mbecRUV4: Remove Unwanted Variation 4 (RUV-4)

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

Description

The updated version of RUV-2 also incorporates the residual matrix (w/o treatment effect) to estimate the unknown BEs. To that end it follows the same procedure in case there are no negative control variables and computes pseudo-controls from the data via l(m)m. As RUV-2, this algorithm also uses the parameter 'k' for the number of latent factors. RUV-4 brings the function 'getK()' that estimates this factor from the data itself. The calculated values are however not always reliable. A value of k=0 fo example can occur and should be set to 1 instead.

Usage

mbecRUV4(input.obj, model.vars, type = "clr", nc.features = NULL)

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'.
nc.features	(OPTIONAL) A vector of features names to be used as negative controls in RUV-3. If not supplied, the algorithm will use an 'lm' to find pseudo-negative controls

Details

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 vector of p-values.

Value

A vector of p-values that indicate significance of the batch-effect for the features.

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