mbecModelVarianceRDA: Estimate Explained Variance with Redundancy Analysis

In buschlab/MBECS: Evaluation and correction of batch effects in microbiome data-sets

Description

The function offers a selection of methods/algorithms to estimate the proportion of variance that can be attributed to covariates of interest. This shows, how much variation is explained by the treatment effect, which proportion is introduced by processing in batches and the leftover variance, i.e., residuals that are not currently explained. Covariates of interest (CoI) are selected by the user and the function will incorporate them into the model.

Usage

mbecModelVarianceRDA(model.vars, tmp.cnts, tmp.meta, type)

Arguments

model.vars	Covariates to use for model building.
tmp.cnts	Abundance matrix in 'sample x feature' orientation.
tmp.meta	Covariate table that contains at least the used variables.
type	String the denotes data source, i.e., one of "otu","clr" or "tss" for the transformed counts or the label of the batch corrected count-matrix.

Details

partial Redundancy Analysis (rda): Iterates over given covariates, builds a model of all covariates that includes one variable as condition/constraint and then fits it to the feature abundance matrix. The difference in explained variance between the full- and the constrained-model is then attributed to the constraint. (cnts ~ group + Condition(batch) vs. cnts ~ group + batch)

Value

Data.frame that contains proportions of variance for given covariates in a partial redundancy analysis approach.

buschlab/MBECS documentation built on Jan. 21, 2022, 1:27 a.m.