ComBatFamQC (version 1.0.5)

data_prep: Data Preparation

Description

Prepares the dataset for effective use in batch effect diagnostics, harmonization, and post-harmonization downstream analysis processes within the ComBatFamQC package.

Usage

data_prep(
  stage = "harmonization",
  result = NULL,
  features = NULL,
  batch = NULL,
  covariates = NULL,
  df = NULL,
  type = "lm",
  random = NULL,
  smooth = NULL,
  interaction = NULL,
  smooth_int_type = NULL,
  predict = FALSE,
  object = NULL
)

Value

data_prep returns a list containing the processed data and parameter-related information for batch effect diagnostics, harmonization, and post-harmonization downstream analysis.

Arguments

stage

Specifies the stage of analysis for which the data preparation is intended: harmonization or residual.

result

A list derived from visual_prep() that contains dataset and batch effect diagnostic information for Shiny visualization. Can be skipped if features, batch, covariates and df are provided.

features

The name of the features to be harmonized. This can be skipped if result is provided.

batch

The name of the batch variable. Can be skipped if result is provided.

covariates

The names of covariates supplied to model. This can be be skipped if result is provided.

df

The dataset to be harmonized. This can be be skipped if result is provided.

type

The name of a regression model to be used in batch effect diagnostics, harmonization, and the post-harmonization stage: "lmer", "lm", "gam".

random

The variable name of a random effect in linear mixed effect model.

smooth

The name of the covariates that require a smooth function.

interaction

Expression of interaction terms supplied to model (eg: "age,diagnosis").

smooth_int_type

A vector that indicates the types of interaction in gam models. By default, smooth_int_type is set to be NULL, "linear" represents linear interaction terms. "categorical-continuous", "factor-smooth" both represent categorical-continuous interactions ("factor-smooth" includes categorical variable as part of the smooth), "tensor" represents interactions with different scales, and "smooth-smooth" represents interaction between smoothed variables.

predict

A boolean variable indicating whether to run ComBat from scratch or apply existing model to new dataset (currently only work for "original ComBat" and "ComBat-GAM").

object

Existing ComBat model.

Examples

data_prep(stage = "harmonization", result = NULL, features = colnames(adni)[43:53],
batch = "manufac", covariates = "AGE", df = head(adni, 100), type = "lm", random = NULL,
smooth = NULL, interaction = NULL, smooth_int_type = NULL, predict = FALSE, object = NULL)