mbecSVD: Singular Value Decomposition (SVD)

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

Description

Basically perform matrix factorization and compute singular eigenvectors (SEV). Assume that the first SEV captures the batch-effect and remove this effect from the data. The interesting thing is that this works pretty well. But since the SEVs are latent factors that are (most likely) confounded with other effects it is not obvious to me that this is the optimal approach to solve this issue.

Usage

mbecSVD(input.obj, model.vars, type = "clr")

Arguments

input.obj	phyloseq object or numeric matrix (correct orientation is handeled internally)
model.vars	Vector of covariate names. First element relates to batch.
type	Which abundance matrix to use, one of 'otu, tss, clr'. DEFAULT is 'clr'.

Details

ToDo: IF I find the time to works on "my-own-approach" then this is the point to start from!!!

The input for this function is supposed to be an MbecData object that contains total sum-scaled and cumulative log-ratio transformed abundance matrices. Output will be a matrix of corrected abundances.

Value

A matrix of batch-effect corrected counts

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