visual_prep: Batch Effect Diagnostic Visualization Preparation

In ComBatFamQC: Comprehensive Batch Effect Diagnostics and Harmonization

Batch Effect Diagnostic Visualization Preparation

Description

Prepare relevant datasets and statistical test results for batch/site effect diagnostic visualization.

Usage

visual_prep(
  type = "lm",
  features,
  batch,
  covariates = NULL,
  interaction = NULL,
  random = NULL,
  smooth = NULL,
  smooth_int_type = NULL,
  df,
  cores = detectCores(),
  mdmr = TRUE
)

Arguments

type	The name of a regression model to be used in batch effect diagnostics stage: "lmer", "lm", "gam".
features	The name of the features to be evaluated.
batch	The name of the batch variable.
covariates	Name of covariates supplied to model.
interaction	Expression of interaction terms supplied to model (eg: "age,diagnosis").
random	Variable name of a random effect in linear mixed effect model.
smooth	Variable name that requires a smooth function.
smooth_int_type	Indicates the type of interaction in gam models. By default, smooth_int_type is set to be "linear", representing 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.
df	Dataset to be evaluated.
cores	number of cores used for parallel computing.
mdmr	A boolean variable indicating whether to run the MDMR test (default: TRUE).

Value

visual_prep returns a list containing the following components:

residual_add_df	Residuals that might contain additive and multiplicative joint batch effects
residual_ml_df	Residuals that might contain multiplicative batch effect
pr.feature	PCA results
pca_summary	A dataframe containing the variance explained by Principal Components (PCs)
pca_df	A dataframe contains features in the form of PCs
tsne_df	A dataframe prepared for T-SNE plots
kr_test_df	A dataframe contains Kenward-Roger(KR) test results
fk_test_df	A dataframe contains Fligner-Killeen(FK) test results
mdmr.summary	A dataframe contains MDMR results
anova_test_df	A dataframe contains ANOVA test results
kw_test_df	A dataframe contains Kruskal-Wallis test results
lv_test_df	A dataframe contains Levene's test results
bl_test_df	A dataframe contains Bartlett's test results
red	A parameter to highlight significant p-values in result table
info	A list contains input information like batch, covariates, df etc

Examples

visual_prep(type = "lm", features = colnames(adni)[43:53], batch = "manufac",
covariates = c("AGE", "SEX", "DIAGNOSIS"), df = head(adni, 500), cores = 1)

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