diff --git a/DESCRIPTION b/DESCRIPTION
index 8401471..6485996 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SPARKLE
 Type: Package
 Title: Single-cell Phenotype Association Research Kit for Large-scale Evaluation
-Version: 0.2.3
+Version: 0.2.4
 Authors@R: c(
   person("Chen", "Xi", ,email = "chenxi199506@stu.pku.edu.cn", role = c("aut", "cre")),
   person("Ding", "Yang", ,email = "dingy@mail.cbi.pku.edu.cn", role = "ctb"),
diff --git a/R/f13.1.Moderation_Analysis_kernal.R b/R/f13.1.Moderation_Analysis_kernal.R
index b39fb65..3e94c57 100644
--- a/R/f13.1.Moderation_Analysis_kernal.R
+++ b/R/f13.1.Moderation_Analysis_kernal.R
@@ -82,7 +82,7 @@ moderation_analysis_auto <- function(cwas.data,Best_model, X.cell=NULL,X.gene=NU
     names(Best_model[["Chosen_model"]]) <- gsub("-", "_", names(Best_model[["Chosen_model"]]) )
     X.cell.fomula <- as.character(Best_model[["Chosen_model"]][[X.cell]]@call[["formula"]])[3]
     X.cell.fomula <- strsplit(X.cell.fomula, " \\+ ")[[1]]
-    #X.cell.fomula <-replace_double_pipe(X.cell.fomula )
+    X.cell.fomula <-replace_double_pipe(X.cell.fomula )
     X.cell.fomula.result <- formula_process_string_vector(X.cell.fomula,ratename = X.cell,clean = Cleaninfo)
 
     return(X.cell.fomula.result)
diff --git a/R/f7.2.cwas_allmodel_heatmap.R b/R/f7.2.cwas_allmodel_heatmap.R
index 0666739..0ce0cfb 100644
--- a/R/f7.2.cwas_allmodel_heatmap.R
+++ b/R/f7.2.cwas_allmodel_heatmap.R
@@ -1,28 +1,28 @@
 #' Generate a heatmap of AIC values for multiple models
-#' 
+#'
 #' This function takes a list of model dataframes and generates a heatmap
 #' to visualize the AIC values across different models.
-#' 
-#' @param mdlist A list of model dataframes. Each element in the list corresponds 
+#'
+#' @param mdlist A list of model dataframes. Each element in the list corresponds
 #'               to a different cell type or model type.
-#' @param method Character string specifying the method to use for ranking. 
+#' @param method Character string specifying the method to use for ranking.
 #'               Currently supports "rank" (default) and "AIC".
-#' 
+#'
 #' @details
 #' The function processes each model dataframe in the input list (\code{mdlist}),
 #' extracts relevant columns (formula, AIC, Pvalue, rank), and combines them into
 #' a single dataframe (\code{alldata}). It then cleans unnecessary columns,
-#' orders the data, and prepares it for heatmap plotting. P-values are used to 
+#' orders the data, and prepares it for heatmap plotting. P-values are used to
 #' annotate the heatmap cells based on significance levels.
-#' 
+#'
 #' @examples
 #' # Example usage:
 #' # cwas_allmodel_heatmap(list(model_data1, model_data2))
-#' 
+#'
 #' @import pheatmap
 #' @export
-#' 
- 
+#'
+
 cwas_allmodel_heatmap <- function (mdlist,method=c("rank")) {
   i = 1
   celltype <- names(mdlist)
@@ -39,7 +39,7 @@ cwas_allmodel_heatmap <- function (mdlist,method=c("rank")) {
     colnames(mydata) <- paste0(celltype[i], ".", colnames(mydata))
     mydata$name <- mydata[, 1]
     alldata <- merge(alldata, mydata, by = "name", all = T)
-    print(i)
+    #print(i)
   }
   alldata_cleaned <- alldata[, !grepl("celltype", names(alldata))]
   alldata_cleaned <- alldata_cleaned[, !grepl("model", names(alldata_cleaned))]
@@ -47,22 +47,22 @@ cwas_allmodel_heatmap <- function (mdlist,method=c("rank")) {
   alldata_cleaned <- alldata_cleaned[, !grepl("id", names(alldata_cleaned))]
   alldata_cleaned$formula <- alldata[, 2]
   alldata_cleaned$modelID <- alldata[, 3]
-  alldata_cleaned <- alldata_cleaned[order(alldata_cleaned$modelID), 
+  alldata_cleaned <- alldata_cleaned[order(alldata_cleaned$modelID),
   ]
   alldata <- alldata[order(alldata[, 3]), ]
   allformula <- alldata$name
-  pvalue_columns <- grep("Pvalue", names(alldata_cleaned), 
+  pvalue_columns <- grep("Pvalue", names(alldata_cleaned),
                          value = TRUE)
   AICrank_columns <- grep(method, names(alldata_cleaned), value = TRUE)
   pvalue_data <- alldata_cleaned[c("name", pvalue_columns)]
   heatmap_data <- alldata_cleaned[c("name", AICrank_columns)]
-  
+
   ## 绘制热图
-  
+
   heatmap_data2 <- heatmap_data
   heatmap_data2[, -1] <- lapply(heatmap_data2[, -1], as.numeric)
   max_value <- max(heatmap_data2[, -1], na.rm = TRUE)
-  
+
   # 将 heatmap_data2 中的所有 NA 值替换成最大值
   heatmap_data2[is.na(heatmap_data2)] <- max_value
   heatmap_data2$name <- gsub("binomial link function: ", "", heatmap_data2$name)
@@ -70,23 +70,23 @@ cwas_allmodel_heatmap <- function (mdlist,method=c("rank")) {
   colnames(heatmap_data2) <- gsub(".rank", "", colnames(heatmap_data2))
   colnames(heatmap_data2) <- gsub(".AIC", "", colnames(heatmap_data2))
   heatmap_data2$name=NULL
-  
+
   pvalue_matrix <- pvalue_data
   pvalue_matrix$name=NULL
   rownames(pvalue_matrix) <- rownames(heatmap_data2)
   colnames(pvalue_matrix) <- colnames(heatmap_data2)
-  
+
   # 定义p值注释
   annotation_matrix <- ifelse(pvalue_matrix < 0.001, "***",
                               ifelse(pvalue_matrix < 0.01, "**",
                                      ifelse(pvalue_matrix < 0.05, "*", "")))
   annotation_matrix[is.na(annotation_matrix)] <- "NA"
-  
-  
-  pheatmap::pheatmap(heatmap_data2, 
+
+
+  pheatmap::pheatmap(heatmap_data2,
                      display_numbers = annotation_matrix,
                      color = colorRampPalette(c("red", "white"))(100),
-                     cluster_rows = T, 
+                     cluster_rows = T,
                      cluster_cols = T,
                      angle_col = 90,
                      fontsize_row = 10,
@@ -94,6 +94,6 @@ cwas_allmodel_heatmap <- function (mdlist,method=c("rank")) {
                      annotation_colors = list(display_numbers = c("black")),
                      main = paste0("Heatmap of AIC Values for All Models","(",method,")")
   )
-  
+
 }