Team-leads: Jenna Oberstaller and Justin Gibbons
Members:
- Samira Jahangiri, Francesca Prieto, Vyoma Sheth, Omkar Dokur, Xiangyun (Sherry) Liao
Microbiome power-calculation tool for biologists: towards rigorous, reproducible microbiome study-design
Statistical Power Calculations aid in figuring out the sample size in order to determine the probability of acquiring significant results from a statistical test when an effect is present. It is difficult to determine a correlational relationship between certain taxa and disease. These differences can stem from differing definitions of what a clinical population signifies within different studies, how to handle sample preparation, and the overall bioinformatics and statistical tools (Debelius, et al., 2016). Determining effect size is of crucial importance to aid in determining the differences within community profiling. Effect size is the quantitative portion of the differences between two or more groups. For example, power and sample-size estimation along with PERMANOVA has been utilized in order to ensure that effect expected from the interference of interest is detectable (Kelly, et al., 2015).
We sought to provide an intuitive power- and effect-size calculator-tool for biologists with limited computational experience.
Provide an intuitive power- and effect-size calculator-tool for biologists with limited computational experience.
we used R-Shiny to build a user-interface around reference microbiome-data OTU-tables from a variety of human body-sites on which we pre-computed statistical power and its relationship to sample-size using the microPower package (Kelly et al., 2015).
We added additional functionality to compute effect-size from these data-sets using linear modeling.
The following packages are required to run MicroPowerPlus:
RStudio
R-packages available from CRAN repository:
- library(shiny)
- library(plotly)
- library(tidyverse)
Download and open the "app.R" file in RStudio. In the upper-right corner of RStudio, press the "Run App" button.
