + "{ " + pt + mt + "}\n";
}
style.innerHTML = "body {" + pt + "padding-bottom: 40px; }\n" + css;
document.head.appendChild(style);
});
</script>
<!-- tabsets -->
<style type="text/css">
.tabset-dropdown > .nav-tabs {
display: inline-table;
max-height: 500px;
min-height: 44px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 4px;
}
.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
content: "\e259";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
content: "\e258";
font-family: 'Glyphicons Halflings';
border: none;
}
.tabset-dropdown > .nav-tabs > li.active {
display: block;
}
.tabset-dropdown > .nav-tabs > li > a,
.tabset-dropdown > .nav-tabs > li > a:focus,
.tabset-dropdown > .nav-tabs > li > a:hover {
border: none;
display: inline-block;
border-radius: 4px;
background-color: transparent;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
display: block;
float: none;
}
.tabset-dropdown > .nav-tabs > li {
display: none;
}
</style>
<!-- code folding -->
</head>
<body>
<div class="container-fluid main-container">
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-bs-toggle="collapse" data-target="#navbar" data-bs-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="index.html">Odiscé</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="index.html">Home</a>
</li>
<li>
<a href="news.html">News</a>
</li>
<li>
<a href="team.html">Team</a>
</li>
<li>
<a href="research.html">Research</a>
</li>
<li>
<a href="projects.html">Projects</a>
</li>
<li>
<a href="publications.html">Publications</a>
</li>
<li>
<a href="software.html">Software</a>
</li>
<li>
<a href="contact.html">Contact</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
</ul>
</div><!--/.nav-collapse -->
</div><!--/.container -->
</div><!--/.navbar -->
<div id="header">
<h1 class="title toc-ignore">Research</h1>
</div>
<p>Our research has been focusing for more than 15 years on
<strong>omics data sciences</strong> for <strong>systems biology and
precision medicine</strong>, with a particular emphasis on metabolomics
data sciences.</p>
<p><strong>Data sciences</strong> provide powerful approaches and
algorithms (signal processing, data mining, machine learning, artificial
intelligence) for the <strong>processing and analysis of
high-dimensional data</strong>, such as omics datasets.
<strong>Metabolomics</strong> (untargeted analysis of small molecules
involved in biochemical reactions) is of major interest for phenotype
characterization and biomarker discovery. <strong>High-resolution mass
spectrometry (HRMS)</strong> is a technology of choice for metabolomics
(and also for proteomics), due to its sensitivity and resolution.</p>
<p>We have implemented our new methods in a <strong>comprehensive
digital ecosystem</strong> for <strong>metabolomics data
sciences</strong>, with applications to <strong>precision
medicine</strong> (including neurosciences, liver diseases and food
allergy).</p>
<div id="data-processing" class="section level2">
<h2>Data processing</h2>
<p><strong>Direct injection methods</strong> (such as Flow Injection
Analysis or Proton Transfer Reaction) are of particular interest for
high-throughput phenotyping. We therefore developed an innovative
<strong>preprocessing workflow</strong> which takes as input the
individual raw files and generates the samples by variables table of
intensities (peak table). The steps include (i) <strong>peak detection
and quantification</strong> within each file, (ii) <strong>peak
alignment</strong> across samples to generate the peak table, and (iii)
<strong>missing value imputation</strong>. In particular, new methods
were required to optimize step (i), including noise estimation, modeling
of the injection peak, and precise determination of each analyte peak
borders. Application to several real data sets resulted in robust and
accurate detection and quantification. <a
href="http://bioconductor.org/packages/proFIA"><strong>proFIA</strong></a>
is available as an R/Bioconductor package (<a
href="http://dx.doi.org/10.1093/bioinformatics/btx458"><strong>Delabrière
et al, 2017</strong></a>).</p>
<center>
<div class="float">
<img src="images/proFIA.png"
alt="proFIA workflow for the processing of high-throughput and high-resolution FIA-HRMS data (Delabrière et al, 2017)" />
<div class="figcaption"><a
href="http://bioconductor.org/packages/proFIA"><strong>proFIA</strong></a>
workflow for the processing of high-throughput and high-resolution
FIA-HRMS data (<a
href="http://dx.doi.org/10.1093/bioinformatics/btx458"><strong>Delabrière
et al, 2017</strong></a>)</div>
</div>
</center>
<p>In <strong>volatolomics</strong> (analysis of volatile organic
compounds), PTR-TOF-MS offers unique opportunities for <strong>real-time
analysis of exhaled air at the patient’s bedside</strong> (<a
href="http://dx.doi.org/10.1016/j.ebiom.2020.103154"><strong>Grassin
Delyle et al., 2021</strong></a><strong>)</strong>. We therefore
developed a <strong>comprehensive suite of algorithms for the
pre-processing of acquisitions in large cohorts</strong>, which includes
an <strong>innovative two-dimensional peak deconvolution model</strong>
based on penalized splines signal regression for accurate estimation of
the temporal profile, implemented in the <a
href="http://bioconductor.org/packages/ptairMS"><strong>ptairMS</strong></a>
software (<a
href="https://doi.org/10.1093/bioinformatics/btac031"><strong>Roquencourt
et al, 2022</strong></a>).</p>
<center>
<div class="float">
<img src="images/ptairMS.png"
alt="Main steps of the processing of PTR-TOF-MS data with the ptairMS software (Roquencourt et al, 2022)" />
<div class="figcaption">Main steps of the processing of PTR-TOF-MS data
with the <a
href="http://bioconductor.org/packages/ptairMS"><strong>ptairMS</strong></a>
software (<a
href="https://doi.org/10.1093/bioinformatics/btac031"><strong>Roquencourt
et al, 2022</strong></a>)</div>
</div>
</center>
</div>
<div id="data-modeling" class="section level2">
<h2>Data modeling</h2>
<p>We implemented the <strong>Orthogonal Partial Least-Squares
(OPLS)</strong> approach for regression and classification from <a
href="http://dx.doi.org/10.1002/cem.695">Trygg and Wold (2002)</a> as an
R package named <a
href="http://bioconductor.org/packages/ropls"><strong>ropls</strong></a>
(<a
href="https://doi.org/10.1021/acs.jproteome.5b00354"><strong>Thévenot et
al, 2015</strong></a>). OPLS algorithm is a variation of PLS and allows
to model separately the orthogonal variation (i.e. non-correlated to the
response) and the predictive variation (i.e. correlated to the
response), and thus facilitates model interpretation.</p>
<center>
<div class="float">
<img src="images/ropls.jpg"
alt="PCA, PLS(-DA) and OPLS(-DA) modeling with ropls (Thévenot et al, 2015)" />
<div class="figcaption">PCA, PLS(-DA) and OPLS(-DA) modeling with <a
href="http://bioconductor.org/packages/ropls"><strong>ropls</strong></a>
(<a
href="http://dx.doi.org/10.1021/acs.jproteome.5b00354"><strong>Thévenot
et al, 2015</strong></a>)</div>
</div>
</center>
<p>We developed a new methodology for <strong>feature
selection</strong>, of the wrapper type, which assesses the significance
of the features for the model performance (<a
href="http://bioconductor.org/packages/biosigner"><strong>biosigner</strong></a>
R package). The wrapping of three classifiers (PLS-DA, Random Forest and
Support Vector Machine) with this methodology resulted in
<strong>stable</strong> signatures of <strong>restricted size</strong>,
when applied to real metabolomics and transcriptomics datasets (<a
href="https://doi.org/10.3389/fmolb.2016.00026"><strong>Rinaudo et al,
2016</strong></a>).</p>
<center>
<div class="float">
<img src="images/biosigner.jpg"
alt="Selection of significant omics features with biosigner for PLS-DA, Random Forest or SVM prediction (Rinaudo et al, 2016)" />
<div class="figcaption">Selection of significant omics features with <a
href="http://bioconductor.org/packages/biosigner"><strong>biosigner</strong></a>
for PLS-DA, Random Forest or SVM prediction (<a
href="https://doi.org/10.3389/fmolb.2016.00026"><strong>Rinaudo et al,
2016</strong></a>)</div>
</div>
</center>
<p><strong>Integration of complementary omics data</strong> is an
opportunity to build <strong>more robust predictive models</strong> and
<strong>facilitate the biologicial interpretation</strong>. To
demonstrate the feasibility and interest of <strong>combining proteomics
and metabolomics</strong> in routine, we have developed, within a
consortium of research infrastructures in <strong>phenogenomics (<a
href="http://www.phenomin.fr/en-us/">PHENOMIN</a>)</strong>,
<strong>proteomics (<a
href="http://www.profiproteomics.fr/">ProFI</a>)</strong>,
<strong>metabolomics (<a
href="https://www.metabohub.fr/home.html">MetaboHUB</a>)</strong> and
<strong>bioinformatics (<a
href="http://www.france-bioinformatique.fr/en">IFB</a>)</strong>, a
<strong>data post-processing and integration pipeline</strong> that we
have applied to the study of murine models. All the data and codes are
available in the <a
href="https://github.com/IFB-ElixirFr/ProMetIS"><strong>ProMetIS</strong></a>
package (<a
href="https://doi.org/10.1038/s41597-021-01095-3"><strong>Imbert et al.,
2021</strong></a>).</p>
<center>
<div class="float">
<img src="images/ProMetIS.png"
alt="ProMetIS, Deep phenotyping of mouse models by combined proteomics and metabolomics analysis (Imbert et al., 2021)" />
<div class="figcaption"><a
href="https://github.com/IFB-ElixirFr/ProMetIS"><strong>ProMetIS</strong></a>,
Deep phenotyping of mouse models by combined proteomics and metabolomics
analysis (<a
href="https://doi.org/10.1038/s41597-021-01095-3"><strong>Imbert et al.,
2021</strong></a>)</div>
</div>
</center>
</div>
<div id="computing-with-data" class="section level2">
<h2>Computing with data</h2>
<p><strong>Reference datasets</strong> are available in the <a
href="https://www.ebi.ac.uk/metabolights/"><strong>MetaboLights</strong></a>
repository as well as in R packages (<a
href="https://www.bioconductor.org/packages/plasFIA/"><strong>plasFIA</strong></a>,
<a
href="https://www.bioconductor.org/packages/ptairData/"><strong>ptairData</strong></a>,
<a
href="http://bioconductor.org/packages/ropls"><strong>ropls</strong></a>,
<a
href="http://bioconductor.org/packages/biosigner"><strong>biosigner</strong></a>,
<a
href="https://github.com/IFB-ElixirFr/ProMetIS"><strong>ProMetIS</strong></a>).</p>
<p>All algorithms are implemented as a <strong>comprehensive suite of 6
R packages</strong> on the <a
href="http://bioconductor.org"><strong>Bioconductor</strong></a>
repository.</p>
<center>
<div class="float">
<img src="images/odisce_software.png"
alt="Software suite for metabolomics data sciences" />
<div class="figcaption">Software suite for metabolomics data
sciences</div>
</div>
</center>
<p>Many of them are also available as <strong>Galaxy modules</strong> on
the <a
href="http://workflow4metabolomics.org"><strong>Workflow4Metabolomics</strong></a>
online platform,jointly developed and maintained by the <a
href="http://www.france-bioinformatique.fr/en"><strong>French Institute
of Bioinformatics</strong></a> and <a
href="http://www.metabohub.fr/home.html"><strong>MetaboHUB</strong></a>
(<a
href="https://doi.org/10.1093/bioinformatics/btu813"><strong>Giacomoni
et al, 2015</strong></a>; <a
href="https://doi.org/10.1016/j.biocel.2017.07.002"><strong>Guitton et
al, 2017</strong></a>).</p>
</div>
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
$(document).ready(function () {
$('.tabset-dropdown > .nav-tabs > li').click(function () {
$(this).parent().toggleClass('nav-tabs-open');
});
});
</script>
<!-- code folding -->
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>