Statistical challenges for human microbiome analysis

Javier Rivera-Pinto; Carla Estany; Roger Paredes; M. Luz Calle; Marc Noguera-Julián

doi:10.1007/978-3-319-55639-0_8

Statistical challenges for human microbiome analysis

Javier Rivera-Pinto, Carla Estany, Roger Paredes, M. Luz Calle, Marc Noguera-Julián

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Resum

© 2017, Springer International Publishing AG. DNA sequencing technologies have revolutionized microbiome studies. In this work we analyze microbiome data from an HIV study focused on the characterization of microbiome composition in HIV-1 infected patients. A 155 cohort of HIV infected and non-infected individuals is analyzed to characterize dietary and gut microbiome association in this group of patients. A penalized Dirichlet Multinomial regression model has been considered. The assumed underlying Dirichlet distribution in this modelization provides additional flexibility to the multinomial model which results in a better fit of the typically overdispersed microbiome data.

Idioma original	Anglès
Títol de la publicació	Trends in Mathematics
Pàgines	47-51
Nombre de pàgines	4
Volum	7
ISBN (electrònic)	2297-024X
DOIs	https://doi.org/10.1007/978-3-319-55639-0_8
Estat de la publicació	Publicada - 1 de gen. 2017

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10.1007/978-3-319-55639-0_8

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abstract = "{\textcopyright} 2017, Springer International Publishing AG. DNA sequencing technologies have revolutionized microbiome studies. In this work we analyze microbiome data from an HIV study focused on the characterization of microbiome composition in HIV-1 infected patients. A 155 cohort of HIV infected and non-infected individuals is analyzed to characterize dietary and gut microbiome association in this group of patients. A penalized Dirichlet Multinomial regression model has been considered. The assumed underlying Dirichlet distribution in this modelization provides additional flexibility to the multinomial model which results in a better fit of the typically overdispersed microbiome data.",

author = "Javier Rivera-Pinto and Carla Estany and Roger Paredes and Calle, \{M. Luz\} and Marc Noguera-Juli{\'a}n",

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AU - Rivera-Pinto, Javier

AU - Estany, Carla

AU - Paredes, Roger

AU - Calle, M. Luz

AU - Noguera-Julián, Marc

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N2 - © 2017, Springer International Publishing AG. DNA sequencing technologies have revolutionized microbiome studies. In this work we analyze microbiome data from an HIV study focused on the characterization of microbiome composition in HIV-1 infected patients. A 155 cohort of HIV infected and non-infected individuals is analyzed to characterize dietary and gut microbiome association in this group of patients. A penalized Dirichlet Multinomial regression model has been considered. The assumed underlying Dirichlet distribution in this modelization provides additional flexibility to the multinomial model which results in a better fit of the typically overdispersed microbiome data.

AB - © 2017, Springer International Publishing AG. DNA sequencing technologies have revolutionized microbiome studies. In this work we analyze microbiome data from an HIV study focused on the characterization of microbiome composition in HIV-1 infected patients. A 155 cohort of HIV infected and non-infected individuals is analyzed to characterize dietary and gut microbiome association in this group of patients. A penalized Dirichlet Multinomial regression model has been considered. The assumed underlying Dirichlet distribution in this modelization provides additional flexibility to the multinomial model which results in a better fit of the typically overdispersed microbiome data.

UR - https://www.scopus.com/pages/publications/85019101248

U2 - 10.1007/978-3-319-55639-0_8

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BT - Trends in Mathematics

ER -

Statistical challenges for human microbiome analysis

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SDG de les Nacions Unides

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