Project: s41467-025-58322-3 | Function: Myseucompentropy (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:** Custom entropy calculation function for single-cell RNA-seq data analysis. Computes Shannon entropy normalized by the number of features to measure transcriptional diversity in beta cells during gestation.

**KEY TERMS:** {
  "object": "Seurat object with scRNA-seq data",
  "feature": "gene features for entropy calculation",
  "probs": "normalized expression probabilities",
  "entropy": "Shannon entropy score"
}

**CONTEXT CODE:**                      color = "grey30",
                     #outlier.shape = NA,
                     show.legend = F,
                     lwd = box.lwd
        )+
        scale_fill_manual(values = time.colors)+
        
        
        guides(colour = guide_legend(title = "",
                                     keywidth = 3,
                                     keyheight = 3,
                                     override.aes = list(size=12),
                                     order = 1,
                                     ncol = 1))+
        theme(axis.ticks = element_blank(),
              axis.text = element_blank(),
              legend.text = element_text(size = 10, colour = "black"))
    }
    return(p)
  }
MylimmafcDataplot <- function(bed,
                              up.name,down.name,
                              phase1,
                              phase2,
                              fc.state,
                              cutoff.K,
                              xlim,
                              ylim
){
  mcols(bed)[,fc.state] <- "-"
  mcols(bed)[,fc.state][names(bed) %in% up.name &
                          mcols(bed)[,phase2] > cutoff.K] <- "up"
  mcols(bed)[,fc.state][names(bed) %in% down.name &
                          mcols(bed)[,phase1] > cutoff.K] <- "down"
  mcols(bed)[,fc.state] <- factor(mcols(bed)[,fc.state],
                                  levels = c("-",
                                             "down",
                                             "up"))
  plot(log2(mcols(bed)[,phase1]),
       log2(mcols(bed)[,phase2]),
       xlim = xlim,
       ylim = ylim,
       col = color.fc[mcols(bed)[,fc.state]],
       pch = 16,
       cex = 0.5)
  box(lwd = 8)
  MyText(paste(fc.state,"\n","up:",sum(mcols(bed)[,fc.state] == "up"),"\n",
               "down:",sum(mcols(bed)[,fc.state] == "down"),"\n",
               "-:",sum(mcols(bed)[,fc.state] == "-"),"\n"))
}
Myseucompentropy <- function(object, feature = rownames(object@assays$RNA@data)) {
  stop("Function implementation has been masked")
}


Mycoord <- function(coord, dims = c('PC_1', 'PC_2'), class = NULL, classorder = NULL) {
  stop("Function implementation has been masked")
}


MyExtend <- 
function(x, upstream = 0, downstream = 0, rm.neg = T) {
  if (any(GenomicRanges::strand(x = x) == "*")) {
    warning("'*' ranges were treated as '+'")
  }
  on_plus <- GenomicRanges::strand(x = x) == "+" | GenomicRanges::strand(x = x) == "*"
  new_start <- GenomicRanges::start(x = x) - ifelse(test = on_plus, yes = upstream, no = downstream)
  new_end <- GenomicRanges::end(x = x) + ifelse(test = on_plus, yes = downstream, no = upstream)
  if(rm.neg){
    new_start[new_start<1]=1
  }
  IRanges::ranges(x = x) <- IRanges::IRanges(start = new_start, end = new_end)
  x <- GenomicRanges::trim(x = x)
  return(x)
}

myplotDeviationsTsne <- function(object, tsne, var_df = NULL, sample_column = NULL, 
                                 annotation_name = NULL, shiny = interactive()) 
{
  if (is.list(tsne) && "Y" %in% names(tsne)) {
    tsne <- tsne$Y
  }
  if (nrow(tsne) != ncol(object)) {
    stop("Number of rows of tsne do not match number of columns of object. ", 
         " plotDeviationsTsne takes result of deviationsTsne for samples")
  }
  if (shiny) 
    return(plot_deviations_tsne_shiny(object, tsne, var_df, 
                                      sample_column))
  stopifnot(sample_column %in% colnames(colData(object)))
  anno <- colData(object)[, sample_column]
  out <- list()
  if (!is.null(sample_column)) {
    for (i in sample_column) {
      anno <- colData(object)[, i]
      out[[i]] <- ggplot(data.frame(x = tsne[, 1], y = tsne[, 
                                                            2], color = anno, text = colnames(object)), 
                         aes_string(x = "x", y = "y", col = "color", 
                                    text = "text")) + geom_point(size = 2) + chromVAR_theme() + 
        xlab("tSNE dim 1") + ylab("tSNE dim 2") + theme(legend.key.size = grid::unit(0.5, 
                                                                                     "lines"))
      if (nlevels(as.factor(anno)) <= 8) {
        out[[i]] <- out[[i]] + scale_color_brewer(palette = "Dark2", 
                                                  name = i)
      }
      else {
        out[[i]] <- out[[i]] + guides(colour = guide_legend(title = i))
      }
    }
  }
  if (!is.null(annotation_name)) {
    for (i in annotation_name) {
      if (i %in% rownames(object)) {
        ix <- match(c(i), rownames(object))
      }
      else if (i %in% rowData(object)$name) {
        ix <- which(rowData(object)$name == i)
        if (length(ix) > 1) 
          ix <- ix[which.max(row_sds(assays(object[ix, 
                                                   ])$z))]
      }
      else if (is.numeric(i)) {
        ix <- i
      }
      else {
        stop("annotation_name invalid")
      }
      if ("name" %in% colnames(rowData(object))) {
        name_val <- rowData(object)[ix, "name"]
      }
      else {
        name_val <- i
      }
      out[[i]] <- ggplot(data.frame(x = tsne[, 1], y = tsne[, 
                                                            2], color = deviationScores(object)[ix, ], text = colnames(object)), 
                         aes_string(x = "x", y = "y", col = "color", 
                                    text = "text")) + geom_point(size = 2) + scale_color_gradient2(name = i, 
                                                                                                   mid = "lightgray", low = "blue", high = "red") + 
        xlab("tSNE dim 1") + ylab("tSNE dim 2") + 
        theme(legend.key.size = grid::unit(0.5, "lines"))
    }
  }
  return(out)
}
MyCalUcsccomquanNormFactor <- 
  function(input.bed,
           list.prefix,
           quantile.cutoff = 0.5){
    merge.bed <- input.bed
    for (sn.tmp in list.prefix) {


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