predict.comfam: Apply Harmonization to New Data

In ComBatFamQC: Comprehensive Batch Effect Diagnostics and Harmonization

Apply Harmonization to New Data

Description

Using parameters estimated via comfam, apply harmonization on new data. predict.comfam will estimate new batch adjustments if new batches are specified. For batches with existing estimates, the estimates from object are used. Harmonization targets are the same as object (e.g. ref.batch from object if specified).

Usage

## S3 method for class 'comfam'
predict(
  object,
  newdata,
  newbat,
  newcovar = NULL,
  robust.LS = FALSE,
  eb = TRUE,
  ...
)

Arguments

object	Object of class comfam, typically output of the harmonization function in this package.
newdata	n x p data frame or matrix of new observations where p is the number of features and n is the number of subjects. The features must match the original data used in object
newbat	Factor indicating new batch (often equivalent to site or scanner)
newcovar	Data frame or matrix of new covariates supplied to model. Must contain all variables specified in the original formula used in object.
robust.LS	If TRUE, uses robust location and scale estimators for new batch effect estimates Currently uses median and biweight midvariance
eb	If TRUE, uses ComBat model with empirical Bayes for new batches
...	Additional arguments to predict for the class of model (e.g. predict.lm for ComBat)

Details

Note: The function currently does not support models of class lmer (e.g., from lmer).

Value

predict.comfam returns a list containing the following components:

dat.combat	New harmonized data as a matrix with same dimensions as newdata
batch.info	New batch information, including reference batch if specified
fits	List of model fits from regression step, forwarded from object
estimates	List of estimates from standardization and batch effect correction, including new batches if relevant

Examples

com_out <- comfam(iris[1:75,1:2], iris$Species[1:75])

# out-of-sample with new batch
out_pred <- predict(com_out, iris[76:150,1:2], iris$Species[76:150])

# in-sample
in_pred <- predict(com_out, iris[1:25,1:2], iris$Species[1:25])
max(in_pred$dat.combat - com_out$dat.combat[1:25,])

ComBatFamQC documentation built on April 4, 2025, 12:24 a.m.