run_ANCOM: Differential Expression Analysis by Analysis of composition...
In HuaZou/MyRtools: Statistic Approaches on Data Analysis

Description Usage Arguments Details Value Author(s) References Examples

ANCOM accounts for the underlying structure in the data and can be used for comparing the composition of microbiomes in two or more populations. ANCOM makes no distributional assumptions and can be implemented in a linear model framework to adjust for covariates as well as model longitudinal data. ANCOM also scales well to compare samples involving thousands of taxa.

run_ANCOM(dataset=ExpressionSet,
                 trim="none",
                 GroupVar="Group",
                 AdjVar=NULL,
                 RandVar=NULL,
                 Pvalue=0.05,
                 Wvalue=0.7)

`trim,`	Character; filter to apply.(default: trim="none").
`GroupVar,`	Character; design factor(default: "Group").
`AdjVar,`	Character; the adjusted variables.
`RandVar,`	Character; random effects for longitudinal analysis or repeated measure("~ 1 \| studyid").
`Pvalue,`	Numeric; significant level(default: 0.05).
`Wvalue,`	Numeric; W statistic for clarify the significant features(default: 0.7).
`Expression,`	ExpressionSet; (Required) ExpressionSet object.

12/3/2021 Guangzhou China

a list of results: significant features pass the threshold of W statistics A volcano plot of significant features

Hua Zou

Mandal et al. "Analysis of composition of microbiomes: a novel method for studying microbial composition", Microbial Ecology in Health & Disease, (2015), 26.

data(ExprSetRawCount)

ANCOM_res <- run_ANCOM(dataset=ExprSetRawCount, GroupVar="Group", AdjVar="Gender", RandVar=NULL, Pvalue=0.05, Wvalue=0.7)
ANCOM_res$res