adjust_batch: Zero-inflated empirical Bayes adjustment of batch effect in...

In MMUPHin: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies

Description

adjust_batch takes as input a feature-by-sample matrix of microbial abundances, and performs batch effect adjustment given provided batch and optional covariate variables. It returns the batch-adjusted abundance matrix. Additional options and parameters can be passed through the control parameter as a list (see details).

Usage

adjust_batch(feature_abd, batch, covariates = NULL, data, control)

Arguments

feature_abd	feature-by-sample matrix of abundances (proportions or counts).
batch	name of the batch variable. This variable in data should be a factor variable and will be converted to so with a warning if otherwise.
covariates	name(s) of covariates to adjust for in the batch correction model.
data	data frame of metadata, columns must include batch and covariates (if specified).
control	a named list of additional control parameters. See details.

Details

control should be provided as a named list of the following components (can be a subset).

zero_inflation

logical. Indicates whether or not a zero-inflated model should be run. Default to TRUE (zero-inflated model). If set to FALSE then the correction will be similar to ComBat as provided in the sva package.

pseudo_count

numeric. Pseudo count to add feature_abd before the methods' log transformation. Default to NULL, in which case adjust_batch will set the pseudo count automatically to half of minimal non-zero values in feature_abd.

diagnostic_plot

character. Name for the generated diagnostic figure file. Default to "adjust_batch_diagnostic.pdf". Can be set to NULL in which case no output will be generated.

conv

numeric. Convergence threshold for the method's iterative algorithm for shrinking batch effect parameters. Default to 1e-4.

maxit

integer. Maximum number of iterations allowed for the method's iterative algorithm. Default to 1000.

verbose

logical. Indicates whether or not verbose information will be printed.

Value

a list, with the following components:

feature_abd_adj

feature-by-sample matrix of batch-adjusted abundances, normalized to the same per-sample total abundance as feature_abd.

control

list of additional control parameters used in the function call.

Author(s)

Siyuan Ma, [e-mail]

Examples

data("CRC_abd", "CRC_meta")
CRC_abd_adj <- adjust_batch(feature_abd = CRC_abd, 
                            batch = "studyID", 
                            covariates = "study_condition",
                            data = CRC_meta)$feature_abd_adj

Example output

feature_abd is proportions
Found 5 batches
Adjusting for 1 covariate(s) or covariate(s) level(s)
Pseudo count is not specified and set to half of minimal non-zero value: 5e-08
Adjusting for (after filtering) 457 features
Standardizing data across features
Estimating batch difference parameters and EB priors
Performing shrinkage adjustments on batch difference parameters
Performing batch corrections

MMUPHin documentation built on April 9, 2021, 6:01 p.m.