---
title: "Post-Harmonization Downstream Analysis"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Post-Harmonization}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Introduction

After harmonization, multiple-sites batch effect get controlled (or ideally eliminated). Some studies require further investigation of life span age trend of brain structures or other significant variable effects on brain structures. `ComBatFamQC` provides a post harmonization tool to:

-   generate age trend estimation adjusting sex and icv: `age_list_gen`
-   interactively visualize age trend: `age_shiny`
-   generate residuals eliminating specific covariates' effects: `residual_gen`
    -   generate residuals from scratch
    -   generate residuals based on existing regression model

# Set up

Import `ComBatFamQC` package and read in harmonized data set for age trend visualization. We use `age_df` data for age trend visualization in the vignette. To be noticed the read-in data set should be a data frame (not tibble). 
 
```{r setup, eval = FALSE}
library(ComBatFamQC)
data(age_df)
```

# Life Span Age Trend Visualization

In this step, we need to generate a list of data sets for all ROIs. Each ROI's data set contains four columns:

-   **roi.name**: ROI value
-   **age**: subject's age info
-   **sex**: subject's sex info
-   **icv**: subject's intracranial volume

```{r, eval = FALSE}
age_df <- data.frame(age_df)
features <- colnames(age_df)[c(6:56)]
age <- "age"
sex <- "sex"
icv <- "ICV_baseline"
age_df[[sex]] <- as.factor(age_df[[sex]])
```

## Create a list of data sets for all ROIs

```{r, eval = FALSE}
# Create sub_df for different features
sub_df_list <- lapply(seq_len(length(features)), function(i){
    sub_df <- age_df[,c(features[i], age, sex, icv)] %>% na.omit()
    colnames(sub_df) <- c(features[i], "age", "sex", "icv")
    return(sub_df)
  })
```

## Create age trend estimation for all ROIs

```{r, eval = FALSE}
# For MAC users
library(parallel)
age_list <- mclapply(seq_len(length(features)), function(w){
  age_sub <- age_list_gen (sub_df = sub_df_list[[w]],  lq = 0.25, hq = 0.75)
  return(age_sub)
}, mc.cores = detectCores()) 

# For Windows users
age_list <- mclapply(1:length(features), function(w){
  age_sub <- age_list_gen (sub_df = sub_df_list[[w]],  lq = 0.25, hq = 0.75)
  return(age_sub)
}, mc.cores = 1) 

names(age_list) <- features

quantile_type <- c(paste0("quantile_", 100*0.25), "median", paste0("quantile_", 100*0.75))
```

## Launch Shiny App for Visualization

Users can choose to generate age trend plots using the `ggplot` package or the `plotly` package (if `plotly` is installed).

```{r, eval=FALSE}
# plotly: interactive plot
ComBatFamQC::age_shiny(age_list, features, quantile_type, use_plotly = TRUE)
# ggplot: static plot
ComBatFamQC::age_shiny(age_list, features, quantile_type, use_plotly = FALSE)
```

## Save Age Trend Table and GAMLSS Model

```{r, eval=FALSE}
# Save age trend table
temp_dir <- tempfile()
dir.create(temp_dir)
age_save(path = temp_dir, age_list = age_list)

# Save GAMLSS Model
gamlss_model <- lapply(seq_len(length(age_list)), function(i){
        g_model <- age_list[[i]]$model
        return(g_model)})
names(gamlss_model) <- names(age_list)
saveRDS(gamlss_model, file = file.path(temp_dir, "gamlss_model.rds"))
```

# Residual Generation

In this step, we would like to generate different sets of residuals removing specific covariates' effects.

## Get harmonized data set
```{r, eval=FALSE}
features <- colnames(adni)[c(43:104)]
covariates <- c("timedays", "AGE", "SEX", "DIAGNOSIS")
interaction <- c("timedays,DIAGNOSIS")
batch <- "manufac"
combat_model <- combat_harm(type = "lm", features = features, batch = batch, covariates = covariates, interaction = interaction, smooth = NULL, random = NULL, df = adni)
harmonized_df <- combat_model$harmonized_df
```

## Generate residual data set

Specify parameters carefully based on regression type and which covariates' effects to remove.

### Generate residuals from scratch
```{r, eval=FALSE}
# generate residuals by removing timedays and DIAGNOSIS effects, while preserving AGE and SEX effects.
result_residual <- residual_gen(type = "lm", features = features, covariates = covariates, interaction = interaction, smooth = NULL, df = harmonized_df, rm = c("timedays", "DIAGNOSIS"))

# save residual data set
write.csv(result_residual$residual, file.path(temp_dir, "residual.csv"))

# save regression model
saveRDS(result_residual$model, file.path(temp_dir, "regression_model.rds"))
```

### Generate residuals from existing model
```{r, eval=FALSE}
result_residual <- residual_gen(df = harmonized_df, rm = c("timedays", "DIAGNOSIS"), model = TRUE, model_path = file.path(temp_dir, "regression_model.rds"))

# save residual data set
write.csv(result_residual$residual, file.path(temp_dir, "residual.csv"))
# Clean up the temporary file
unlink(temp_dir, recursive = TRUE)
```
