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powmic

power assessment in microbiome case-control studies

Description

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.

Maintainer

Li Chen [email protected]

Install R packages for infeerring microbial correlation network

#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")

Install other dependent R packages

BiocManager::install(c("biomformat","edgeR","DESeq2"))
install.packages(c('ggplot2','gridExtra','lattice','reshape2','MASS','dirmult','nonnest2'))

Install powmic

install.packages("devtools")
library(devtools)
install_github("lichen-lab/powmic")

Tutorial

library(powmic)
browseVignettes('powmic')

Tutorial is available here