Package 'ComBatFamQC'

Biweight Midvariance Calculation

Description

Compute a robust estimate of midvariance using the biweight method, which reduces the influence of outliers by applying a weighting function to the data based on their deviation from a central value.

Usage

.biweight_midvar(data, center = NULL, norm.unbiased = TRUE)

Arguments

data	A numeric vector containing the data points for which the biweight midvariance is to be calculated.
center	An optional parameter specifying the central location of the data. If not provided, the function defaults to using the median of the data.
norm.unbiased	A logical parameter (default: TRUE) indicating whether to use a normalization constant for unbiased estimation. When TRUE, the constant is adjusted to 9 divided by the quantile function of 0.75 from the standard normal distribution.

Value

A numeric value representing the robust biweight midvariance estimate.

Examples

data <- c(1, 2, 3, 4, 100)
biweight_var <- .biweight_midvar(data)
print(biweight_var)

---

Harmonization Data

Description

This data set is a sample data set from ADNI study.

Usage

data(adni)

Format

"adni" is a data frame with 2515 cases (rows) and 104 variables (columns). 62 features are included for harmonization purpose.

Source

ADNI

Examples

data(adni)
head(adni)

---

Age Trajectory Data

Description

This data set is used to observe life span age trend of brain structures.

Usage

data(age_df)

Format

"age_df" is a simulated data frame with 712 cases (rows) and 56 variables (columns). 51 rois are included.

Examples

data(age_df)
head(age_df)

---

Age Trend Estimates Generation

Description

A GAMLSS model using a Normal distribution was fitted separately to rois of interest, to establish normative reference ranges for the volume of a specific ROI as a function of age, adjusting for sex and intracranial volume.

Usage

age_list_gen(
  sub_df,
  lq = 0.25,
  hq = 0.75,
  mu = "smooth",
  sigma = "smooth",
  nu = "default",
  tau = "default"
)

Arguments

sub_df	A four-column dataset that contains age, sex, intracranial volume (ICV) and roi volume related information. The columns for age, sex, and ICV must be strictly named "age", "sex", and "icv".
lq	The lower bound quantile. eg: 0.25, 0.05
hq	The upper bound quantile. eg: 0.75, 0.95
mu	An indicator of whether to smooth age variable, include it as a linear term or only include the intercept in the mu formula. "smooth": y ~ pb(age), "linear": y ~ age, "default": y ~ 1.
sigma	An indicator of whether to smooth age variable, include it as a linear term or only include the intercept in the sigma formula. "smooth": ~ pb(age), "linear": ~ age, "default": ~ 1.
nu	An indicator of whether to smooth age variable, include it as a linear term or only include the intercept in the nu formula. "smooth": ~ pb(age), "linear": ~ age, "default": ~ 1.
tau	An indicator of whether to smooth age variable, include it as a linear term or only include the intercept in the tau formula. "smooth": ~ pb(age), "linear": ~ age, "default": ~ 1.

Value

age_list_gen returns a list containing the following components:

true_df	a dataframe contains the true age and ROI volume information
predicted_df_sex	a dataframe contains the estimated age trend adjusting sex and icv
model	the fitted GAMLSS model

Examples

sub_df <- age_df[,c("Volume_1", "age", "sex", "ICV_baseline")] |> na.omit()
colnames(sub_df) <- c("Volume_1", "age", "sex", "icv")
age_list_gen(sub_df = sub_df)

---

Export Brain ROI Age Trends

Description

Save all brain age trends into a single Excel file.

Usage

age_save(path, age_list)

Arguments

path	The path to save the excel file.
age_list	A list containing all ROIs' true volumes, age trend estimates, and the fitted GAMLSS model.

Value

This function does not return a value. It saves the data to the specified file.

Examples

if(interactive()){
sub_df <- age_df[,c("Volume_1", "age", "sex", "ICV_baseline")] |> na.omit()
colnames(sub_df) <- c("Volume_1", "age", "sex", "icv")
age_list <- list("Volume_1" = age_list_gen(sub_df = sub_df))

temp_dir <- tempfile()
dir.create(temp_dir)
age_save(temp_dir, age_list)
message("Age trend table saved to: ", temp_dir)
unlink(temp_dir, recursive = TRUE)
}

---

Lifespan Age Trends

Description

Provide estimated lifespan age trends of neuroimaging-derived brain structures through shiny app.

Usage

age_shiny(age_list, features, quantile_type, use_plotly = TRUE)

Arguments

age_list	A list containing all ROIs' true volumes, age trend estimates, and the fitted GAMLSS model.
features	A vector of roi names.
quantile_type	A vector of quantile types (e.g., c("quantile_25", "median", "quantile_75"))
use_plotly	A boolean variable that indicates whether to display the age plot using the plotly package.

Details

When this function is called, it starts a Shiny application in the user's default web browser. Execution is blocked until the app is closed.

Value

This function does not return a value. It launches a Shiny app.

Examples

sub_df <- age_df[,c("Volume_1", "age", "sex", "ICV_baseline")] |> na.omit()
colnames(sub_df) <- c("Volume_1", "age", "sex", "icv")
age_list <- list("Volume_1" = age_list_gen(sub_df = sub_df))
quantile_type <- c("quantile_25", "median", "quantile_75")
if(interactive()){
  age_shiny(age_list = age_list, features = "Volume_1", quantile_type = quantile_type)
}

---

Generate Age Trend Summary Table

Description

This function generates a summary table of age trend predictions for a specified quantile and demographic group. The table includes the predicted volume values, percentage changes between age intervals, and other details for either females, males, or a comparison between both genders.

Usage

age_table_gen(result, q = "median", s = "F")

Arguments

result	A data object containing prediction results and metadata.
q	A string specifying the quantile of interest (e.g., "quantile_50" for the median).
s	A string indicating the demographic group for which the summary table is generated: "F" for female "M" for male "F vs M" for comparison between females and males

Details

The function processes the input data to filter predictions based on the specified quantile and demographic group. It calculates percentage changes in predicted volume values for easier interpretation of trends. For gender comparisons ("F vs M"), it generates side-by-side columns for females and males.

The output table is formatted using the DT package with additional features, such as CSV and Excel export options.

Value

A datatable object (from the DT package) containing the age trend summary table with the following columns:

• Age: The age values at regular intervals (rounded to the nearest 10).

• Percentile.Volume: The predicted volume values for the specified quantile (only for females or males).

• ⁠PercentageChange (%)⁠: The percentage change in volume between consecutive age intervals (only for females or males).

• Percentile.Volume_F: The predicted volume values for females (when comparing genders).

• Percentile.Volume_M: The predicted volume values for males (when comparing genders).

• ⁠PercentageChange_F (%)⁠: The percentage change for females (when comparing genders).

• ⁠PercentageChange_M (%)⁠: The percentage change for males (when comparing genders).

Examples

sub_df <- age_df[,c("Volume_1", "age", "sex", "ICV_baseline")] |> na.omit()
colnames(sub_df) <- c("Volume_1", "age", "sex", "icv")
age_list <- list("Volume_1" = age_list_gen(sub_df = sub_df))
result <- age_list[[1]]
if(interactive()){
 age_table_gen(result, q = "median", s = "F")
}

---

Generate Age Trend Plot

Description

This function creates an age trend plot for a specified feature and demographic group based on GAMLSS model predictions. The function supports both static plots using ggplot2 and interactive plots using plotly.

Usage

age_trend_plot(
  age_list,
  f,
  s = "none",
  q = "median",
  cus_list = NULL,
  use_plotly = TRUE
)

Arguments

age_list	A list containing all ROIs' true volumes, age trend estimates, and the fitted GAMLSS model.
f	A string specifying the feature of interest within the age_list.
s	A string indicating the demographic group for visualization: "F" for female, "M" for male, "F vs M" for gender comparison, or "none" for base plot (true data) visualization.
q	A string specifying the quantile type (e.g., "quantile_75", "median", or "customization").
cus_list	A list object containing customized quantile predictions generated by the cus_result_gen function.
use_plotly	A boolean indicating whether to create an interactive plot using plotly (default: TRUE).

Details

The function overlays true data points with predicted quantile trends for the specified feature and demographic group. It supports customization for quantile visualization and uses precomputed results from the cus_result_gen function for enhanced flexibility.

Value

A plot object:

• If use_plotly = TRUE, returns a plotly interactive plot.

• If use_plotly = FALSE, returns a ggplot2 static plot.

Examples

sub_df <- age_df[,c("Volume_1", "age", "sex", "ICV_baseline")] |> na.omit()
colnames(sub_df) <- c("Volume_1", "age", "sex", "icv")
age_list <- list("Volume_1" = age_list_gen(sub_df = sub_df))
customized_results <- cus_result_gen(age_list, customized_q = 0.75, f = "Volume_1")

if(interactive()){
 age_trend_plot(
   age_list = age_list,
   f = "Volume_1",
   s = "F",
   q = "customization",
   cus_list = customized_results,
   use_plotly = TRUE
 )
}

---

ComBatFamily Harmonization

Description

Conduct harmonization using four types of methods: 1) Original ComBat, 2) Longitudinal ComBat, 3) ComBat-GAM, and 4) CovBat.

Usage

combat_harm(
  eb_check = FALSE,
  result = NULL,
  features = NULL,
  batch = NULL,
  covariates = NULL,
  df = NULL,
  type = "lm",
  random = NULL,
  smooth = NULL,
  interaction = NULL,
  smooth_int_type = NULL,
  family = "comfam",
  eb = TRUE,
  ref.batch = NULL,
  predict = FALSE,
  object = NULL,
  reference = NULL,
  out_ref_include = TRUE,
  ...
)

Arguments

eb_check	A boolean variable indicating whether the user wants to run the EB assumption test before harmonization.
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	Dataset to be harmonized. This can be be skipped if result is provided.
type	The name of a regression model to be used: "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.
family	The type of combat family to use, comfam or covfam.
eb	If TRUE, uses ComBat model with empirical Bayes for mean and variance harmonization
ref.batch	The name of the reference batch.
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.
reference	Dataset to be considered as the reference group.
out_ref_include	A boolean variable indicating whether the reference data should be included in the harmonized data output.
...	Additional arguments to comfam or covfam models.

Value

If the eb_check is set to be FALSE, then combat_harm returns a list containing the following components:

com_family	ComBat family to be considered: comfam, covfam
harmonized_df	Harmonized dataset
combat.object	Saved ComBat model and relevant information, such as the batch variable name and whether the EB method is used

If eb_check is set to be TRUE, then combat_harm will return a dataframe with the EB assumption test result.

Examples

combat_harm(features = colnames(adni)[43:53], batch = "manufac",
covariates = c("AGE", "SEX", "DIAGNOSIS"), df = head(adni,100), type = "lm")

---

Generate Diagnostic Plots for Batch Effect Analysis

Description

This function generates a variety of diagnostic plots for analyzing batch effects and their relationships with features and covariates. Depending on the specified plot type, it can create density plots, box plots, residual plots, PCA plots, T-SNE plots, and empirical Bayes diagnostic plots.

Usage

combat_plot_gen(
  result,
  f = NULL,
  batch_control = "No",
  batch_level = NULL,
  plot_name,
  c = NULL,
  smooth_method = "lm",
  alpha = 0.2,
  char_plot_type = "boxplot",
  text_status = "No",
  color = "No",
  label = "No",
  angle = 0,
  PC1 = NULL,
  PC2 = NULL,
  eb = TRUE,
  eb_df = NULL
)

Arguments

result	A list derived from visual_prep() that contains datasets and statistical test results for Shiny visualization.
f	A string specifying the feature of interest for visualization.
batch_control	A string indicating whether to include batch-specific controls. Defaults to "No".
batch_level	A vector specifying the batch levels to include in the plot. Used only when batch_control is not "No".
plot_name	A string specifying the type of plot to generate. Options include "batch_density", "cov_feature", "batch_summary", "cov_distribution", "resid_add", "resid_mul", "pca", "tsne", "eb_location", and "eb_scale".
c	A string specifying the covariate of interest for "cov_feature" or "cov_distribution" plots.
smooth_method	A string specifying the smoothing method for trend lines. Defaults to "lm" (linear model).
alpha	A numeric value between 0 and 1 controlling the transparency of trend lines. Defaults to 0.2.
char_plot_type	A string specifying the type of plot for categorical covariates. Options include "boxplot", "boxplot with points", and "density plot". Defaults to "boxplot".
text_status	A string indicating whether to display text annotations in the plot. Defaults to "No".
color	A string indicating whether to use color coding in plots. Defaults to "No".
label	A string indicating whether to include axis labels in the plot. Defaults to "No".
angle	A numeric value specifying the angle of x-axis labels. Defaults to 0.
PC1	A string specifying the first principal component for PCA plots.
PC2	A string specifying the second principal component for PCA plots.
eb	A logical value indicating whether to include empirical Bayes prior information in the plot. Defaults to TRUE.
eb_df	A data frame containing empirical Bayes information for generating eb_location and eb_scale plots.

Details

The function dynamically generates plots based on the plot_name parameter:

• "batch_density": Density plots of features by batch levels.

• "cov_feature": Covariate vs. feature plots with optional batch adjustments.

• "batch_summary": Bar plots summarizing batch-level distributions.

• "cov_distribution": Covariate distributions stratified by batch.

• "resid_add": Additive residual box plots.

• "resid_mul": Multiplicative residual box plots.

• "pca": Principal Component Analysis (PCA) plots.

• "tsne": T-SNE plots for dimensionality reduction.

• "eb_location": Empirical Bayes location parameter density plots.

• "eb_scale": Empirical Bayes scale parameter density plots.

Value

A ggplot object representing the specified diagnostic plot.

Examples

if(interactive()){
 result <- visual_prep(type = "lm", features = "thickness.left.cuneus",
 batch = "manufac", covariates = "AGE", df = adni[1:100, ], mdmr = FALSE, cores = 1)
 combat_plot_gen(result, f = "thickness.left.cuneus", plot_name = "batch_density")
 combat_plot_gen(result, f = "thickness.left.cuneus", c = "AGE", plot_name = "cov_feature")
}

---

Generate Diagnostic Tables for Batch Effect Analysis

Description

This function generates a variety of tables to summarize data and results from batch effect analyses. Depending on the specified table name, it can create data overview tables, exploratory analysis summaries, statistical test results, PCA summaries, and covariate distributions.

Usage

combat_table_gen(
  result,
  table_name,
  f = NULL,
  c = NULL,
  PC1 = NULL,
  PC2 = NULL
)

Arguments

result	A list derived from visual_prep() that contains datasets and statistical test results for Shiny visualization.
table_name	A string specifying the type of table to generate. Options include: "data_overview": Overview of the dataset, including covariates, features, and batch information. "exploratory_analysis": Summary of the selected feature and covariates. "summary_df": Summary of batch-level distributions, including batch removal status. "cov_table": Covariate summary table, displaying distributions for numeric or categorical covariates. "pc_variance": Variance explained by specified principal components. "mdmr": Results from the Multivariate Distance Matrix Regression (MDMR) analysis. "kenward_roger": Results from Kenward-Roger tests. "anova": Results from ANOVA tests. "kruskal_wallis": Results from Kruskal-Wallis tests. "fligner_killeen": Results from Fligner-Killeen tests. "levenes": Results from Levene's tests. "bartletts": Results from Bartlett's tests.
f	A string specifying the feature of interest for tables requiring a specific feature. Default is NULL.
c	A string specifying the covariate of interest for tables requiring a specific covariate. Default is NULL.
PC1	A string specifying the first principal component for PCA variance tables. Default is NULL.
PC2	A string specifying the second principal component for PCA variance tables. Default is NULL.

Details

The function dynamically generates tables based on the table_name parameter.

Value

A DT::datatable object containing the requested table.

Examples

if(interactive()){
 result <- visual_prep(type = "lm", features = "thickness.left.cuneus",
 batch = "manufac", covariates = "AGE", df = adni[1:100, ], mdmr = FALSE, cores = 1)
 combat_table_gen(result, table_name = "cov_table", c = "AGE")
 combat_table_gen(result, table_name = "pc_variance", PC1 = "PC1", PC2 = "PC2")
 }

---

ComBat Family Harmonization

Description

Implementation of the ComBat Family of harmonization methods allowing for flexible covariate modeling and alternative estimators for site effect adjustment. Support for modeling of both location and scale via GAMLSS and longitudinal harmonization via mixed effects models.

Usage

comfam(
  data,
  bat,
  covar = NULL,
  model = lm,
  formula = NULL,
  eb = TRUE,
  robust.LS = FALSE,
  ref.batch = NULL,
  ...
)

Arguments

data	n x p data frame or matrix of observations where p is the number of features and n is the number of subjects.
bat	Factor indicating batch (often equivalent to site or scanner)
covar	Data frame or matrix of covariates supplied to model
model	Model function. ComBat Family supports any models that take arguments formula and data, but are limited to models fitting with identity link (e.g. family = gaussian(link = "identity")). This includes lm, gam, gamlss, rq, lmer, and more
formula	Formula for model starting with ⁠y ~⁠ where y represents each feature
eb	If TRUE, uses ComBat model with empirical Bayes for mean and variance harmonization
robust.LS	If TRUE, uses robust location and scale estimators for error variance and site effect parameters. Currently uses median and biweight midvariance
ref.batch	Reference batch, must take value in levels(bat)
...	Additional arguments to model

Value

comfam returns a list containing the following components:

dat.combat	Harmonized data as a matrix with same dimensions as data
batch.info	Batch information, including reference batch if specified
fits	List of model fits from regression step, outputs of model for each feature
estimates	List of estimates from standardization and batch effect correction

See Also

predict.comfam for applying ComBat parameters for harmonization of new observations

Examples

comfam(iris[,1:2], iris$Species)
comfam(iris[,1:2], iris$Species, iris[3:4], lm, y ~ Petal.Length + Petal.Width)

---

Batch Effect Interactive Visualization

Description

Provides an interactive visualization of batch or site effects using a Shiny application.

Usage

comfam_shiny(result, after = FALSE)

Arguments

result	A list derived from visual_prep() that contains datasets and statistical test results for Shiny visualization.
after	A boolean variable indicating whether the batch effect diagnostic occurs before or after harmonization (default: FALSE).

Details

When this function is called, it starts a Shiny application in the user's default web browser. Execution is blocked until the app is closed.

Value

This function does not return a value. It launches a Shiny app.

Examples

result_lm <- visual_prep(type = "lm", features = colnames(adni)[43:53],
batch = "manufac", covariates = c("AGE", "SEX", "DIAGNOSIS"),
df = head(adni, 500), cores = 1)
if (interactive()) {
  comfam_shiny(result = result_lm)
}

---

CovBat Family Harmonization

Description

Implementation of the CovBat Family of harmonization methods allowing for removal of multivariate batch effects, flexible covariate modeling and alternative estimators for site effect adjustment. Support for modeling of both location and scale via GAMLSS. Additional support for modeling of covariate effects in score location and scale.

Usage

covfam(
  data,
  bat,
  covar = NULL,
  model = lm,
  formula = NULL,
  score.model = NULL,
  score.args = NULL,
  eb = TRUE,
  robust.LS = FALSE,
  ref.batch = NULL,
  percent.var = 0.95,
  n.pc = NULL,
  std.var = TRUE,
  ...
)

Arguments

data	n x p data frame or matrix of observations where p is the number of features and n is the number of subjects.
bat	Factor indicating batch (often equivalent to site or scanner)
covar	Data frame or matrix of covariates supplied to model
model	Model function. ComBat Family supports any models that take arguments formula and data, but are limited to models fitting with identity link (e.g. family = gaussian(link = "identity")). This includes lm, gam, gamlss, rq, lmer, and more
formula	Formula for model starting with ⁠y ~⁠ where y represents each feature
score.model	Model for scores, defaults to NULL for fitting basic location and scale model without covariates on the scores
score.args	List of arguments for score model
eb	If TRUE, uses ComBat model with empirical Bayes for mean and variance harmonization.
robust.LS	If TRUE, uses robust location and scale estimators for error variance and site effect parameters. Uses median and biweight midvariance
ref.batch	Reference batch, must take value in levels(bat)
percent.var	Numeric. The number of harmonized principal component scores is selected to explain this proportion of the variance
n.pc	Optional numeric. If specified, this number of principal component scores is harmonized. Overrides percent.var
std.var	If TRUE, scales variances to be equal to 1 before PCA.
...	Additional arguments to model

Value

covfam returns a list containing the following components:

dat.covbat	Harmonized data as a matrix with same dimensions as data
batch.info	Batch information, including reference batch if specified
combat.out	List output of comfam from the ComBat step
pc.output	Output of prcomp from PCA step
n.pc	Numeric, number of PCs harmonized
scores.com	List output of comfam from the CovBat step

Examples

covfam(iris[,1:2], iris$Species)
covfam(iris[,1:2], iris$Species, iris[3:4], lm, y ~ Petal.Length + Petal.Width)

---

Generate Customized Predicted Quantiles List

Description

This function computes customized predicted quantiles for a specified feature across both female and male groups using a GAMLSS model. The resulting list object is structured for visualization purposes.

Usage

cus_result_gen(age_list, customized_q = 0.75, f)

Arguments

age_list	A list containing all ROIs' true volumes, age trend estimates, and the fitted GAMLSS model.
customized_q	A numeric value between 0 and 1 representing the quantile to predict (e.g., 0.75 for the 75th percentile).
f	A string specifying the feature of interest within the age_list.

Details

This function utilizes the GAMLSS model to compute predictions for the specified quantile across demographic groups. The results include predictions for both the specified quantile and the median, enabling enhanced visualization.

Value

A list containing the customized quantile value used for predictions and a data frame with columns for age, quantile type, prediction, and sex.

Examples

sub_df <- age_df[,c("Volume_1", "age", "sex", "ICV_baseline")] |> na.omit()
colnames(sub_df) <- c("Volume_1", "age", "sex", "icv")
age_list <- list("Volume_1" = age_list_gen(sub_df = sub_df))
cus_result_gen(age_list, customized_q = 0.75, f = "Volume_1")

---

Generate Predicted Quantiles for Age Trends

Description

This function computes predicted quantiles for a specified feature and demographic group based on a GAMLSS model. The function interpolates predictions over a range of ages while accounting for fixed covariates.

Usage

customize_percentile(age_list, feature, q = 0.75, s = "F")

Arguments

age_list	A list containing all ROIs' true volumes, age trend estimates, and the fitted GAMLSS model.
feature	A string specifying the feature of interest within the age_list.
q	A numeric value between 0 and 1 representing the quantile to predict (e.g., 0.5 for the median).
s	A string indicating the gender of the group for which the predictions are generated (e.g., "F" for female, "M" for male).

Details

This function uses a GAMLSS model to generate predictions for a specified quantile and demographic group. The predictions are computed over a sequence of ages (age_test) that spans the observed age range in the data. The function adjusts for fixed covariates such as icv by using their mean values from the input data.

Value

A data frame containing columns for age, quantile type, prediction, and sex.

Examples

sub_df <- age_df[,c("Volume_1", "age", "sex", "ICV_baseline")] |> na.omit()
colnames(sub_df) <- c("Volume_1", "age", "sex", "icv")
age_list <- list("Volume_1" = age_list_gen(sub_df = sub_df))
customize_percentile(age_list, feature = "Volume_1", q = 0.5, s = "F")

---

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
)

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.

Value

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

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)

---

Export Batch Effect Diagnosis Results

Description

Save all the batch effect diagnosis results in a single Excel file or a Quarto report.

Usage

diag_save(path, result, use_quarto = TRUE)

Arguments

path	The path to save the result.
result	A list derived from visual_prep() that contains datasets and statistical test results.
use_quarto	A boolean variable indicating whether to generate a Quarto report.

Value

This function does not return a value. It saves the data to the specified file.

Examples

if(interactive()){
  result <- visual_prep(type = "lm", features = "thickness.left.cuneus",
  batch = "manufac", covariates = "AGE", df = adni[1:100, ], mdmr = FALSE, cores = 1)
  temp_dir <- tempfile()
  dir.create(temp_dir)
  diag_save(temp_dir, result, quarto = FALSE)
  message("Diagnostics saved to: ", temp_dir)
  unlink(temp_dir, recursive = TRUE)  # Clean up the temporary directory
}

---

EB Assumption Check

Description

Generate the empirical and prior distribution of both the location parameter gamma and the scale parameter delta.

Usage

eb_check(
  data,
  bat,
  covar = NULL,
  model = lm,
  formula = NULL,
  robust.LS = FALSE,
  ref.batch = NULL,
  ...
)

Arguments

data	n x p data frame or matrix of observations where p is the number of features and n is the number of subjects.
bat	Factor indicating batch (often equivalent to site or scanner).
covar	Data frame or matrix of covariates supplied to model.
model	The model function that ComBat Family supports: lm, lmer, gam.
formula	Formula for model starting with ⁠y ~⁠ where y represents each feature.
robust.LS	If TRUE, uses robust location and scale estimators for error variance and site effect parameters. Currently uses median and biweight midvariance.
ref.batch	Reference batch, must take value in levels(bat).
...	Additional arguments to model.

Value

eb_check returns a dataframe containing the empirical and prior distribution of both the location parameter (gamma) and the scale parameter (delta).

Examples

eb_check(data = adni[1:500,43:53], bat = as.factor(adni$manufac[1:500]),
covar = adni[1:500, c("AGE", "SEX")], model = lm, formula = y ~ AGE + SEX)

---

ComBatFamily Model Formula Generations

Description

Generate appropriate formula for ComBatFamily models

Usage

form_gen(x, c = NULL, i = NULL, random = NULL, smooth = NULL)

Arguments

x	A model function name that is used or to be used in the ComBatFamily Package (eg: "lmer", "lm", "gam").
c	Data frame or matrix of covariates supplied to model.
i	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.

Value

A string of formula

Examples

covariates <- adni[, c("AGE", "SEX")]
form_gen(x = "lm", c = covariates)

---

Compute Quantile Functions for a Predictor in a GAMLSS Model

Description

This function computes quantile functions for a specified predictor in a GAMLSS model, allowing the evaluation of response quantiles (e.g., 25th, 50th, 75th percentiles) as a function of the predictor. It is a modified version of the getQuantile function from the GAMLSS package, with improvements to explicitly require the dataset as an argument, avoiding reliance on global or external variables.

Usage

getQuantileRefactored(
  obj,
  term,
  quantile,
  data,
  n.points = 100,
  fixed.at = list()
)

Arguments

obj	A fitted GAMLSS model object.
term	A character string specifying the name of the predictor variable for which quantiles are computed.
quantile	A numeric vector of probabilities (e.g., c(0.25, 0.5, 0.75)) at which to compute the quantiles.
data	A data frame containing the dataset used in the model. This must include the predictor specified in term.
n.points	An integer specifying the number of points at which to evaluate the quantile functions (default: 100).
fixed.at	A named list specifying fixed values for other predictors in the dataset. If not provided, categorical predictors are set to their most frequent levels, and numeric predictors to their median values.

Details

This function generates a temporary dataset by varying the specified predictor (term) over a sequence of values while holding all other predictors constant (using values specified in fixed.at, or derived defaults). It then computes the distribution parameters for the GAMLSS model and calculates the quantiles using the appropriate quantile function for the distribution family.

Value

A list of spline functions, one for each quantile specified in quantile. Each spline function can be evaluated at specific predictor values to obtain the corresponding quantile.

Examples

if (requireNamespace("gamlss", quietly = TRUE)) {
  library(gamlss)
  sub_df <- data.frame(
    age = seq(1, 20, length.out = 100),
    height = 50 + 2.5 * seq(1, 20, length.out = 100) + rnorm(100, 0, 5)
  )

  mdl <- gamlss(height ~ pb(age), data = sub_df, family = NO())

  quantile_function <- getQuantileRefactored(
    obj = mdl,
    term = "age",
    quantile = c(0.25, 0.5, 0.75),
    data = sub_df
  )
 }else{
 message("The 'gamlss' package is not installed. Please install it to run this example.")
 }

---

Interaction Term Generation

Description

Generate appropriate interaction terms for regression models.

Usage

interaction_gen(
  type = "lm",
  covariates = NULL,
  smooth = NULL,
  interaction = NULL,
  smooth_int_type = NULL
)

Arguments

type	The type of model to be used for batch effect evaluation or harmonization (eg: "lmer", "lm", "gam").
covariates	Name of covariates supplied to model.
smooth	Variable names 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.

Value

interaction_gen returns a list containing the following components:

interaction	A vector of interaction terms to be included
covariates	Modified covariates after expressing interaction terms
smooth	Modified smooth terms after expressing interaction terms

Examples

interaction_gen(type = "lm", covariates = c("AGE", "SEX", "DIAGNOSIS"),
interaction = "AGE,DIAGNOSIS")

interaction_gen(type = "gam", covariates = c("AGE", "SEX", "DIAGNOSIS"),
smooth = "AGE", smooth_int_type = "linear", interaction = "AGE,DIAGNOSIS")

---

Model Generations

Description

Generate appropriate regression models based on the model type and formula

Usage

model_gen(
  y,
  type = "lm",
  batch = NULL,
  covariates = NULL,
  interaction = NULL,
  random = NULL,
  smooth = NULL,
  df
)

Arguments

y	Dependent variable in the model.
type	A model function name that is used or to be used in the ComBatFamily Package (eg: "lmer", "lm", "gam").
batch	Name of batch variable (often equivalent to site or scanner).
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.
df	Dataset to be harmonized.

Value

A regression model object to be used for batch effect diagnostics and the post-harmonization stage.

Examples

model_gen(y = "thickness.left.caudal.anterior.cingulate", type = "lm",
batch = "manufac", covariates = c("AGE", "SEX"), df = adni)

---

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,])

---

Post Harmonization Residual Generation

Description

Extract residuals after harmonization.

Usage

residual_gen(
  type = "lm",
  features = NULL,
  covariates = NULL,
  interaction = NULL,
  random = NULL,
  smooth = NULL,
  smooth_int_type = NULL,
  df,
  rm = NULL,
  model = FALSE,
  model_path = NULL,
  cores = detectCores()
)

Arguments

type	A model function name that is to be used (eg: "lmer", "lm", "gam").
features	The names of the features from which to extract residuals.
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	Harmonized dataset to extract residuals from.
rm	variables to remove effects from.
model	A boolean variable indicating whether an existing model is to be used.
model_path	path to the existing model.
cores	number of cores used for parallel computing.

Value

residual_gen returns a list containing the following components:

model	a list of regression models for all rois
residual	Residual dataframe

Examples

features <- colnames(adni)[43:53]
residual_gen(type = "lm", features = features,
covariates = c("AGE", "SEX", "DIAGNOSIS"), df = adni, rm = c("AGE", "SEX"), cores = 1)

---

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)

---