9 results on '"Decamps, Clémentine"'
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2. Additional file 1 of DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification
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Decamps, Clémentine, Arnaud, Alexis, Petitprez, Florent, Ayadi, Mira, Baurès, Aurélia, Armenoult, Lucile, Escalera, Sergio, Guyon, Isabelle, Nicolle, Rémy, Tomasini, Richard, de Reyniès, Aurélien, Cros, Jérôme, Blum, Yuna, and Richard, Magali
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FOS: Computer and information sciences ,FOS: Biological sciences ,Genetics ,FOS: Mathematics ,19999 Mathematical Sciences not elsewhere classified ,80699 Information Systems not elsewhere classified ,69999 Biological Sciences not elsewhere classified ,Cancer - Abstract
Additional file 1: Figure S1: DECONbench benchmark of OLS and RLR methods: an example of graphical outputs of new contributions to the benchmark. Source code.
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- 2021
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3. Développements méthodologiques pour la caractérisation de l'hétérogénéité tumorale
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Decamps, Clémentine and STAR, ABES
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Bioinformatic ,Tumor heterogeneity ,Transcriptomique ,Bioinformatique ,[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,Méthode statistique ,[SDV.CAN] Life Sciences [q-bio]/Cancer ,Statistics ,Methods ,Omics ,Hétérogénéité tumorale ,Cancer ,Omiques - Abstract
Cancer is a complex disease in which each tumor is different in terms of the genetic mutations, the expression of genes, or the composition of cell types. These numerous factors play a determining role in the response to treatments and the survival of patients. During my thesis, I developed innovative methods based on different types of omics data in order to better characterize tumor heterogeneity at these different scales, thus allowing a better understanding of individual tumors, with the possibility of implementing personalized medicine strategies.The first part of my thesis focused on the development of a personalized differential analysis method. This method allows to infer deregulated genes at the scale of a single tumor from transcriptomic data. Using this method, I characterized inter-tumor (between individuals) heterogeneity in lung cancer. We were able to isolate genes of interest, such as "super-conserved" genes (whose expression never varies between patients) or systematically deregulated genes, and also to discover new tumor subtypes related to patient survival and to infer associated biomarkers.In the second part, I was interested in intra-tumor heterogeneity, i.e. the cell type composition of a given tumor, which contains both cancer cells and microenvironment cells (immune cells, stroma, healthy cells). The goal here is to infer this composition from omics data (transcriptomic and DNA methylation) obtained on the whole tumor. For this purpose, we organized a first multi-disciplinary data challenge to explore different reference-free deconvolution methods based on methylation data and to evaluate the interest of pre-processing the data. We derived a comparative analysis of existing methods, as well as a best practice guide for the use of deconvolution to infer tumor heterogeneity. We then explored the integration of two types of data (RNA seq and methylation) in a second data challenge that led to the implementation of a benchmarking platform.Finally, the third part of my thesis links the first two parts. By combining deconvolution and differential analysis, I developed an algorithm that allows to infer gene deregulation in cancer cells only, and to associate this deregulation to the composition of the tumor microenvironment. After extensive study of the different parameters of the method on simulation sets, this approach should make it possible to identify genes whose deregulation in cancer plays a role in the organization of the tumor microenvironment, thus opening the door to the possibility of identifying new biomarkers of heterogeneity that could be related to the virulence of cancer, and thus overall to obtain a better understanding of the biological mechanisms involved., Le cancer est une maladie complexe où chaque tumeur est différente, aussi bien au niveau des mutations génétiques présentes, de l’expression des gènes, ou de la composition en types cellulaires. Ces nombreux facteurs jouent un rôle déterminant dans la réponse aux traitements et la survie des patients. Durant ma thèse, j’ai développé des méthodes innovantes basées sur différents types de données omiques afin de mieux caractériser l'hétérogénéité tumorale à ces différentes échelles, permettant ainsi une meilleure compréhension individuelle des tumeurs, avec à terme la possibilité de mettre en place des stratégies de médecine personnalisée.La première partie de ma thèse s’est concentrée sur le développement d’une méthode d’analyse différentielle personnalisée. Cette méthode permet d’inférer les gènes dérégulés à l’échelle d’une tumeur unique à partir de données transcriptomiques. A l’aide de cette méthode, j’ai caractérisé l’hétérogénéité inter-tumorale (entre individus) dans le cancer du poumon. Nous avons ainsi pu isoler des gènes d’intérêts, comme des gènes "super-conservés" (dont l’expression ne varie jamais entre les patients) ou bien des gènes systématiquement dérégulés, et également découvrir de nouveaux sous-types tumoraux liés à la survie des patients ainsi qu'inférer les biomarqueurs associés.Dans la deuxième partie, je me suis intéressé à l’hétérogénéité intra-tumorale, c’est-à-dire à la composition en types cellulaires d’une tumeur donnée, qui contient à la fois des cellules cancéreuses et des cellules du microenvironnement (cellules immunitaires, stroma, cellules saines). Le but est ici d’inférer cette composition à partir de données omiques (transcriptomique et méthylation de l’ADN) obtenues sur la tumeur entière. Pour cela, nous avons organisé un premier data challenge multi-disciplinaire pour explorer différentes méthodes de déconvolution dites sans référence qui se basent sur les données de méthylation et évaluer l'intérêt du pré-traitement des données. Nous en avons tiré une analyse comparative des méthodes existantes, ainsi qu'un guide des bonnes pratiques quant à l'utilisation de la déconvolution pour inférer l'hétérogénéité tumorale. Nous avons ensuite exploré l'intégration de deux types de données (RNA seq et méthylation) dans un second data challenge qui a notamment débouché sur la mise en place d'une plateforme de benchmarking.Enfin, la troisième partie de ma thèse fait le lien entre les deux premières. En associant la déconvolution et l’analyse différentielle, j’ai développé un algorithme qui permet d’inférer la dérégulation des gènes dans les cellules cancéreuses uniquement, et d’associer cette dérégulation à la composition du microenvironnement tumoral. Après l'étude poussée des différents paramètres de la méthode sur des jeux de simulations, cette approche devrait permettre de déterminer des gènes dont la dérégulation dans le cancer joue un rôle dans l’organisation du microenvironnement tumoral, ouvrant ainsi la porte à la possibilité d'identifier de nouveaux biomarqueurs de l'hétérogénéité qui pourraient être en lien avec la virulence du cancer, et ainsi globalement d'obtenir une meilleure compréhension des mécanismes biologiques impliqués.
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- 2021
4. MOESM2 of Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software
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Decamps, Clémentine, Privé, Florian, Bacher, Raphael, Jost, Daniel, Waguet, Arthur, Houseman, Eugene, Lurie, Eugene, Lutsik, Pavlo, Milosavljevic, Aleksandar, Scherer, Michael, Blum, Michael, and Richard, Magali
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Additional file 2: Table S1. Number of remaining probes in Fig. 3. Table S2. Number of remaining probes in Additional file 1: Figure S8. Table S3. Mean execution time in Fig. 7 (minutes). Table S4. Table of cells used for simulations. Table S5. Informative loci.
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- 2020
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5. PenDA, a rank-based method for personalized differential analysis: Application to lung cancer
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Richard, Magali, primary, Decamps, Clémentine, additional, Chuffart, Florent, additional, Brambilla, Elisabeth, additional, Rousseaux, Sophie, additional, Khochbin, Saadi, additional, and Jost, Daniel, additional
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- 2020
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6. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.
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Decamps, Clémentine, Arnaud, Alexis, Petitprez, Florent, Ayadi, Mira, Baurès, Aurélia, Armenoult, Lucile, HADACA consortium, Alcala, N., Arnaud, A., Avila Cobos, F., Batista, Luciana, Batto, A.-F., Blum, Y., Chuffart, F., Cros, J., Decamps, C., Dirian, L., Doncevic, D., Durif, G., and Bahena Hernandez, S. Y.
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HETEROGENEITY , *CANCER invasiveness , *TRANSCRIPTOMES , *DECONVOLUTION (Mathematics) , *MEDICAL sciences , *SOURCE code - Abstract
Background: Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. Results: We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. Conclusion: DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453. [ABSTRACT FROM AUTHOR]
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- 2021
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7. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software.
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Decamps, Clémentine, Privé, Florian, Bacher, Raphael, Jost, Daniel, Waguet, Arthur, HADACA consortium, Achard, Sophie, Amblard, Elise, Bergmann, Fabian, Blum, Michael, Blum, Yuna, Bottaz-Bosson, Guillaume, Broseus, Lucile, Chuffart, Florent, Devijver, Emilie, Durif, Ghislain, Feofanov, Vassili, Houseman, Eugene Andres, Gallopin, Melina, and Jedynak, Paulina
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DECONVOLUTION (Mathematics) , *HETEROGENEITY , *INTEGRATED software , *DNA methylation , *CANCER cells - Abstract
Background: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. Results: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30–35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Conclusion: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir. [ABSTRACT FROM AUTHOR]
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- 2020
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8. Additional file 1 of DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification
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'Decamps, Clémentine
9. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification
- Author
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Decamps, Clémentine, Arnaud, Alexis, Petitprez, Florent, Ayadi, Mira, Baurès, Aurélia, Armenoult, Lucile, Alcala, N., Arnaud, A., Avila Cobos, Francisco, Batista, Luciana, Batto, A.-F., Blum, Y., Chuffart, F., Cros, J., Decamps, C., Dirian, L., Doncevic, D., Durif, G., Bahena Hernandez, S. Y., Jakobi, M., Jardillier, R., Jeanmougin, M., Jedynak, P., Jumentier, B., Kakoichankava, A., Kondili, Maria, Liu, J., Maie, T., Marécaille, J., Merlevede, J., Meylan, M., Nazarov, P., Newar, K., Nyrén, K., Petitprez, F., Novella Rausell, C., Richard, M., Scherer, M., Sompairac, N., Waury, K., Xie, T., Zacharouli, M.-A., Escalera, Sergio, Guyon, Isabelle, Nicolle, Rémy, Tomasini, Richard, de Reyniès, Aurélien, Cros, Jérôme, Blum, Yuna, Richard, Magali, HADACA consortium, [missing], Translational Innovation in Medicine and Complexity / Recherche Translationnelle et Innovation en Médecine et Complexité - UMR 5525 (TIMC ), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Ligue Nationale Contre le Cancer - Paris, Ligue Nationnale Contre le Cancer, University of Barcelona, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), A&O (Apprentissage et Optimisation) (A&O), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Algorithmes, Apprentissage et Calcul (AAC), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Cancérologie de Marseille (CRCM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU), Institut de Génétique et Développement de Rennes (IGDR), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), The research leading to these results was supported by Univ. Grenoble-Alpes via the Grenoble Alpes Data Institute [MR, AA] (ANR-15-IDEX-02), EIT Health Campus HADACA and COMETH programs [MR, YB], activities 19359 and 20377 and the Ligue Nationale Contre le Cancer. Other fundings: South-Eastern Norway Regional Health Authority (project number 2019030 [MJ]), European IMI IMMUCAN project [NS], European Union's Horizon 2020 program (Grant 826121, iPC project, [JM, FAC]). This article did not receive specific sponsorship in the design of the study, analysis, interpretation of data and in writing the manuscript., ANR-15-IDEX-0002,UGA,IDEX UGA(2015), European Project: 826121,iPaediatricCurie, Biologie Computationnelle et Modélisation (TIMC-BCM ), Université Grenoble Alpes (UGA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Ligue Nationale Contre le Cancer (LNCC), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Algorithmes, Apprentissage et Calcul (AAC), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Richard, Magali, IDEX UGA - - UGA2015 - ANR-15-IDEX-0002 - IDEX - VALID, and European Union’s Horizon 2020 program (grant No. 826121, iPC project) - iPaediatricCurie - 826121 - INCOMING
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Classificació de tumors ,Source code ,Computer science ,Deconvolution ,computer.software_genre ,Biochemistry ,Omics integration ,0302 clinical medicine ,Resource (project management) ,Structural Biology ,Medicine and Health Sciences ,Biology (General) ,Càncer ,media_common ,Cancer ,0303 health sciences ,[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,DNA methylation ,Applied Mathematics ,Benchmarking ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,Computer algorithms ,3. Good health ,Computer Science Applications ,Benchmarking platform ,030220 oncology & carcinogenesis ,Benchmark (computing) ,Algorithms ,EXPRESSION ,Cellular heterogeneity ,QH301-705.5 ,media_common.quotation_subject ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Adenocarcinoma ,Machine learning ,Set (abstract data type) ,03 medical and health sciences ,Computational Deconvolution ,Humans ,Leverage (statistics) ,Algorismes computacionals ,CELL ,Molecular Biology ,030304 developmental biology ,business.industry ,Tumors classification ,Computational Biology ,Gold standard (test) ,Pancreatic Neoplasms ,Artificial intelligence ,business ,Transcriptome ,computer ,Software - Abstract
Background Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. Results We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. Conclusion DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453.
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