Project: s41467-023-43003-w | Function: my.t.test.cluster (r)

system: You are an expert R programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow R best practices and maintain consistent code style.
Return **only** the complete R function code.

user: Complete this R function:

**DESCRIPTION:** Performs t-test with cluster adjustment for single-cell RNA-seq data, accounting for within-cluster correlation. Takes expression values (y), cluster identities, and group assignments to compute cluster-adjusted statistics including intracluster correlation, variance correction factor, and corrected p-values.

**KEY TERMS:** {
  "y": "expression values",
  "cluster": "cell cluster identities",
  "group": "treatment groups (CHIP vs No-CHIP)",
  "msc": "mean square among clusters",
  "mse": "mean square within clusters",
  "rho": "intracluster correlation"
}

**CONTEXT CODE:** 
PlotClusterPrecentageBarplot <- function(x, faceWrapeBy, fileName=NULL, width=15, height=8){
  df<-data.frame(x1=x,x2=faceWrapeBy)
  
  df <- df %>% count(x1,x2,.drop = FALSE) %>% group_by(x1) %>%
    mutate(freq = n /sum(n)) %>% complete(x2,fill = list(n=0,freq=0))
  if (!is.null(fileName)) {
    svg(filename = fileName,width = width, height = height)
    
  }
  ggplot(df, aes(x=x1, y= freq, fill= x1))+
    geom_col(width = 0.9, color = "black")+
    facet_wrap(~x2, nrow = 2, scales = "free")+
    scale_y_continuous(name = "Percent per timepoint", labels = scales::percent_format())+
    theme(panel.background = element_blank(), axis.title.x = element_blank(),
          axis.text.x = element_text(angle = 45, hjust= 1, size = 8))
  
  
  
}

## prepare data for cluster t.test from the deg list and do a cluster t-test
do_cluster_t_test <- function(seurat_subset, degs, group="condition", cluster="seurat_clusters"){
  gene_names<- names(table(rownames(degs)))
  #print(head(gene_names))
  p_values <- vector("list",length(gene_names))
  names(p_values) <- gene_names
  #gene_names <- row.names(cluster_subset)
  #if (celltype=="Adipocytes"){
  #  seurat_subset <- seurat_subset[,!seurat_subset$orig.ident=="D7"]
  #}
  group <- seurat_subset[[group]][,1]
  cluster <- seurat_subset[[cluster]][,1]
  for (gene in gene_names){
    y <- c(t(as.matrix(seurat_subset@assays$RNA[gene,])))
    test_info <- my.t.test.cluster(y, cluster, group)
    p_values[[gene]] <- test_info[nrow(test_info)]
  }
  return(p_values)
}

#' Welch t-test adjusted for cluster-correlated data
#' t-test with Satterthwaite approximation of standard errors
#' Original author: Jean Monlong, Brent Pedersen
#' Modified by: David John
#' @param y Numerical: the dependent variable, typically expression values for a single gene
#' @param cluster Factor: the cluster assignment of the observations
#' @param group Factor: the group of the observations, should have exactly 2 groups
#' @param conf.int the confidence interval to be calculated
#' @return matrix with cluster-adjusted t-test results
my.t.test.cluster <- function (y, cluster, group, conf.int = 0.95) {
  stop("Function implementation has been masked")
}

DO.Mean.SEM.Graphs <- function(SeuratObject, Features, ListTest=NULL, returnValues=FALSE, ctrl.condition=NULL, group.by = "condition", returnPlot=FALSE){ 
  require(ggplot2)
  require(ggpubr)
  #require(tidyverse)
  require(reshape2)
  #SEM function definition
  SEM <- function(x) sqrt(var(x)/length(x))
  #create data frame with conditions from provided SeuratObject, aswell as original identifier of samples
  df<-data.frame(condition=setNames(SeuratObject[[group.by]][,group.by], rownames(SeuratObject[[group.by]]))
                 ,orig.ident = SeuratObject$orig.ident)
  #get expression values for genes from individual cells, add to df
  for(i in Features){
    df[,i] <- expm1(SeuratObject@assays$RNA@data[i,])
  }
  
  #melt results 
  df.melt <- melt(df)
  #group results and summarize, also add/use SEM 
  df.melt.sum <- df.melt %>% 
    dplyr::group_by(condition, variable) %>% 
    dplyr::summarize(Mean = mean(value))
  # #second dataframe containing mean values for individual samples
  df.melt.orig <- df.melt %>%
    dplyr::group_by(condition, variable, orig.ident) %>%
    dplyr::summarize(Mean = mean(value))
  
  #add SEM calculated over sample means
  df.melt.sum$SEM <- NA
  for (condition in df.melt.sum$condition) {
    df.melt.orig.sub <- df.melt.orig[df.melt.orig$condition %in% condition,]
    df.melt.sum[df.melt.sum$condition %in% condition,]$SEM <- SEM(df.melt.orig.sub$Mean)
  }
  
  stat.df <- data.frame(Mean = SeuratObject[["RNA"]]@data[Features,],
             condition = SeuratObject[[group.by]][,group.by])
  
  #create comparison list for t.test, always against control, so please check your sample ordering
  # ,alternative add your own list as argument
  if (is.null(ListTest)) {
    #if ListTest is empty, so grep the ctrl conditions out of the list 
    # and define ListTest comparing every other condition with that ctrl condition
    cat("ListTest empty, comparing every sample with provided control")
    conditions <- unique(SeuratObject[[group.by]][,group.by])
    #set automatically ctrl condition if not provided
    if (is.null(ctrl.condition)) { 
      ctrl.condition <- conditions[grep(pattern = paste(c("CTRL","Ctrl","WT","Wt","wt"),collapse ="|")
                                        ,conditions)[1]]
    }
    
    df.melt.sum$condition <- factor(df.melt.sum$condition
                                    ,levels = c(as.character(ctrl.condition),levels(factor(df.melt.sum$condition))[!(levels(factor(df.melt.sum$condition)) %in% ctrl.condition)]))
    #create ListTest
    ListTest <- list()
    for (i in 1:length(conditions)) {
      cndtn <- conditions[i] 
      if(cndtn!=ctrl.condition)
      {
        ListTest[[i]] <- as.character(c(ctrl.condition,cndtn))
      }
    }
  }
  #delete Null values, created by count index
  ListTest <- ListTest[!sapply(ListTest, is.null)]
  #create barplot with significance
  p<-ggplot(df.melt.sum, aes(x = condition, y = Mean, fill = condition))+
    geom_col(color = "black")+
    geom_errorbar(aes(ymin = Mean-SEM, ymax = Mean+SEM), width = .2)+
    geom_point(data = df.melt.orig, aes(x=condition,y=Mean), size = 1, shape=1, position = "jitter")+
    #ordering, control always first
    scale_x_discrete(limits=c(as.character(ctrl.condition),levels(factor(df.melt.sum$condition))[!(levels(factor(df.melt.sum$condition)) %in% ctrl.condition)]))+
    #t-test, always against control, using means from orig sample identifier
    stat_compare_means(data=stat.df, comparisons = ListTest, method = "wilcox", size=3)+
    facet_wrap(~variable, ncol = 9, scales = "free") +
    scale_fill_manual(values = rep(c("#FFFFFF","#BDD7E7" ,"#6BAED6", "#3182BD", "#08519C"),5) #20 colours set for more change number
                      , name = "Condition")+
    labs( title = "", y = "Mean UMI") +
    theme_classic() +
    theme(axis.text.x = element_text(color = "black",angle = 45,hjust = 1, size = 14),
          axis.text.y = element_text(color = "black", size = 14),
          axis.title.x = element_blank(),
          axis.title = element_text(size = 14, color = "black"),
          plot.title = element_text(size = 14, hjust = 0.5),
          axis.line = element_line(color = "black"),
          strip.text.x = element_text(size = 14, color = "black"),
          legend.position = "bottom")
  print(p)
  if (returnValues==TRUE) {
    return(df.melt.sum)
  }
  if (returnPlot==TRUE) {
    return(p)
  }
}

#perform SEM Graphs, similar to DO.mean.SEM.graphs but takes the mean not over cells, it takes the mean over the mean per sample
#' @author Mariano Ruz Jurado


Complete the function implementation replacing "stop("Not implemented")".