power assessment in microbiome case-control studies
Power analysis is essential to decide the sample size of metagenomic sequencing experiments in a case-control study for identifying differentially abundant microbes. However, the complexity of microbiome data characteristics such as excessive zeros, over-dispersion, compositional effect, intrinsically microbial correlations and variable sequencing depths makes the power analysis particularly challenging as the analytical form is usually unavailable. Here, we develop a simulation-based strategy and R package powmic to estimate the empirical statistical power while considering the complexity of data characteristics.
Li Chen li.chen1@ufl.edu
#MAGMA
library(devtools)
install_gitlab("arcgl/rmagma")
library(rMAGMA)
#CCREPE
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ccrepe")
#SpiecEasi
library(devtools)
install_github("zdk123/SpiecEasi")
```{r block2, echo=TRUE,eval=FALSE} BiocManager::install(c("biomformat","edgeR","DESeq2")) install.packages(c('ggplot2','gridExtra','lattice','reshape2','MASS','dirmult','nonnest2'))
# Install powmic
```r
install.packages("devtools")
library(devtools)
install_github("lichen-lab/powmic")
library(powmic)
browseVignettes('powmic')
Tutorial is available here
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