mbecRunCorrections: Run Correction Pipeline

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

Description

Run all correction algorithms selected by method and add corrected counts as matrices to the data-set.

Usage

mbecRunCorrections(
  input.obj,
  model.vars = c("batch", "group"),
  type = "clr",
  method = c("ruv3", "bmc", "bat", "rbe", "pn", "svd"),
  nc.features = NULL
)

Arguments

input.obj	phyloseq object or numeric matrix (correct orientation is handeled internally)
model.vars	two covariates of interest to select by first variable selects panels and second one determines coloring
type	One of 'otu', 'tss' or 'clr' to determine the abundance matrix to use for evaluation.
method	algorithms to use
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

Value

an object of class MbecDataSet

Examples

# This call will use 'ComBat' for batch effect correction and store the new
# counts in a list-obj in the output.
study.obj <- mbecRunCorrections(input.obj=dummy.mbec,
model.vars=c("batch","group"), method=c("bat","bmc"))

# This call will use 'Percentile Normalization' for batch effect correction
# and replace the old count matrix.
study.obj <- mbecRunCorrections(dummy.mbec, model.vars=c("batch","group"),
method=c("pn"))

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