15 · UpSet 图（多集合交集）

######################
#  公众号：MsTt笔记  #
######################

# 加载必要的包
library(ComplexUpset)   # 用于绘制复杂Upset图
library(ggplot2)        # 用于绘图和图形语法
library(readxl)         # 用于读取Excel文件

# 读入数据----------------------------------------------------------------------
data <- read_xlsx("data2.xlsx")   # 多个比较组的差异分析结果

# 找到 log2FC 列和 padj 列
log2fc_cols <- grep("log2fc", colnames(data))   # 使用正则表达式查找包含"log2fc"的列名
padj_cols   <- grep("padj", colnames(data))     # 使用正则表达式查找包含"padj"的列名

# 判断每个基因在任意时间点是否为差异表达，并创建逻辑矩阵
is_DEG <- apply(data, 1, function(row) {
  # 对每一行数据，检查是否存在至少一个时间点满足：|log2FC| > 0.5 且 padj < 0.05
  any(abs(as.numeric(row[log2fc_cols])) > 0.5 & as.numeric(row[padj_cols]) < 0.05)
})

# 筛选差异基因：从原始数据中提取被识别为差异表达基因的行
DEG_df <- data[is_DEG, ]


# 对差异基因进行分类：根据log2FC值模式将基因分为三类：
# 如果所有log2FC值都≥0，或者虽有负值但都不小于-0.5，分类为"Apo共生昆虫上调"
# 如果所有log2FC值都≤0，或者虽有正值但都不大于0.5，分类为"Sym共生昆虫上调"
# 其他情况分类为"混合表达"

log2fc_cols <- grep("log2fc", colnames(DEG_df))
DEG_df$Expression <- apply(DEG_df[, log2fc_cols], 1, function(x) {
  x <- as.numeric(x)
  if(all(x >= 0) || (any(x < 0) && all(x >= -0.5))) {
    return("Up-regulated in Apoymbiotic Insects")
  } else if(all(x <= 0) || (any(x > 0) && all(x <= 0.5))) {
    return("Up-regulated in Symbiotic Insects")
  } else {
    return("Mixed expression")
  }
})


# 将数据转成upset需要的0/1格式--------------------------------------------------

# 创建Apo方向的二元矩阵：log2FC > 0.5的位置标记为1，否则为0
Apo_mat <- as.data.frame(
  lapply(DEG_df[, log2fc_cols], function(x) as.integer(as.numeric(x) > 0.5))
)
# 创建Sym方向的二元矩阵：log2FC < -0.5的位置标记为1，否则为0
Sym_mat <- as.data.frame(
  lapply(DEG_df[, log2fc_cols], function(x) as.integer(as.numeric(x) < -0.5))
)

# 重命名列：将列名中的".log2fc"替换为组名后缀
colnames(Apo_mat) <- gsub("\\.log2fc", "_Apo", colnames(DEG_df)[log2fc_cols])
colnames(Sym_mat) <- gsub("\\.log2fc", "_Sym", colnames(DEG_df)[log2fc_cols])

# 合并两个矩阵：将GeneID、Apo矩阵、Sym矩阵和表达分类合并为一个数据框
binary_mat <- cbind(DEG_df$GeneID, Apo_mat, Sym_mat, Expression = DEG_df$Expression)
colnames(binary_mat)[1] <- "GeneID"   # 重命名第一列为GeneID

# 将表达方向设为因子：明确指定因子水平顺序以确保绘图时的一致性
binary_mat$Expression <- factor(binary_mat$Expression,
                                levels = c("Up-regulated in Apoymbiotic Insects",
                                           "Up-regulated in Symbiotic Insects",
                                           "Mixed expression"))

# 设置绘图顺序：定义集合在Upset图中的显示顺序
set_cols <- c("D9_Sym","D7_Sym","D5_Sym","D3_Sym","D1_Sym",
              "D9_Apo","D7_Apo","D5_Apo","D3_Apo","D1_Apo")


# 绘图 -------------------------------------------------------------------------
# 基础Upset图
upset(
  binary_mat,
  intersect = set_cols,       # 指定用于构建交集的集合（列）    
  name = NULL,                # 图例标题设为NULL，表示不显示
  height_ratio = 1,           # 图形高度比例
  width_ratio = 0.12,         # 图形宽度比例
  min_size = 21,              # 交集包含的最小观测数，小于此值的交集将被排除
  keep_empty_groups = TRUE,   # 是否保留空集合（包括经过大小筛选后为空的集合）
  sort_sets = FALSE           # 是否对集合矩阵中的行进行排序，选FALSE才会按照前面指定的顺序排列集合
)


# 完整Upset图：包含所有自定义样式和注释
upset(
  binary_mat,
  intersect = set_cols,       
  name = NULL,
  height_ratio = 1,
  width_ratio = 0.12, 
  min_size = 21, 
  keep_empty_groups = TRUE,
  sort_sets = FALSE,
  
  # 设置下方矩阵行的背景色：交替色背景增强可读性
  stripes=c('white','#e7dcf5','white','#e7dcf5','white',     # Sym组：白紫交替
            '#e4e4e4','white','#e4e4e4','white','#e4e4e4'),  # Apo组：白灰交替
  
  # 定义上方条形图的样式：交集大小柱状图
  base_annotations = list(
    'Intersection size' = intersection_size(
      counts = TRUE,                           # 显示交集大小的数值
      text=list(color = 'black', size = 3),    # 数值的文本颜色和大小
      mapping = aes(fill = Expression),        # 柱子的填充颜色：根据Expression列填充颜色
    ) +
      scale_fill_manual(values = c(            # 柱子的填充颜色的映射关系
        "Up-regulated in Apoymbiotic Insects" = "#a9a9a9",
        "Up-regulated in Symbiotic Insects"   = "#986bd3",
        "Mixed expression"                    = "#feac03"
      )) +
      scale_y_continuous(limits = c(0, 350),        # 设置Y轴范围
                         breaks = seq(0, 350, 50),  # 设置Y轴刻度
                         expand = c(0, 0))          # 移除轴扩展
  ),
  
  # 定义左侧条形图的样式：显示每个集合的基因数量
  set_sizes = upset_set_size(
    geom = geom_bar(width = 0.7)) +   # 使用柱状图，并设置宽度
    ylab("Number of DEGs"),           # 设置Y轴标签名
  
  # 定义下方矩阵点图：显示基因在各集合中的存在情况
  matrix = intersection_matrix(
    geom = geom_point(
      # 根据分组设置不同的形状：Sym用圆形(21)，Apo用菱形(24)
      aes(shape = after_scale(ifelse(y %in% c(1,2,3,4,5), 21, 24))),
      size = 2.5)   # 设置点的大小
  ),
  
  # 定义queries：为每个集合指定颜色
  queries = list(
    # Apo组的颜色设置：从浅红到深蓝的渐变
    upset_query(set = 'D1_Apo', color = '#ff9c98', fill = '#ff9c98'),
    upset_query(set = 'D3_Apo', color = '#b41600', fill = '#b41600'),
    upset_query(set = 'D5_Apo', color = '#8a0001', fill = '#8a0001'),
    upset_query(set = 'D7_Apo', color = '#182d80', fill = '#182d80'),
    upset_query(set = 'D9_Apo', color = '#1949e4', fill = '#1949e4'),
    
    # Sym组的颜色设置：使用与Apo组相同的颜色方案
    upset_query(set = 'D1_Sym', color = '#ff9c98', fill = '#ff9c98'),
    upset_query(set = 'D3_Sym', color = '#b41600', fill = '#b41600'),
    upset_query(set = 'D5_Sym', color = '#8a0001', fill = '#8a0001'),
    upset_query(set = 'D7_Sym', color = '#182d80', fill = '#182d80'),
    upset_query(set = 'D9_Sym', color = '#1949e4', fill = '#1949e4')
  ),
  
  # 设置主题：自定义各个小图的样式
  themes = upset_modify_themes(
    list(
      # 上方条形图
      'Intersection size' = theme(
        panel.grid.major = element_blank(),       # 移除主要网格线
        panel.grid.minor = element_blank(),       # 移除次要网格线
        axis.ticks.length.y = unit(0.05, "cm"),   # 设置Y轴刻度长度
        axis.ticks.y = element_line(color = "grey23", linewidth = 0.4),   # Y轴刻度样式
        axis.line.x = element_line(color = "grey23", linewidth = 0.4),    # X轴线样式
        axis.title.y = element_text(size = 11),                           # Y轴标题样式
        axis.text.y =element_text(size = 9, color = "black"),             # Y轴文本样式
        legend.position = "none"    # 移除图例
      ),
      
      # 左侧条形图
      'overall_sizes' = theme(
        panel.grid.major = element_blank(),             # 移除主要网格线
        panel.grid.minor = element_blank(),             # 移除次要网格线
        axis.ticks.x = element_line(color = "grey23"),  # X轴刻度样式
        axis.line.x = element_line(color = "grey23", linewidth = 0.6),  # X轴线样式
        axis.text.x = element_text(size = 9, color="black"),            # X轴文本样式
        axis.title.x = element_text(size = 11)                          # X轴标题样式
      ),
      
      # 下方矩阵点图
      'intersections_matrix' = theme(
        panel.grid.major = element_blank(),   # 移除主要网格线
        panel.grid.minor = element_blank(),   # 移除次要网格线
        axis.text.y = element_text(size = 9, color = "black")  # Y轴文本样式
      )
    )
  )
  
)


# 保存图形
ggsave("ComplexUpset_plot.pdf", width = 12, height = 5)
ggsave("ComplexUpset_plot.png", width = 12, height = 5)

######################
#  公众号：MsTt笔记  #
######################

# 加载必要的包
library(VennDiagram)
library(dplyr)

# 分组提取基因集合：假设已经准备好DEG_df（见upset部分）
Up_Sym <- DEG_df %>% 
  filter(Expression != "Up-regulated in Apoymbiotic Insects") %>% 
  pull(GeneID)

Up_Apo <- DEG_df %>% 
  filter(Expression != "Up-regulated in Symbiotic Insects") %>% 
  pull(GeneID)

# 绘制Venn图
venn.plot <- venn.diagram(
  x = list(Apoymbiotic = Up_Apo, Symbiotic = Up_Sym),
  filename = NULL,       # 不保存文件，直接绘制
  scaled = FALSE,        # 是否根据比例显示大小
  category.names = c("Up-regulation in\nApoymbiotic", 
                     "Up-regulation\nin Symbiotic"),
  fill = c("#a9a9a9", "#986bd3"),   # 圈的内部填充颜色
  alpha = 1,                        # 圈的内部填充颜色的透明度
  col = "black",                    # 圈的外部边框线的颜色
  lwd = 3,                          # 圈的外部边框线的粗细
  lty = 1,                          # 圈的外部边框线的类型，1为直线
  label.col = "white",        # 圆内数字的字体颜色
  cex = 1.8,                  # 圈内数字的字体大小
  fontfamily = "ArialMT",     # 圆内数字的字体
  cat.col = c("#444444", "#953df8" ),   # 外圈类别标签的颜色
  cat.cex = 1.8,                        # 外圈类别标签的字体大小
  cat.fontfamily = "ArialMT",           # 外圈类别标签的字体
  cat.dist = 0.15,          # 圈的半径
  cat.pos = c(45, -145),    # 调整标签位置,顺时钟360度
  rotation.degree = -90,    # 整体旋转 90°
  margin = 0.1,             # 边距
  imagetype = "pdf"
)
# 显示图形
grid::grid.newpage()
grid::grid.draw(venn.plot)


# 打开 pdf 设备并写入
pdf("Venn_plot.pdf", width = 5, height = 5)
grid::grid.newpage()
grid::grid.draw(venn.plot)
dev.off()  # 关闭PDF设备，保存输出

# 打开 png 设备并写入
png("Venn_plot.png", width = 500, height = 500)
grid::grid.newpage()
grid::grid.draw(venn.plot)
dev.off()  # 关闭PDF设备，保存输出

# 加载必要的包
library(UpSetR)     # 用于绘制UpSet图
library(ggplot2)    # 用于图形绘制（UpSetR部分依赖）
library(readxl)     # 用于读取Excel文件
library(tibble)     # 提供 column_to_rownames() 等数据框转换函数


# 读入数据
data_mat <- read_excel("data1.xlsx")   # 0/1型数据
data_mat <- column_to_rownames(data_mat, var = colnames(data_mat)[1])   # 第一列作为行名


# 绘制基础 upset 图-------------------------------------------------------------
upset(
  data_mat, nintersects = 25,    # 显示前25个交集
  sets = names(data_mat),        # 使用全部集合
  keep.order = TRUE,             # 这里是True：代表左侧集合条形的顺序，会与表格中列的顺序相同
  matrix.dot.alpha = 0.5,                  # 矩阵中点的透明度
  point.size = 3,                          # 矩阵中点的大小
  line.size = 0.7,                         # 矩阵中点连线的粗细
  mb.ratio = c(0.55, 0.45),                # 上方条形图与下方矩阵图的比例
  mainbar.y.label = "Intersection size",   # 上方条形图Y轴标签
  sets.x.label = "Set size",               # 左侧条形图X轴标签
  text.scale = c(1.8,1.4,1.5,1.4,1.4,1.5), # 各文字元素字体大小（依次为：主Y轴标题、主Y轴刻度、X轴标题、X轴刻度、集合名称、条形数字）
  order.by = "freq"                        # 按交集频数排序
)


# 基于 metadata 美化基础 upset 图-----------------------------------------------

# 定义集合颜色映射
set_colors <- corrplot::COL2('RdBu', 15)[c(4:7, 9:12)]  # 选择 RdBu 颜色梯度中的部分颜色
names(set_colors) <- colnames(data_mat)                 # 给颜色命名以对应集合名

# 设置集合的元信息 metadata
meta_info <- data.frame(
  sets = colnames(data_mat),      # 每个集合名称
  Group = colnames(data_mat)      # 使用集合名本身作为分组依据
)

# 定义集合注释样式（行注释）
set_meta <- list(
  data = meta_info,               # 数据来源
  plots = list(
    list(
      type = "matrix_rows",       # 在矩阵行显示注释
      column = "Group",           # 颜色按“Group”列映射
      colors = set_colors,        # 每个Group对应的颜色
      alpha = 0.25                # 设置填充颜色的透明度
    )
  )
)


# 绘制 UpSet 图-样式1
upset(
  data_mat, nintersects = 25,    # 显示前25个交集
  sets = names(data_mat),        # 使用全部集合
  keep.order = TRUE,             # 这里是True：代表左侧集合条形的顺序，会与表格中列的顺序相同
  set.metadata = set_meta,       # 添加集合注释信息，这里控制矩阵背景颜色和点的颜色
  sets.bar.color = set_colors,   # 左侧集合条形的填充颜色
  main.bar.color = "#4E9AC6",    # 上方交集条形的填充颜色
  queries = list(                # 突出显示指定的交集
    list(query = intersects, params = list("N+_vs_N0"), color= "#ae3336"),
    list(query = intersects, params = list("StageIV_vs_StageIII"), color= "#ae3336"),
    list(query = intersects, params = list("StageIV_vs_StageII"), color= "#ae3336"),
    list(query = intersects, params = list("StageIV_vs_StageI"), color= "#ae3336"),
    list(query = intersects, params = list("StageIII_vs_StageI"), color= "#ae3336"),
    list(query = intersects, params = list("StageII_vs_StageI"), color= "#ae3336"),
    list(query = intersects, params = list("Malignant_vs_Benign"), color= "#ae3336")
  ),
  matrix.dot.alpha = 0.5,                  # 矩阵中点的透明度
  point.size = 3,                          # 矩阵中点的大小
  line.size = 0.7,                         # 矩阵中点连线的粗细
  mb.ratio = c(0.55, 0.45),                # 上方条形图与下方矩阵图的比例
  mainbar.y.label = "Intersection size",   # 上方条形图Y轴标签
  sets.x.label = "Set size",               # 左侧条形图X轴标签
  text.scale = c(1.8,1.4,1.5,1.4,1.4,1.5), # 各文字元素字体大小（依次为：主Y轴标题、主Y轴刻度、X轴标题、X轴刻度、集合名称、条形数字）
  order.by = "freq"                        # 按交集频数排序
)


# 进一步更改上方bar的颜色填充---------------------------------------------------

# 导入自定义函数（例如 myupset）
source("utils.R")   

# 上方交集条形，先默认全部为蓝色
main_bar_colors <- rep("#4E9AC6", 40)   

# 然后再将指定的索引位置，条形颜色修改为红色
main_bar_colors[c(2,3,4,6,10,11,12)] <- "#ae3336"



# 调用自定义函数myupset绘制图形
p <- myupset(
  data_mat, nintersects = 25,        # 显示前25个交集
  sets = names(data_mat),            # 使用全部集合
  keep.order = TRUE,                 # 这里是True：代表左侧集合条形的顺序，会与表格中列的顺序相同
  set.metadata = set_meta,           # 添加集合注释信息，这里控制矩阵背景颜色和点的颜色
  sets.bar.color = set_colors,       # 左侧集合条形的填充颜色
  main.bar.color = main_bar_colors,  # 上方交集条形的填充颜色（与前面代码不同）
  matrix.dot.alpha = 0.5,                   # 矩阵中点的透明度
  point.size = 3,                           # 矩阵中点的大小
  line.size = 0.7,                          # 矩阵中点连线的粗细
  mb.ratio = c(0.55, 0.45),                 # 上方条形图与下方矩阵图的比例
  mainbar.y.label = "Intersection size",    # 上方条形图Y轴标签
  sets.x.label = "Set size",                # 左侧条形图X轴标签
  text.scale = c(1.8,1.4,1.5,1.4,1.4,1.5),  # 各文字元素字体大小（依次为：主Y轴标题、主Y轴刻度、X轴标题、X轴刻度、集合名称、条形数字）
  order.by = "freq"                         # 按交集频数排序
)
# 显示图形
print(p)


# 输出最终定制版UpSet图到PDF
pdf("upsetR_plot.pdf", width = 13, height = 8, onefile = FALSE)
grid::grid.newpage()    # 确保只绘制一页（避免叠图）
print(p)
dev.off()   # 关闭PDF设备，保存输出

Make_matrix_plot <- function(Mat_data,Set_size_data, Main_bar_data, point_size, line_size, text_scale, labels,
                             shading_data, shade_alpha){
  
  if(length(text_scale) == 1){
    name_size_scale <- text_scale
  }
  if(length(text_scale) > 1 && length(text_scale) <= 6){
    name_size_scale <- text_scale[5]
  }
  
  Mat_data$line_col <- 'grey23'
  print(Mat_data)
  
  Matrix_plot <- (ggplot()
                  + theme(panel.background = element_rect(fill = "white"),
                          plot.margin=unit(c(-0.2,0.5,0.5,0.5), "lines"),
                          axis.text.x = element_blank(),
                          axis.ticks.x = element_blank(),
                          axis.ticks.y = element_blank(),
                          axis.text.y = element_text(colour = "gray0",
                                                     size = 7*name_size_scale, hjust = 0.4),
                          panel.grid.major = element_blank(),
                          panel.grid.minor = element_blank())
                  + xlab(NULL) + ylab("   ")
                  + scale_y_continuous(breaks = c(1:nrow(Set_size_data)),
                                       limits = c(0.5,(nrow(Set_size_data) +0.5)),
                                       labels = labels, expand = c(0,0))
                  + scale_x_continuous(limits = c(0,(nrow(Main_bar_data)+1 )), expand = c(0,0))
                  + geom_rect(data = shading_data, aes_string(xmin = "min", xmax = "max",
                                                              ymin = "y_min", ymax = "y_max"),
                              fill = shading_data$shade_color, alpha = shade_alpha)
                  # 无连线的点 (value == 0)
                  + geom_point(data = Mat_data[Mat_data$value == 0, ], aes_string(x= "x", y= "y"),
                               colour = Mat_data$color[Mat_data$value == 0],  size = point_size,
                               shape = 16, alpha = Mat_data$alpha[Mat_data$value == 0])
                  + geom_line(data= Mat_data, aes_string(group = "Intersection", x="x", y="y",
                                                         colour = "line_col"), size = line_size)
                  # 有连线的点 (value != 0)
                  + geom_point(data = Mat_data[Mat_data$value != 0, ], aes_string(x= "x", y= "y"),
                               fill = Mat_data$color[Mat_data$value != 0], colour = "black",
                               size = point_size, shape = 21, alpha = Mat_data$alpha[Mat_data$value != 0])
                  
                  + scale_color_identity())
  Matrix_plot <- ggplot_gtable(ggplot_build(Matrix_plot))
  return(Matrix_plot)
}

## Default upset() arguments
myupset <- function(data, nsets = 5, nintersects = 40, sets = NULL, keep.order = F, 
                    set.metadata = NULL, intersections = NULL, matrix.color = "gray23",
                    main.bar.color = "gray23", mainbar.y.label = "Intersection Size", 
                    mainbar.y.max = NULL, sets.bar.color = "gray23", sets.x.label = "Set Size", 
                    point.size = 2.2, line.size = 0.7, mb.ratio = c(0.7, 0.3), 
                    expression = NULL, att.pos = NULL, att.color = main.bar.color, 
                    order.by = c("freq", "degree"), decreasing = c(T, F), show.numbers = "yes", 
                    number.angles = 0, group.by = "degree", cutoff = NULL, queries = NULL, 
                    query.legend = "none", shade.color = "gray88", shade.alpha = 0.25, 
                    matrix.dot.alpha = 0.5, empty.intersections = NULL, color.pal = 1, 
                    boxplot.summary = NULL, attribute.plots = NULL, scale.intersections = "identity", 
                    scale.sets = "identity", text.scale = 1, set_size.angles = 0, 
                    set_size.show = FALSE, set_size.numbers_size = NULL, set_size.scale_max = NULL)
{
  startend <- UpSetR:::FindStartEnd(data)
  first.col <- startend[1]
  last.col <- startend[2]
  
  if(color.pal == 1){
    palette <- c("#1F77B4", "#FF7F0E", "#2CA02C", "#D62728", "#9467BD", "#8C564B", "#E377C2",
                 "#7F7F7F", "#BCBD22", "#17BECF")
  } else{
    palette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00",
                 "#CC79A7")
  }
  
  if(is.null(intersections) == F){
    Set_names <- unique((unlist(intersections)))
    Sets_to_remove <- UpSetR:::Remove(data, first.col, last.col, Set_names)
    New_data <- UpSetR:::Wanted(data, Sets_to_remove)
    Num_of_set <- UpSetR:::Number_of_sets(Set_names)
    if(keep.order == F){
      Set_names <- UpSetR:::order_sets(New_data, Set_names)
    }
    All_Freqs <- UpSetR:::specific_intersections(data, first.col, last.col, intersections, order.by, group.by, decreasing,
                                                 cutoff, main.bar.color, Set_names)
  } else if(is.null(intersections) == T){
    Set_names <- sets
    if(is.null(Set_names) == T || length(Set_names) == 0 ){
      Set_names <- UpSetR:::FindMostFreq(data, first.col, last.col, nsets)
    }
    Sets_to_remove <- UpSetR:::Remove(data, first.col, last.col, Set_names)
    New_data <- UpSetR:::Wanted(data, Sets_to_remove)
    Num_of_set <- UpSetR:::Number_of_sets(Set_names)
    if(keep.order == F){
      Set_names <- UpSetR:::order_sets(New_data, Set_names)
    }
    All_Freqs <- UpSetR:::Counter(New_data, Num_of_set, first.col, Set_names, nintersects, main.bar.color,
                                  order.by, group.by, cutoff, empty.intersections, decreasing)
  }
  
  Matrix_setup <- UpSetR:::Create_matrix(All_Freqs)
  labels <- UpSetR:::Make_labels(Matrix_setup)
  #Chose NA to represent NULL case as result of NA being inserted when at least one contained both x and y
  #i.e. if one custom plot had both x and y, and others had only x, the y's for the other plots were NA
  #if I decided to make the NULL case (all x and no y, or vice versa), there would have been alot more if/else statements
  #NA can be indexed so that we still get the non NA y aesthetics on correct plot. NULL cant be indexed.
  att.x <- c(); att.y <- c();
  if(is.null(attribute.plots) == F){
    for(i in seq_along(attribute.plots$plots)){
      if(length(attribute.plots$plots[[i]]$x) != 0){
        att.x[i] <- attribute.plots$plots[[i]]$x
      }
      else if(length(attribute.plots$plots[[i]]$x) == 0){
        att.x[i] <- NA
      }
      if(length(attribute.plots$plots[[i]]$y) != 0){
        att.y[i] <- attribute.plots$plots[[i]]$y
      }
      else if(length(attribute.plots$plots[[i]]$y) == 0){
        att.y[i] <- NA
      }
    }
  }
  
  BoxPlots <- NULL
  if(is.null(boxplot.summary) == F){
    BoxData <- UpSetR:::IntersectionBoxPlot(All_Freqs, New_data, first.col, Set_names)
    BoxPlots <- list()
    for(i in seq_along(boxplot.summary)){
      BoxPlots[[i]] <- UpSetR:::BoxPlotsPlot(BoxData, boxplot.summary[i], att.color)
    }
  }
  
  customAttDat <- NULL
  customQBar <- NULL
  Intersection <- NULL
  Element <- NULL
  legend <- NULL
  EBar_data <- NULL
  if(is.null(queries) == F){
    custom.queries <- UpSetR:::SeperateQueries(queries, 2, palette)
    customDat <- UpSetR:::customQueries(New_data, custom.queries, Set_names)
    legend <- UpSetR:::GuideGenerator(queries, palette)
    legend <- UpSetR:::Make_legend(legend)
    if(is.null(att.x) == F && is.null(customDat) == F){
      customAttDat <- UpSetR:::CustomAttData(customDat, Set_names)
    }
    customQBar <- UpSetR:::customQueriesBar(customDat, Set_names, All_Freqs, custom.queries)
  }
  if(is.null(queries) == F){
    Intersection <- UpSetR:::SeperateQueries(queries, 1, palette)
    Matrix_col <- UpSetR:::intersects(UpSetR:::QuerieInterData, Intersection, New_data, first.col, Num_of_set,
                                      All_Freqs, expression, Set_names, palette)
    Element <- UpSetR:::SeperateQueries(queries, 1, palette)
    EBar_data <-UpSetR:::ElemBarDat(Element, New_data, first.col, expression, Set_names,palette, All_Freqs)
  } else{
    Matrix_col <- NULL
  }
  
  Matrix_layout <- UpSetR:::Create_layout(Matrix_setup, matrix.color, Matrix_col, matrix.dot.alpha)
  
  ## our modification
  for(i in 1:Num_of_set) {
    j <- which(Matrix_layout$y == i & Matrix_layout$value == 1)
    if(length(j) > 0) Matrix_layout$color[j] <- sets.bar.color[i]
  }
  
  ## continuing with upset()
  Set_sizes <- UpSetR:::FindSetFreqs(New_data, first.col, Num_of_set, Set_names, keep.order)
  Bar_Q <- NULL
  if(is.null(queries) == F){
    Bar_Q <- UpSetR:::intersects(UpSetR:::QuerieInterBar, Intersection, New_data, first.col, Num_of_set, All_Freqs, expression, Set_names, palette)
  }
  QInter_att_data <- NULL
  QElem_att_data <- NULL
  if((is.null(queries) == F) & (is.null(att.x) == F)){
    QInter_att_data <- UpSetR:::intersects(UpSetR:::QuerieInterAtt, Intersection, New_data, first.col, Num_of_set, att.x, att.y,
                                           expression, Set_names, palette)
    QElem_att_data <- UpSetR:::elements(UpSetR:::QuerieElemAtt, Element, New_data, first.col, expression, Set_names, att.x, att.y,
                                        palette)
  }
  AllQueryData <- UpSetR:::combineQueriesData(QInter_att_data, QElem_att_data, customAttDat, att.x, att.y)
  
  ShadingData <- NULL
  
  if(is.null(set.metadata) == F){
    ShadingData <- UpSetR:::get_shade_groups(set.metadata, Set_names, Matrix_layout, shade.alpha)
    output <- UpSetR:::Make_set_metadata_plot(set.metadata, Set_names)
    set.metadata.plots <- output[[1]]
    set.metadata <- output[[2]]
    
    if(is.null(ShadingData) == FALSE){
      shade.alpha <- unique(ShadingData$alpha)
    }
  } else {
    set.metadata.plots <- NULL
  }
  if(is.null(ShadingData) == TRUE){
    ShadingData <- UpSetR:::MakeShading(Matrix_layout, shade.color)
  }
  Main_bar <- suppressMessages(UpSetR:::Make_main_bar(All_Freqs, Bar_Q, show.numbers, mb.ratio, customQBar, number.angles, EBar_data, mainbar.y.label,
                                                      mainbar.y.max, scale.intersections, text.scale, attribute.plots))
  
  Matrix <- Make_matrix_plot(Matrix_layout, Set_sizes, All_Freqs, point.size, line.size,
                             text.scale, labels, ShadingData, shade.alpha)
  Sizes <- UpSetR:::Make_size_plot(Set_sizes, sets.bar.color, mb.ratio,sets.x.label, scale.sets, text.scale, set_size.angles, set_size.show, set_size.scale_max, set_size.numbers_size)
  
  structure(class = "upset",
            .Data=list(
              Main_bar = Main_bar,
              Matrix = Matrix,
              Sizes = Sizes,
              labels = labels,
              mb.ratio = mb.ratio,
              att.x = att.x,
              att.y = att.y,
              New_data = New_data,
              expression = expression,
              att.pos = att.pos,
              first.col = first.col,
              att.color = att.color,
              AllQueryData = AllQueryData,
              attribute.plots = attribute.plots,
              legend = legend,
              query.legend = query.legend,
              BoxPlots = BoxPlots,
              Set_names = Set_names,
              set.metadata = set.metadata,
              set.metadata.plots = set.metadata.plots)
  )
}