modify package description

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: ComBatFamQC
-Title: Harmonization QC visualization
-Version: 0.0.1
+Title: Comprehensive Batch Effect Diagnostics and Harmonization
+Version: 1.0.0
 Authors@R: 
     person("Zheng", "Ren", , "zren1422@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0009-0006-2430-4625"))
-Description: Streamline interactive batch effect diagnostics, harmonization, and post-harmonization downstream analysis to greatly simplify the data harmonization process and improve transparency and reproducibility. 
+Description: Provides a comprehensive framework for batch effect diagnostics, harmonization, and post-harmonization downstream analysis. Features include interactive visualization tools, robust statistical tests, and a range of harmonization techniques. Additionally, ComBatFamQC enables the creation of life-span age trend plots with estimated age-adjusted centiles and facilitates the generation of covariate-corrected residuals for analytical purposes.
 License: MIT + file LICENSE
-URL: https://github.com/Zheng206/ComBatFamQC
+URL: https://github.com/Zheng206/ComBatFamQC, https://zheng206.github.io/ComBatQC-Web/
 BugReports: https://github.com/Zheng206/ComBatFamQC/issues
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)

R/Harmonization.R

@@ -8,14 +8,14 @@
 #' @param batch The name of the batch variable. Can be skipped if `result` is provided.
 #' @param covariates The names of covariates supplied to `model`. This can be be skipped if `result` is provided.
 #' @param df Dataset to be harmonized. This can be be skipped if `result` is provided.
-#' @param type The name of a regression model to be used in the ComBatFamily package: "lmer", "lm", "gam".
+#' @param type The name of a regression model to be used in the ComBatFamily package: `"lmer"`, `"lm"`, `"gam"`.
 #' @param random The variable name of a random effect in linear mixed effect model.
 #' @param smooth The name of the covariates that require a smooth function.
-#' @param interaction Expression of interaction terms supplied to `model` (eg: "age,diagnosis").
-#' @param 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.
-#' @param family The type of combat family to use, comfam or covfam.
+#' @param interaction Expression of interaction terms supplied to `model` (eg: `"age,diagnosis"`).
+#' @param 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.
+#' @param family The type of combat family to use, `comfam` or `covfam`.
 #' @param eb If \code{TRUE}, uses ComBat model with empirical Bayes for mean and variance harmonization
 #' @param ref.batch The name of the reference batch.
 #' @param 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).

R/age_shiny.R

@@ -4,7 +4,7 @@
 #'
 #' @param age_list A list containing all ROIs' true volumes, age trend estimates, and the fitted GAMLSS model.
 #' @param features A vector of roi names.
-#' @param quantile_type A vector of quantile types.
+#' @param quantile_type A vector of quantile types (e.g., `c("quantile_25", "median", "quantile_75")`)
 #' @param use_plotly A boolean variable that indicates whether to display the age plot using the `plotly` package.
 #'
 #' @importFrom gamlss gamlss gamlss.control predictAll getQuantile ps pb
@@ -344,7 +344,7 @@ age_list_gen <- function(sub_df, lq = 0.25, hq = 0.75, mu = "smooth", sigma = "s
 #' }
 
 
-customize_percentile <- function(age_list, feature, q, s){
+customize_percentile <- function(age_list, feature, q = 0.75, s = "F"){
   mdl_sex <- age_list[[feature]]$model
   sub_df <- age_list[[feature]]$true_df
   min_age <- min(sub_df[["age"]])
@@ -381,7 +381,7 @@ customize_percentile <- function(age_list, feature, q, s){
 #' cus_result_gen(age_list, customized_q = 0.75, f = "Volume_1")
 #' }
 
-cus_result_gen <- function(age_list, customized_q, f){
+cus_result_gen <- function(age_list, customized_q = 0.75, f){
   result <- age_list[[f]]
   cus_result <- lapply(c("F", "M"), function(x) customize_percentile(age_list, f, customized_q, x)) |> bind_rows()
   cus_result <- rbind(cus_result, result$predicted_df_sex %>% filter(.data[["type"]] == "median"))
@@ -427,7 +427,7 @@ cus_result_gen <- function(age_list, customized_q, f){
 #' )
 #' }
 
-age_trend_plot <- function(age_list, f, s, q, cus_list = NULL, use_plotly = TRUE){
+age_trend_plot <- function(age_list, f, s = "none", q = "median", cus_list = NULL, use_plotly = TRUE){
   result <- age_list[[f]]
   pal <- c("coral", "olivedrab")
   pal <- setNames(pal, c("F", "M"))
@@ -442,7 +442,6 @@ age_trend_plot <- function(age_list, f, s, q, cus_list = NULL, use_plotly = TRUE
         marker = list(
           color = "rgba(70, 130, 150, 0.5)",
           size = 6
-          #line = list(color = "lightblue", width = 0.5)
         ),
         hoverinfo = "text",
         text = ~paste("Age: ", round(.data[["age"]], 2), "<br>Volume: ", round(.data[[f]],2)),
@@ -455,7 +454,6 @@ age_trend_plot <- function(age_list, f, s, q, cus_list = NULL, use_plotly = TRUE
         font = list(family = "Arial", size = 12)
       )
     if(q != "customization"){
-
       if(s == "none"){
         base_plot
       }else if(s == "F"){
@@ -481,7 +479,7 @@ age_trend_plot <- function(age_list, f, s, q, cus_list = NULL, use_plotly = TRUE
             mode = "lines",
             line = list(color = "olivedrab"),
             linetype = ~.data[["type"]],
-            hoverinfo = "text",  # Show custom hover text
+            hoverinfo = "text",
             text = ~paste("Age: ", round(.data[["age"]], 2), "<br>Volume: ", round(.data[["prediction"]], 2))
           )
       }else if(s == "F vs M"){
@@ -552,7 +550,6 @@ age_trend_plot <- function(age_list, f, s, q, cus_list = NULL, use_plotly = TRUE
         axis.text.x = element_text(size = 12, face = "bold"),
         axis.text.y = element_text(size = 12, face = "bold"),
       )
-
     if(q != "customization"){
       if(s == "none"){
         base_plot
@@ -564,7 +561,7 @@ age_trend_plot <- function(age_list, f, s, q, cus_list = NULL, use_plotly = TRUE
         base_plot + geom_line(data = result$predicted_df_sex %>% filter(.data[["type"]] == q), mapping = aes(x = .data[["age"]], y = .data[["prediction"]], color = .data[["sex"]])) +
           scale_color_manual(
             values = c("F" = "coral", "M" = "olivedrab"),
-            name = "Sex"  # Optional: specify the legend title
+            name = "Sex"
           )
       }
     }else{
@@ -620,7 +617,7 @@ age_trend_plot <- function(age_list, f, s, q, cus_list = NULL, use_plotly = TRUE
 #' @examples
 #' \dontrun{
 #' # Generate table for females at the 50th percentile
-#' age_table_gen(result, q = "quantile_50", s = "F")
+#' age_table_gen(result, q = "median", s = "F")
 #'
 #' # Generate comparison table for females vs. males at the 75th percentile
 #' age_table_gen(result, q = "quantile_75", s = "F vs M")
@@ -629,7 +626,7 @@ age_trend_plot <- function(age_list, f, s, q, cus_list = NULL, use_plotly = TRUE
 #' @export
 
 
-age_table_gen <- function(result, q, s){
+age_table_gen <- function(result, q = "median", s = "F"){
   if(s == "F"){
     if("predicted_df_sex" %in% names(result)){
       age_table <- result$predicted_df_sex %>% filter(.data[["type"]] == q, .data[["sex"]] == "F") %>% dplyr::select(all_of(c("age", "prediction"))) %>% rename("Percentile.Volume" = "prediction", "Age" = "age")
@@ -692,8 +689,6 @@ age_table_gen <- function(result, q, s){
     )
   }
 
-  age_table_dt <- age_table %>% DT::datatable(options = list(dom = 'Bfrtip',
-                                                             buttons = c('csv', 'excel'), columnDefs = list(list(className = 'dt-center',
-                                                                                                                 targets = "_all"))), extensions = 'Buttons')
+  age_table_dt <- age_table %>% DT::datatable(options = list(dom = 'Bfrtip', buttons = c('csv', 'excel'), columnDefs = list(list(className = 'dt-center', targets = "_all"))), extensions = 'Buttons')
   return(age_table_dt)
 }

R/comfam_shiny.R

@@ -1,9 +1,9 @@
 #' Batch Effect Interactive Visualization
 #'
-#' Provide interactive batch/site effect visualization through Shiny app.
+#' Provides an interactive visualization of batch or site effects using a Shiny application.
 #'
 #' @param result A list derived from `visual_prep()` that contains datasets and statistical test results for Shiny visualization.
-#' @param after A boolean variable indicating whether the batch effect diagnostic occurs before or after harmonization. The default value is FALSE.
+#' @param after A boolean variable indicating whether the batch effect diagnostic occurs before or after harmonization (default: `FALSE`).
 #'
 #' @import ggplot2
 #' @import shiny

R/residual_gen.R

@@ -2,15 +2,15 @@
 #'
 #' Extract residuals after harmonization.
 #'
-#' @param type A model function name that is to be used (eg: "lmer", "lm", "gam").
+#' @param type A model function name that is to be used (eg: `"lmer"`, `"lm"`, `"gam"`).
 #' @param features The names of the features from which to extract residuals.
 #' @param covariates Name of covariates supplied to `model`.
-#' @param interaction Expression of interaction terms supplied to `model` (eg: "age,diagnosis").
+#' @param interaction Expression of interaction terms supplied to `model` (eg: `"age,diagnosis"`).
 #' @param random Variable name of a random effect in linear mixed effect model.
 #' @param smooth Variable name that requires a smooth function.
-#' @param 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.
+#' @param 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.
 #' @param df Harmonized dataset to extract residuals from.
 #' @param rm variables to remove effects from.
 #' @param model A boolean variable indicating whether an existing model is to be used.

R/visual_prep.R

@@ -2,19 +2,19 @@
 #'
 #' Prepare relevant datasets and statistical test results for batch/site effect diagnostic visualization.
 #'
-#' @param type The name of a regression model to be used in batch effect diagnostics stage: "lmer", "lm", "gam".
+#' @param type The name of a regression model to be used in batch effect diagnostics stage: `"lmer"`, `"lm"`, `"gam"`.
 #' @param features The name of the features to be evaluated.
 #' @param batch The name of the batch variable.
 #' @param covariates Name of covariates supplied to `model`.
-#' @param interaction Expression of interaction terms supplied to `model` (eg: "age,diagnosis").
+#' @param interaction Expression of interaction terms supplied to `model` (eg: `"age,diagnosis"`).
 #' @param random Variable name of a random effect in linear mixed effect model.
 #' @param smooth Variable name that requires a smooth function.
-#' @param 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.
+#' @param 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.
 #' @param df Dataset to be evaluated.
 #' @param cores number of cores used for parallel computing.
-#' @param mdmr A boolean variable indicating whether to run the MDMR test. By default, it is set to TRUE.
+#' @param mdmr A boolean variable indicating whether to run the MDMR test (default: `TRUE`).
 #'
 #' @return `visual_prep` returns a list containing the following components:
 #' \item{residual_add_df}{Residuals that might contain additive and multiplicative joint batch effects}

README.md

@@ -39,8 +39,6 @@ The ComBatFamQC package offers the following five key functionalities:
     -   ComBat-GAM (Pomponio et al., 2020)
     -   CovBat (Chen et al., 2021)
 
-![ComBatQC Shiny](/inst/figure/combat_shiny.png)
-
 -   **Interactive Visualization through R Shiny**: The ComBatFamQC package comes with an interactive visualization tool built on R Shiny, providing an intuitive user interface to explore and evaluate batch effects, as well as conduct interactive harmonization if needed. The output is organized into multiple tabs, which includes:
 
     -   **Data Overview**: Complete data overview and exploratory analysis
@@ -59,8 +57,6 @@ The ComBatFamQC package offers the following five key functionalities:
     -  **Age Trend Plots**
     -  **Age Trend Table** 
 
-![Age Shiny](/inst/figure/age_shiny.png)
-
 -   **Residual Generation** \
     Generate residual data set, removing specific covariates' effetcs.
 

codemeta.json

@@ -2,12 +2,13 @@
   "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
   "@type": "SoftwareSourceCode",
   "identifier": "ComBatFamQC",
-  "description": "Streamline interactive batch effect diagnostics, harmonization, and post-harmonization downstream analysis to greatly simplify the data harmonization process and improve transparency and reproducibility. ",
-  "name": "ComBatFamQC: Harmonization QC visualization",
+  "description": "Provides a comprehensive framework for batch effect diagnostics, harmonization, and post-harmonization downstream analysis. Features include interactive visualization tools, robust statistical tests, and a range of harmonization techniques. Additionally, ComBatFamQC enables the creation of life-span age trend plots with estimated age-adjusted centiles and facilitates the generation of covariate-corrected residuals for analytical purposes.",
+  "name": "ComBatFamQC: Comprehensive Batch Effect Diagnostics and Harmonization",
+  "relatedLink": "https://zheng206.github.io/ComBatQC-Web/",
   "codeRepository": "https://github.com/Zheng206/ComBatFamQC",
   "issueTracker": "https://github.com/Zheng206/ComBatFamQC/issues",
   "license": "https://spdx.org/licenses/MIT",
-  "version": "0.0.1",
+  "version": "1.0.0",
   "programmingLanguage": {
     "@type": "ComputerLanguage",
     "name": "R",
@@ -393,6 +394,7 @@
     },
     "SystemRequirements": null
   },
-  "fileSize": "4932.818KB",
-  "readme": "https://github.com/Zheng206/ComBatFamQC/blob/main/README.md"
+  "fileSize": "4934.773KB",
+  "readme": "https://github.com/Zheng206/ComBatFamQC/blob/main/README.md",
+  "contIntegration": ["https://github.com/Zheng206/ComBatFamQC/actions/workflows/R-CMD-check.yaml", "https://app.codecov.io/gh/Zheng206/ComBatFamQC"]
 }