Federico Mattiello1, Bie Verbist2, Karoline Faust3, Jeroen Raes3, William D Shannon4, Luc Bijnens2, Olivier Thas5. 1. Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Gent, 9000. 2. Janssen Pharmaceutica, Turnhoutseweg 30, Beerse, 2340, Belgium. 3. KU Leuven, Laboratory of Molecular Bacteriology and Department of Microbiology and Immunology, Herestraat 49, Leuven, 3000, Belgium. 4. BioRankings, 4041 Forest Park Ave, St.Louis, MO 63108, USA. 5. Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Gent, 9000 University of Wollongong, National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, Australia.
Abstract
UNLABELLED: : When designing a case-control study to investigate differences in microbial composition, it is fundamental to assess the sample sizes needed to detect an hypothesized difference with sufficient statistical power. Our application includes power calculation for (i) a recoded version of the two-sample generalized Wald test of the 'HMP' R-package for comparing community composition, and (ii) the Wilcoxon-Mann-Whitney test for comparing operational taxonomic unit-specific abundances between two samples (optional). The simulation-based power calculations make use of the Dirichlet-Multinomial model to describe and generate abundances. The web interface allows for easy specification of sample and effect sizes. As an illustration of our application, we compared the statistical power of the two tests, with and without stratification of samples. We observed that statistical power increases considerably when stratification is employed, meaning that less samples are needed to detect the same effect size with the same power. AVAILABILITY AND IMPLEMENTATION: The web interface is written in R code using Shiny (RStudio Inc., 2016) and it is available at https://fedematt.shinyapps.io/shinyMB The R code for the recoded generalized Wald test can be found at https://github.com/mafed/msWaldHMP CONTACT: Federico.Mattiello@UGent.be.
UNLABELLED: : When designing a case-control study to investigate differences in microbial composition, it is fundamental to assess the sample sizes needed to detect an hypothesized difference with sufficient statistical power. Our application includes power calculation for (i) a recoded version of the two-sample generalized Wald test of the 'HMP' R-package for comparing community composition, and (ii) the Wilcoxon-Mann-Whitney test for comparing operational taxonomic unit-specific abundances between two samples (optional). The simulation-based power calculations make use of the Dirichlet-Multinomial model to describe and generate abundances. The web interface allows for easy specification of sample and effect sizes. As an illustration of our application, we compared the statistical power of the two tests, with and without stratification of samples. We observed that statistical power increases considerably when stratification is employed, meaning that less samples are needed to detect the same effect size with the same power. AVAILABILITY AND IMPLEMENTATION: The web interface is written in R code using Shiny (RStudio Inc., 2016) and it is available at https://fedematt.shinyapps.io/shinyMB The R code for the recoded generalized Wald test can be found at https://github.com/mafed/msWaldHMP CONTACT: Federico.Mattiello@UGent.be.
Authors: Sharon M Carney; Jose C Clemente; Michael J Cox; Robert P Dickson; Yvonne J Huang; Georgios D Kitsios; Kirsten M Kloepfer; Janice M Leung; Tricia D LeVan; Philip L Molyneaux; Bethany B Moore; David N O'Dwyer; Leopoldo N Segal; Stavros Garantziotis Journal: Am J Respir Cell Mol Biol Date: 2020-03 Impact factor: 6.914
Authors: Elise M Didion; Zakee L Sabree; Laura Kenyon; Gabriela Nine; Richard W Hagan; Sema Osman; Joshua B Benoit Journal: J Insect Physiol Date: 2021-08-17 Impact factor: 2.608
Authors: George B Saffouri; Robin R Shields-Cutler; Jun Chen; Yi Yang; Heather R Lekatz; Vanessa L Hale; Janice M Cho; Eric J Battaglioli; Yogesh Bhattarai; Kevin J Thompson; Krishna K Kalari; Gaurav Behera; Jonathan C Berry; Stephanie A Peters; Robin Patel; Audrey N Schuetz; Jeremiah J Faith; Michael Camilleri; Justin L Sonnenburg; Gianrico Farrugia; Jonathan R Swann; Madhusudan Grover; Dan Knights; Purna C Kashyap Journal: Nat Commun Date: 2019-05-01 Impact factor: 14.919
Authors: Laura E Peachey; Cecilia Castro; Rebecca A Molena; Timothy P Jenkins; Julian L Griffin; Cinzia Cantacessi Journal: Sci Rep Date: 2019-07-31 Impact factor: 4.379
Authors: Joanna W Szopinska; Raphaële Gresse; Saskia van der Marel; Jos Boekhorst; Sabina Lukovac; Iris van Swam; Barbara Franke; Harro Timmerman; Clara Belzer; Alejandro Arias Vasquez Journal: BMC Microbiol Date: 2018-09-06 Impact factor: 3.605