Search

Your search keyword '"multi patient analysis"' showing total 3 results
Start Over Descriptor "multi patient analysis"
3 results on '"multi patient analysis"'

1. Analysis of genomic markers:Make it easy with the R package MPAgenomics

Author

Grimonprez, Q. (Quentin), Celisse, A. (Alain), Marot, G. (Guillemette), CHU Lille, CNRS, Université de Lille, and MOdel for Data Analysis and Learning [MODAL]

Subjects
Segmentation, Genomic marker selection, SNP array, Calling, Normalization, Multi patient analysis
Abstract

MPAgenomics, standing for multi-patients analysis of genomicmarkers, is an R-package which enables to study several copy number andSNP data profiles at the same time. It offers wrappers from commonlyused packages (aroma [Bengtsson, 2004], changepoint [Killick et al., 2013],cghcall [van de Wiel et al., 2007], glmnet [Friedman et al., 2010]) to of-fer a pipeline for beginners in R. Thus, normalization, segmentation andcalling of copy-number or SNP data profiles can be easily performed atthe same time. The special architecture of [Bengtsson, 2004] packages isautomatically created and outputs to right formats for further analysesare suggested. Therefore, more advanced users can use their own methodat one point if they prefer to separate the global analysis in several steps.In addition to these useful wrappers, we propose a strategy to improvethe choice of the penalty parameter used in [Killick et al., 2013] for seg-mentation. As far as multi-patients analysis is concerned, we suggest toselect relevant markers associated with a given response thanks to wrap-pers with the R-packages glmnet [Friedman et al., 2010] and HDPenReg(still under development). Functionalities already implemented in ourpackage HDPenReg, standing for penalized regression in high dimension,will be briefly presented.

Published
2014

2. Analysis of genomic markers: Make it easy with the R package MPAgenomics

Author

Grimonprez, Quentin, Celisse, Alain, Marot, Guillemette, MOdel for Data Analysis and Learning (MODAL), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Paul Painlevé - UMR 8524 (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille)-Université de Lille, Sciences et Technologies, Laboratoire Paul Painlevé - UMR 8524 (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), inria ADT MPAgenomics, Laboratoire de Génomique des Microorganismes, Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), and Grimonprez, Quentin

Subjects
[STAT]Statistics [stat], [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], normalization, segmentation, multi patient analysis, SNP array, genomic marker selection, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], calling, [STAT] Statistics [stat]
Abstract

National audience; MPAgenomics, standing for multi-patients analysis of genomicmarkers, is an R-package which enables to study several copy number andSNP data profiles at the same time. It offers wrappers from commonlyused packages (aroma [Bengtsson, 2004], changepoint [Killick et al., 2013],cghcall [van de Wiel et al., 2007], glmnet [Friedman et al., 2010]) to of-fer a pipeline for beginners in R. Thus, normalization, segmentation andcalling of copy-number or SNP data profiles can be easily performed atthe same time. The special architecture of [Bengtsson, 2004] packages isautomatically created and outputs to right formats for further analysesare suggested. Therefore, more advanced users can use their own methodat one point if they prefer to separate the global analysis in several steps.In addition to these useful wrappers, we propose a strategy to improvethe choice of the penalty parameter used in [Killick et al., 2013] for seg-mentation. As far as multi-patients analysis is concerned, we suggest toselect relevant markers associated with a given response thanks to wrap-pers with the R-packages glmnet [Friedman et al., 2010] and HDPenReg(still under development). Functionalities already implemented in ourpackage HDPenReg, standing for penalized regression in high dimension,will be briefly presented.

Published
2014

3. MPAgenomics : An R package for multi-patients analysis of genomic markers

Author

Guillemette Marot, Quentin Grimonprez, Alain Celisse, Martin Figeac, Meyling Cheok, Samuel Blanck, MOdel for Data Analysis and Learning (MODAL), Laboratoire Paul Painlevé - UMR 8524 (LPP), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Centre de Recherche Jean-Pierre AUBERT Neurosciences et Cancer - U1172 Inserm - U837 (JPArc), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Lille Nord de France (COMUE)-Université de Lille, Plateforme de génomique fonctionnelle et structurelle [Lille], Institut pour la recherche sur le cancer de Lille [Lille] (IRCL)-Université de Lille, Droit et Santé, Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), inria ADT MPAGenomics, Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche Jean-Pierre AUBERT Neurosciences et Cancer - U837 (JPArc), Université Lille Nord de France (COMUE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, CHU Lille, CNRS, Inserm, Université de Lille, Laboratoire Paul Painlevé [LPP], MOdel for Data Analysis and Learning [MODAL], Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS], and Taibi, Nadia

Subjects
FOS: Computer and information sciences, computer.software_genre, Quantitative Biology - Quantitative Methods, 01 natural sciences, Biochemistry, 010104 statistics & probability, Software, Structural Biology, Segmentation, calling, Quantitative Methods (q-bio.QM), Multi-patient analysis, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Applied Mathematics, Multi patient analysis, SNP array, Genomic markers selection, Calling, Normalization, High-Throughput Nucleotide Sequencing, multi patient analysis, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, normalization, Marker selection, Data mining, Genetic Markers, Normalization (statistics), DNA Copy Number Variations, Segmentation of genomic data, genomic markers selection, Biology, Polymorphism, Single Nucleotide, Statistics - Applications, 03 medical and health sciences, Humans, Applications (stat.AP), 0101 mathematics, Molecular Biology, 030304 developmental biology, Penalized regression, SNP arrays, business.industry, R package, segmentation, Sequence Analysis, DNA, Pipeline (software), FOS: Biological sciences, business, computer
Abstract

Background Last generations of Single Nucleotide Polymorphism (SNP) arrays allow to study copy-number variations in addition to genotyping measures. Results MPAgenomics, standing for multi-patient analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation and (ii) selection of genomic markers from multi-patient copy number and SNP data profiles. It provides wrappers from commonly used packages to streamline their repeated (sometimes difficult) manipulation, offering an easy-to-use pipeline for beginners in R. The segmentation of successive multiple profiles (finding losses and gains) is performed with an automatic choice of parameters involved in the wrapped packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given outcome. Conclusions MPAgenomics provides an easy tool to analyze data from SNP arrays in R. The R-package MPAgenomics is available on CRAN.

Published
2014
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results