Upload Figure4 and FigureS4/Figure4 and FigureS4 v2.qmd

Figure4 and FigureS4/Figure4 and FigureS4 v2.qmd

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+---
+title: "Figure 5 and S5 v2"
+format: html
+editor: visual
+---
+
+## Quarto
+
+Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see <https://quarto.org>.
+
+## Running Code
+
+## 1.单因素方差分析
+
+### 1.1组合过表达统计分析
+
+```{r}
+# =====================================================
+# ANOVA分析 - SCI发表标准版
+# 优化图形，标注p值
+# =====================================================
+
+# 加载必要的包
+library(ggplot2)
+library(ggsignif)
+library(dplyr)
+library(tidyr)
+
+# 准备数据
+data <- data.frame(
+  control = c(10, 12, 16, 11, 12, 5, 1, 5, 6),
+  GA = c(30, 28, 12, 10, 23, 14, 1, 20, 14),
+  GB = c(16, 49, 45, 28, 15, 43, 33, 19, 12),
+  GC = c(20, 41, 23, 11, 31, 4, 0, 26, 32)
+)
+
+# 转换为长格式
+data_long <- pivot_longer(data, 
+                         cols = everything(),
+                         names_to = "group",
+                         values_to = "value")
+
+data_long$group <- factor(data_long$group, 
+                         levels = c("control", "GA", "GB", "GC"))
+
+# =====================================================
+# 统计分析
+# =====================================================
+
+# ANOVA
+anova_result <- aov(value ~ group, data = data_long)
+summary_anova <- summary(anova_result)
+anova_p <- summary_anova[[1]]$`Pr(>F)`[1]
+
+# 效应量 (eta-squared)
+eta_squared <- summary_anova[[1]]$`Sum Sq`[1] / sum(summary_anova[[1]]$`Sum Sq`)
+
+# Tukey HSD事后检验
+tukey_result <- TukeyHSD(anova_result)
+
+# 提取与control组的比较结果
+control_comparisons <- tukey_result$group[grep("control", rownames(tukey_result$group)), ]
+
+# 创建比较结果数据框
+comparisons_df <- data.frame(
+  comparison = rownames(control_comparisons),
+  diff = control_comparisons[, "diff"],
+  p_adj = control_comparisons[, "p adj"],
+  stars = case_when(
+    control_comparisons[, "p adj"] < 0.001 ~ "***",
+    control_comparisons[, "p adj"] < 0.01 ~ "**",
+    control_comparisons[, "p adj"] < 0.05 ~ "*",
+    TRUE ~ "ns"
+  ),
+  p_label = case_when(
+    control_comparisons[, "p adj"] < 0.001 ~ "p < 0.001",
+    control_comparisons[, "p adj"] < 0.01 ~ paste0("p = ", round(control_comparisons[, "p adj"], 3)),
+    control_comparisons[, "p adj"] < 0.05 ~ paste0("p = ", round(control_comparisons[, "p adj"], 3)),
+    TRUE ~ paste0("p = ", round(control_comparisons[, "p adj"], 3))
+  )
+)
+
+# 打印统计结果
+cat("\n========== ANOVA Results ==========\n")
+print(summary_anova)
+cat("\nEffect size (η²) =", round(eta_squared, 3), "\n")
+
+cat("\n========== Tukey HSD Results (vs control) ==========\n")
+print(comparisons_df)
+
+# 描述性统计
+summary_stats <- data_long %>%
+  group_by(group) %>%
+  summarise(
+    n = n(),
+    Mean = round(mean(value), 2),
+    SD = round(sd(value), 2),
+    SEM = round(sd(value)/sqrt(n()), 2),
+    Min = min(value),
+    Max = max(value)
+  )
+
+cat("\n========== Descriptive Statistics ==========\n")
+print(summary_stats)
+
+# =====================================================
+# SCI期刊标准主题
+# =====================================================
+
+theme_sci <- theme_classic(base_size = 11) +
+  theme(
+    axis.title = element_text(size = 11, face = "bold"),
+    axis.text = element_text(size = 9, color = "black"),
+    axis.line = element_line(color = "black", size = 0.5),
+    axis.ticks = element_line(color = "black", size = 0.5),
+    legend.position = "none",
+    plot.title = element_text(size = 12, face = "bold", hjust = 0.5),
+    plot.subtitle = element_text(size = 9, color = "gray40", hjust = 0.5),
+    plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm")
+  )
+
+# =====================================================
+# 计算y轴最大位置（为p值标注留空间）
+# =====================================================
+
+y_max <- max(data_long$value)
+y_positions <- c(y_max + 8, y_max + 12, y_max + 10)  # 调整位置避免重叠
+
+# =====================================================
+# SCI标准配色
+# =====================================================
+
+sci_colors <- c(
+  "control" = "#999999",  # 灰色
+  "GA" = "#E69F00",       # 橙色
+  "GB" = "#56B4E9",       # 蓝色
+  "GC" = "#009E73"        # 绿色
+)
+
+# =====================================================
+# 方法1：星号标注（最简洁，SCI常用）
+# =====================================================
+
+p1 <- ggplot(data_long, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.6, size = 1.8, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 15)
+
+# 添加ANOVA结果
+p1 <- p1 + annotate("text", x = 2.5, y = y_max + 12,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3),
+                                   ", η² = ", round(eta_squared, 3)),
+                    size = 3, fontface = "italic")
+
+# 添加星号标注（只标注显著的）
+for (i in 1:nrow(comparisons_df)) {
+  if (comparisons_df$p_adj[i] < 0.05) {
+    grp_name <- gsub("-control", "", comparisons_df$comparison[i])
+    grp_x <- which(levels(data_long$group) == grp_name)
+    
+    p1 <- p1 + annotate("text",
+                        x = grp_x,
+                        y = y_max + 5,
+                        label = comparisons_df$stars[i],
+                        size = 6, fontface = "bold")
+  }
+}
+
+print(p1)
+
+# =====================================================
+# 方法2：bracket + p值（更正式）
+# =====================================================
+
+p2 <- ggplot(data_long, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.6, size = 1.8, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 18)
+
+# 添加ANOVA结果
+p2 <- p2 + annotate("text", x = 2.5, y = y_max + 15,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3),
+                                   ", η² = ", round(eta_squared, 3)),
+                    size = 3, fontface = "italic")
+
+# 添加bracket和p值（只标注显著的）
+sig_idx <- which(comparisons_df$p_adj < 0.05)
+for (i in seq_along(sig_idx)) {
+  idx <- sig_idx[i]
+  grp_name <- gsub("-control", "", comparisons_df$comparison[idx])
+  grp_x <- which(levels(data_long$group) == grp_name)
+  y_pos <- y_positions[i]
+  
+  # 绘制bracket线
+  p2 <- p2 + 
+    annotate("segment",
+             x = 1, xend = grp_x,
+             y = y_pos, yend = y_pos,
+             color = "black", size = 0.4) +
+    annotate("segment",
+             x = 1, xend = 1,
+             y = y_pos - 0.3, yend = y_pos,
+             color = "black", size = 0.4) +
+    annotate("segment",
+             x = grp_x, xend = grp_x,
+             y = y_pos - 0.3, yend = y_pos,
+             color = "black", size = 0.4) +
+    annotate("text",
+             x = (1 + grp_x) / 2,
+             y = y_pos + 0.5,
+             label = comparisons_df$p_label[idx],
+             size = 2.8, fontface = "italic")
+}
+
+print(p2)
+
+# =====================================================
+# 方法3：使用ggsignif包
+# =====================================================
+
+# 创建比较列表和对应的y位置
+comparison_list <- list(c("control", "GA"), c("control", "GB"), c("control", "GC"))
+annotation_list <- comparisons_df$p_label
+y_pos_ggsignif <- c(y_max + 8, y_max + 12, y_max + 16)
+
+p3 <- ggplot(data_long, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.6, size = 1.8, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 20)
+
+# 添加ANOVA结果
+p3 <- p3 + annotate("text", x = 2.5, y = y_max + 17,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3),
+                                   ", η² = ", round(eta_squared, 3)),
+                    size = 3, fontface = "italic")
+
+# 使用ggsignif添加显著性标记
+p3 <- p3 + geom_signif(
+  comparisons = comparison_list,
+  annotations = annotation_list,
+  y_position = y_pos_ggsignif,
+  tip_length = 0.01,
+  size = 0.4,
+  textsize = 2.8,
+  vjust = -0.2
+)
+
+print(p3)
+
+# =====================================================
+# 保存图形
+# =====================================================
+
+output_dir <- "I:/RV Entry Factor Paper/Figure5 and FigureS5"
+if(!dir.exists(output_dir)) {
+  dir.create(output_dir, recursive = TRUE)
+}
+
+# 保存三种版本
+ggsave(file.path(output_dir, "Figure5_ANOVA_stars.pdf"), 
+       p1, width = 5, height = 5, device = cairo_pdf)
+ggsave(file.path(output_dir, "Figure5_ANOVA_stars.png"), 
+       p1, width = 5, height = 5, dpi = 300, bg = "white")
+
+ggsave(file.path(output_dir, "Figure5_ANOVA_bracket.pdf"), 
+       p2, width = 5, height = 5, device = cairo_pdf)
+
+ggsave(file.path(output_dir, "Figure5_ANOVA_ggsignif.pdf"), 
+       p3, width = 5, height = 5, device = cairo_pdf)
+
+# 保存统计结果
+write.csv(summary_stats, file.path(output_dir, "Summary_statistics.csv"), row.names = FALSE)
+write.csv(comparisons_df, file.path(output_dir, "Tukey_HSD_results.csv"), row.names = FALSE)
+
+cat("\n========== Analysis Complete ==========\n")
+cat("Output directory:", output_dir, "\n")
+cat("\nGenerated files:\n")
+cat("  - Figure5_ANOVA_stars.pdf (星号标注)\n")
+cat("  - Figure5_ANOVA_stars.png\n")
+cat("  - Figure5_ANOVA_bracket.pdf (bracket+p值)\n")
+cat("  - Figure5_ANOVA_ggsignif.pdf (ggsignif包)\n")
+cat("  - Summary_statistics.csv\n")
+cat("  - Tukey_HSD_results.csv\n")
+```
+
+### 1.2 GroupB单独过表达统计分析
+
+```{r}
+# =====================================================
+# SAD-B19感染实验 - 各组与Control比较（只保留同一批次Control）
+# Control批次: 1, 5, 6
+# =====================================================
+
+# 加载必要的包
+library(ggplot2)
+library(dplyr)
+library(cowplot)
+
+# 设置输出路径（请根据实际情况修改）
+output_dir <- "I:/RV Entry Factor Paper/Figure5 and FigureS5"
+if(!dir.exists(output_dir)) {
+  dir.create(output_dir, recursive = TRUE)
+}
+
+# =====================================================
+# 1. 准备数据
+# =====================================================
+
+# 只使用同一批次的Control
+data_list <- list(
+  control = c(1, 5, 6),  # 只保留同一批次的3个值
+  Car10 = c(9, 12, 10),
+  Cplx2 = c(10, 14, 20, 12),
+  Slc1a2 = c(7, 12, 11, 1),
+  Fxyd7 = c(4, 11, 1),
+  Arc = c(1, 8, 0)
+)
+
+# 转换为长格式数据框
+df <- data.frame()
+for (grp in names(data_list)) {
+  df <- rbind(df, data.frame(
+    group = grp,
+    value = data_list[[grp]]
+  ))
+}
+
+# 设置因子顺序（control作为第一组）
+df$group <- factor(df$group, levels = c("control", "Car10", "Cplx2", "Slc1a2", "Fxyd7", "Arc"))
+
+# =====================================================
+# 2. 描述性统计
+# =====================================================
+
+summary_stats <- df %>%
+  group_by(group) %>%
+  summarise(
+    n = n(),
+    Mean = round(mean(value), 2),
+    SD = round(sd(value), 2),
+    SEM = round(sd(value)/sqrt(n()), 2),
+    Min = min(value),
+    Max = max(value)
+  )
+
+cat("\n========== Descriptive Statistics ==========\n")
+print(as.data.frame(summary_stats))
+
+# =====================================================
+# 3. 单因素ANOVA
+# =====================================================
+
+anova_result <- aov(value ~ group, data = df)
+summary_anova <- summary(anova_result)
+anova_p <- summary_anova[[1]]$`Pr(>F)`[1]
+eta_squared <- summary_anova[[1]]$`Sum Sq`[1] / sum(summary_anova[[1]]$`Sum Sq`)
+
+cat("\n========== ANOVA Results ==========\n")
+print(summary_anova)
+cat("\nEffect size (η²) =", round(eta_squared, 3), "\n")
+
+# =====================================================
+# 4. 各组与Control的t检验
+# =====================================================
+
+control_data <- data_list$control
+test_groups <- c("Car10", "Cplx2", "Slc1a2", "Fxyd7", "Arc")
+
+# 存储结果
+results <- data.frame(
+  group = test_groups,
+  mean_control = mean(control_data),
+  mean_test = NA,
+  mean_diff = NA,
+  p_value = NA,
+  p_label = NA
+)
+
+for (i in 1:length(test_groups)) {
+  grp <- test_groups[i]
+  test_data <- data_list[[grp]]
+  t_result <- t.test(test_data, control_data)
+  
+  results$mean_test[i] <- mean(test_data)
+  results$mean_diff[i] <- mean(test_data) - mean(control_data)
+  results$p_value[i] <- t_result$p.value
+  
+  # 格式化p值标签
+  if (t_result$p.value < 0.001) {
+    results$p_label[i] <- "p < 0.001"
+  } else if (t_result$p.value < 0.01) {
+    results$p_label[i] <- paste0("p = ", round(t_result$p.value, 3))
+  } else if (t_result$p.value < 0.05) {
+    results$p_label[i] <- paste0("p = ", round(t_result$p.value, 3))
+  } else {
+    results$p_label[i] <- paste0("p = ", round(t_result$p.value, 3))
+  }
+}
+
+cat("\n========== Pairwise t-test (each group vs control) ==========\n")
+print(results)
+
+# =====================================================
+# 5. SCI期刊标准主题
+# =====================================================
+
+theme_sci <- theme_classic(base_size = 11) +
+  theme(
+    axis.title = element_text(size = 11, face = "bold"),
+    axis.text = element_text(size = 9, color = "black"),
+    axis.line = element_line(color = "black", size = 0.5),
+    axis.ticks = element_line(color = "black", size = 0.5),
+    legend.position = "none",
+    plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm")
+  )
+
+# =====================================================
+# 6. SCI标准配色
+# =====================================================
+
+sci_colors <- c(
+  "control" = "#999999",  # 灰色
+  "Car10" = "#E69F00",    # 橙色
+  "Cplx2" = "#56B4E9",    # 蓝色
+  "Slc1a2" = "#009E73",   # 绿色
+  "Fxyd7" = "#F0E442",    # 黄色
+  "Arc" = "#D55E00"       # 红色
+)
+
+# =====================================================
+# 7. 计算y轴位置（为p值标注留空间）
+# =====================================================
+
+group_max <- df %>%
+  group_by(group) %>%
+  summarise(max_val = max(value))
+
+control_max <- group_max$max_val[group_max$group == "control"]
+y_max <- max(df$value)
+
+# 为每个实验组计算标注位置
+for (i in 1:nrow(results)) {
+  grp <- results$group[i]
+  test_max <- group_max$max_val[group_max$group == grp]
+  results$y_pos[i] <- max(control_max, test_max) + 1 + (i * 0.8)
+}
+
+# =====================================================
+# 8. 方法1：在每个实验组上方直接标注p值
+# =====================================================
+
+p1 <- ggplot(df, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.7, size = 2.5, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of SAD-B19 infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 6)
+
+# 添加ANOVA结果
+p1 <- p1 + annotate("text", x = 3.5, y = y_max + 5,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3),
+                                   ", η² = ", round(eta_squared, 3)),
+                    size = 3, fontface = "italic")
+
+# 在每个实验组上方标注p值
+for (i in 1:nrow(results)) {
+  grp_x <- which(levels(df$group) == results$group[i])
+  grp_y <- results$y_pos[i]
+  
+  p1 <- p1 + annotate("text",
+                      x = grp_x,
+                      y = grp_y,
+                      label = results$p_label[i],
+                      size = 3, fontface = "italic")
+}
+
+print(p1)
+
+# =====================================================
+# 9. 方法2：用bracket线连接Control和实验组，标注p值
+# =====================================================
+
+# 计算bracket的y位置
+y_positions <- seq(y_max + 1, y_max + 5, length.out = nrow(results))
+
+p2 <- ggplot(df, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.7, size = 2.5, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of SAD-B19 infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 7)
+
+# 添加ANOVA结果
+p2 <- p2 + annotate("text", x = 3.5, y = y_max + 6,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3),
+                                   ", η² = ", round(eta_squared, 3)),
+                    size = 3, fontface = "italic")
+
+# 为每个实验组添加bracket和p值
+for (i in 1:nrow(results)) {
+  grp_x <- which(levels(df$group) == results$group[i])
+  y_pos <- y_positions[i]
+  
+  # 绘制bracket线
+  p2 <- p2 + 
+    annotate("segment",
+             x = 1, xend = grp_x,
+             y = y_pos, yend = y_pos,
+             color = "black", size = 0.4) +
+    annotate("segment",
+             x = 1, xend = 1,
+             y = y_pos - 0.3, yend = y_pos,
+             color = "black", size = 0.4) +
+    annotate("segment",
+             x = grp_x, xend = grp_x,
+             y = y_pos - 0.3, yend = y_pos,
+             color = "black", size = 0.4) +
+    # 添加p值标签
+    annotate("text",
+             x = (1 + grp_x) / 2,
+             y = y_pos + 0.5,
+             label = results$p_label[i],
+             size = 2.8, fontface = "italic")
+}
+
+print(p2)
+
+# =====================================================
+# 10. 方法3：星号标注（SCI最常用）
+# =====================================================
+
+# 为每个实验组生成星号
+results$stars <- case_when(
+  results$p_value < 0.001 ~ "***",
+  results$p_value < 0.01 ~ "**",
+  results$p_value < 0.05 ~ "*",
+  TRUE ~ "ns"
+)
+
+p3 <- ggplot(df, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.7, size = 2.5, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of SAD-B19 infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 3)
+
+# 添加ANOVA结果
+p3 <- p3 + annotate("text", x = 3.5, y = y_max + 2.5,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3)),
+                    size = 3, fontface = "italic")
+
+# 添加星号标注
+for (i in 1:nrow(results)) {
+  if (results$p_value[i] < 0.05) {
+    grp_x <- which(levels(df$group) == results$group[i])
+    test_max <- group_max$max_val[group_max$group == results$group[i]]
+    y_pos <- test_max + 0.8
+    
+    p3 <- p3 + annotate("text",
+                        x = grp_x,
+                        y = y_pos,
+                        label = results$stars[i],
+                        size = 5, fontface = "bold")
+  }
+}
+
+print(p3)
+
+# =====================================================
+# 11. 组合图形（方法1+方法2）
+# =====================================================
+
+combined <- plot_grid(p1, p2, ncol = 2, labels = c("A", "B"), label_size = 12)
+
+# =====================================================
+# 12. 保存图形
+# =====================================================
+
+# 方法1：直接标注p值
+ggsave(file.path(output_dir, "Figure_SAD_B19_pvalues_above_bars.pdf"), 
+       p1, width = 4, height = 8, device = cairo_pdf)
+ggsave(file.path(output_dir, "Figure_SAD_B19_pvalues_above_bars.png"), 
+       p1, width = 4, height = 8, dpi = 300, bg = "white")
+
+# 方法2：bracket标注
+ggsave(file.path(output_dir, "Figure_SAD_B19_pvalues_bracket.pdf"), 
+       p2, width = 4, height = 8, device = cairo_pdf)
+
+# 方法3：星号标注（推荐用于SCI发表）
+ggsave(file.path(output_dir, "Figure_SAD_B19_stars.pdf"), 
+       p3, width = 4, height = 8, device = cairo_pdf)
+ggsave(file.path(output_dir, "Figure_SAD_B19_stars.png"), 
+       p3, width = 4, height = 8, dpi = 300, bg = "white")
+
+# 组合图形
+ggsave(file.path(output_dir, "Figure_SAD_B19_combined.pdf"), 
+       combined, width = 12, height = 5, device = cairo_pdf)
+
+# =====================================================
+# 13. 保存统计结果
+# =====================================================
+
+write.csv(summary_stats, file.path(output_dir, "Summary_statistics.csv"), row.names = FALSE)
+write.csv(results, file.path(output_dir, "Pairwise_comparison_results.csv"), row.names = FALSE)
+
+# =====================================================
+# 14. 打印所有p值
+# =====================================================
+
+cat("\n========== All P-values (vs Control, same batch) ==========\n")
+cat("Control values:", paste(control_data, collapse = ", "), "\n")
+cat("Control mean:", mean(control_data), "\n\n")
+
+for (i in 1:nrow(results)) {
+  cat(results$group[i], "vs Control:\n")
+  cat("  Test mean:", round(results$mean_test[i], 2), "\n")
+  cat("  Mean difference:", round(results$mean_diff[i], 2), "\n")
+  cat("  p =", results$p_value[i], 
+      ifelse(results$p_value[i] < 0.05, " *", " ns"), "\n\n")
+}
+
+cat("\n========== Analysis Complete ==========\n")
+cat("Output directory:", output_dir, "\n")
+cat("\nGenerated files:\n")
+cat("  - Figure_SAD_B19_pvalues_above_bars.pdf (p值标注在柱上)\n")
+cat("  - Figure_SAD_B19_pvalues_bracket.pdf (bracket标注)\n")
+cat("  - Figure_SAD_B19_stars.pdf (星号标注，推荐)\n")
+cat("  - Figure_SAD_B19_combined.pdf (组合图)\n")
+cat("  - Summary_statistics.csv\n")
+cat("  - Pairwise_comparison_results.csv\n")
+```
+
+```{r}
+# =====================================================
+# SAD-B19感染实验 - 各组与Control比较（只保留同一批次Control）
+# Control批次: 1, 5, 6
+# 图形顺序: control, Cplx2, Car10, Slc1a2, Fxyd7, Arc
+# =====================================================
+
+# 加载必要的包
+library(ggplot2)
+library(dplyr)
+library(cowplot)
+
+# 设置输出路径（请根据实际情况修改）
+output_dir <- "I:/RV Entry Factor Paper/Figure5 and FigureS5"
+if(!dir.exists(output_dir)) {
+  dir.create(output_dir, recursive = TRUE)
+}
+
+# =====================================================
+# 1. 准备数据
+# =====================================================
+
+# 只使用同一批次的Control
+data_list <- list(
+  control = c(1, 5, 6),
+  Cplx2 = c(10, 14, 20, 12),
+  Car10 = c(9, 12, 10),
+  Slc1a2 = c(7, 12, 11, 1),
+  Fxyd7 = c(4, 11, 1),
+  Arc = c(1, 8, 0)
+)
+
+# 转换为长格式数据框
+df <- data.frame()
+for (grp in names(data_list)) {
+  df <- rbind(df, data.frame(
+    group = grp,
+    value = data_list[[grp]]
+  ))
+}
+
+# 设置因子顺序（control第一，然后按指定顺序）
+df$group <- factor(df$group, levels = c("control", "Cplx2", "Car10", "Slc1a2", "Fxyd7", "Arc"))
+
+# =====================================================
+# 2. 描述性统计
+# =====================================================
+
+summary_stats <- df %>%
+  group_by(group) %>%
+  summarise(
+    n = n(),
+    Mean = round(mean(value), 2),
+    SD = round(sd(value), 2),
+    SEM = round(sd(value)/sqrt(n()), 2),
+    Min = min(value),
+    Max = max(value)
+  )
+
+cat("\n========== Descriptive Statistics ==========\n")
+print(as.data.frame(summary_stats))
+
+# =====================================================
+# 3. 单因素ANOVA
+# =====================================================
+
+anova_result <- aov(value ~ group, data = df)
+summary_anova <- summary(anova_result)
+anova_p <- summary_anova[[1]]$`Pr(>F)`[1]
+eta_squared <- summary_anova[[1]]$`Sum Sq`[1] / sum(summary_anova[[1]]$`Sum Sq`)
+
+cat("\n========== ANOVA Results ==========\n")
+print(summary_anova)
+cat("\nEffect size (η²) =", round(eta_squared, 3), "\n")
+
+# =====================================================
+# 4. 各组与Control的t检验
+# =====================================================
+
+control_data <- data_list$control
+test_groups <- c("Cplx2", "Car10", "Slc1a2", "Fxyd7", "Arc")
+
+# 存储结果
+results <- data.frame(
+  group = test_groups,
+  mean_control = mean(control_data),
+  mean_test = NA,
+  mean_diff = NA,
+  p_value = NA,
+  p_label = NA
+)
+
+for (i in 1:length(test_groups)) {
+  grp <- test_groups[i]
+  test_data <- data_list[[grp]]
+  t_result <- t.test(test_data, control_data)
+  
+  results$mean_test[i] <- mean(test_data)
+  results$mean_diff[i] <- mean(test_data) - mean(control_data)
+  results$p_value[i] <- t_result$p.value
+  
+  # 格式化p值标签
+  if (t_result$p.value < 0.001) {
+    results$p_label[i] <- "p < 0.001"
+  } else if (t_result$p.value < 0.01) {
+    results$p_label[i] <- paste0("p = ", round(t_result$p.value, 3))
+  } else if (t_result$p.value < 0.05) {
+    results$p_label[i] <- paste0("p = ", round(t_result$p.value, 3))
+  } else {
+    results$p_label[i] <- paste0("p = ", round(t_result$p.value, 3))
+  }
+}
+
+# 按图形顺序重新排列results
+results$group <- factor(results$group, levels = c("Cplx2", "Car10", "Slc1a2", "Fxyd7", "Arc"))
+results <- results[order(results$group), ]
+
+cat("\n========== Pairwise t-test (each group vs control) ==========\n")
+print(results)
+
+# =====================================================
+# 5. SCI期刊标准主题
+# =====================================================
+
+theme_sci <- theme_classic(base_size = 11) +
+  theme(
+    axis.title = element_text(size = 11, face = "bold"),
+    axis.text = element_text(size = 9, color = "black"),
+    axis.line = element_line(color = "black", size = 0.5),
+    axis.ticks = element_line(color = "black", size = 0.5),
+    legend.position = "none",
+    plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm")
+  )
+
+# =====================================================
+# 6. SCI标准配色
+# =====================================================
+
+sci_colors <- c(
+  "control" = "#999999",  # 灰色
+  "Cplx2" = "#56B4E9",    # 蓝色
+  "Car10" = "#E69F00",    # 橙色
+  "Slc1a2" = "#009E73",   # 绿色
+  "Fxyd7" = "#F0E442",    # 黄色
+  "Arc" = "#D55E00"       # 红色
+)
+
+# =====================================================
+# 7. 计算y轴位置（为p值标注留空间）- 修复版
+# =====================================================
+
+group_max <- df %>%
+  group_by(group) %>%
+  summarise(max_val = max(value), .groups = 'drop')
+
+# 将group列转换为字符，避免因子水平问题
+group_max$group <- as.character(group_max$group)
+
+control_max <- group_max$max_val[group_max$group == "control"]
+y_max <- max(df$value)
+
+# 为每个实验组计算标注位置（按图形顺序）
+for (i in 1:nrow(results)) {
+  grp <- as.character(results$group[i])  # 确保是字符
+  test_max <- group_max$max_val[group_max$group == grp]
+  results$y_pos[i] <- max(control_max, test_max) + 1 + (i * 0.8)
+}
+
+# =====================================================
+# 8. 方法1：在每个实验组上方直接标注p值
+# =====================================================
+
+p1 <- ggplot(df, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.7, size = 2.5, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of SAD-B19 infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 6)
+
+# 添加ANOVA结果
+p1 <- p1 + annotate("text", x = 3.5, y = y_max + 5,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3),
+                                   ", η² = ", round(eta_squared, 3)),
+                    size = 3, fontface = "italic")
+
+# 在每个实验组上方标注p值
+for (i in 1:nrow(results)) {
+  grp_x <- which(levels(df$group) == results$group[i])
+  grp_y <- results$y_pos[i]
+  
+  p1 <- p1 + annotate("text",
+                      x = grp_x,
+                      y = grp_y,
+                      label = results$p_label[i],
+                      size = 3, fontface = "italic")
+}
+
+print(p1)
+
+# =====================================================
+# 9. 方法2：用bracket线连接Control和实验组，标注p值
+# =====================================================
+
+# 计算bracket的y位置
+y_positions <- seq(y_max + 1, y_max + 5, length.out = nrow(results))
+
+p2 <- ggplot(df, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.7, size = 2.5, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of SAD-B19 infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 7)
+
+# 添加ANOVA结果
+p2 <- p2 + annotate("text", x = 3.5, y = y_max + 6,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3),
+                                   ", η² = ", round(eta_squared, 3)),
+                    size = 3, fontface = "italic")
+
+# 为每个实验组添加bracket和p值
+for (i in 1:nrow(results)) {
+  grp_x <- which(levels(df$group) == results$group[i])
+  y_pos <- y_positions[i]
+  
+  # 绘制bracket线
+  p2 <- p2 + 
+    annotate("segment",
+             x = 1, xend = grp_x,
+             y = y_pos, yend = y_pos,
+             color = "black", size = 0.4) +
+    annotate("segment",
+             x = 1, xend = 1,
+             y = y_pos - 0.3, yend = y_pos,
+             color = "black", size = 0.4) +
+    annotate("segment",
+             x = grp_x, xend = grp_x,
+             y = y_pos - 0.3, yend = y_pos,
+             color = "black", size = 0.4) +
+    # 添加p值标签
+    annotate("text",
+             x = (1 + grp_x) / 2,
+             y = y_pos + 0.5,
+             label = results$p_label[i],
+             size = 2.8, fontface = "italic")
+}
+
+print(p2)
+
+# =====================================================
+# 10. 方法3：星号标注（SCI最常用）
+# =====================================================
+
+# 为每个实验组生成星号
+results$stars <- case_when(
+  results$p_value < 0.001 ~ "***",
+  results$p_value < 0.01 ~ "**",
+  results$p_value < 0.05 ~ "*",
+  TRUE ~ "ns"
+)
+
+p3 <- ggplot(df, aes(x = group, y = value, fill = group)) +
+  geom_boxplot(alpha = 0.8, outlier.shape = NA, width = 0.6, size = 0.4) +
+  geom_jitter(width = 0.15, alpha = 0.7, size = 2.5, 
+              shape = 21, color = "black", stroke = 0.3) +
+  scale_fill_manual(values = sci_colors) +
+  labs(
+    x = "", 
+    y = "Number of SAD-B19 infected neurons"
+  ) +
+  theme_sci +
+  ylim(0, y_max + 3)
+
+# 添加ANOVA结果
+p3 <- p3 + annotate("text", x = 3.5, y = y_max + 2.5,
+                    label = paste0("ANOVA: p = ", format.pval(anova_p, digits = 3)),
+                    size = 3, fontface = "italic")
+
+# 添加星号标注
+for (i in 1:nrow(results)) {
+  if (results$p_value[i] < 0.05) {
+    grp_x <- which(levels(df$group) == results$group[i])
+    test_max <- group_max$max_val[group_max$group == as.character(results$group[i])]
+    y_pos <- test_max + 0.8
+    
+    p3 <- p3 + annotate("text",
+                        x = grp_x,
+                        y = y_pos,
+                        label = results$stars[i],
+                        size = 5, fontface = "bold")
+  }
+}
+
+print(p3)
+
+# =====================================================
+# 11. 组合图形（方法1+方法2）
+# =====================================================
+
+combined <- plot_grid(p1, p2, ncol = 2, labels = c("A", "B"), label_size = 12)
+
+# =====================================================
+# 12. 保存图形
+# =====================================================
+
+# 方法1：直接标注p值
+ggsave(file.path(output_dir, "Figure_SAD_B19_pvalues_above_bars.pdf"), 
+       p1, width = 4, height = 8, device = cairo_pdf)
+ggsave(file.path(output_dir, "Figure_SAD_B19_pvalues_above_bars.png"), 
+       p1, width = 4, height = 8, dpi = 300, bg = "white")
+
+# 方法2：bracket标注
+ggsave(file.path(output_dir, "Figure_SAD_B19_pvalues_bracket.pdf"), 
+       p2, width = 4, height = 8, device = cairo_pdf)
+
+# 方法3：星号标注（推荐用于SCI发表）
+ggsave(file.path(output_dir, "Figure_SAD_B19_stars.pdf"), 
+       p3, width = 4, height = 8, device = cairo_pdf)
+ggsave(file.path(output_dir, "Figure_SAD_B19_stars.png"), 
+       p3, width = 4, height = 8, dpi = 300, bg = "white")
+
+# 组合图形
+ggsave(file.path(output_dir, "Figure_SAD_B19_combined.pdf"), 
+       combined, width = 12, height = 5, device = cairo_pdf)
+
+# =====================================================
+# 13. 保存统计结果
+# =====================================================
+
+write.csv(summary_stats, file.path(output_dir, "Summary_statistics.csv"), row.names = FALSE)
+write.csv(results, file.path(output_dir, "Pairwise_comparison_results.csv"), row.names = FALSE)
+
+# =====================================================
+# 14. 打印所有p值
+# =====================================================
+
+cat("\n========== All P-values (vs Control, same batch) ==========\n")
+cat("Control values:", paste(control_data, collapse = ", "), "\n")
+cat("Control mean:", mean(control_data), "\n\n")
+
+for (i in 1:nrow(results)) {
+  cat(results$group[i], "vs Control:\n")
+  cat("  Test mean:", round(results$mean_test[i], 2), "\n")
+  cat("  Mean difference:", round(results$mean_diff[i], 2), "\n")
+  cat("  p =", results$p_value[i], 
+      ifelse(results$p_value[i] < 0.05, " *", " ns"), "\n\n")
+}
+
+cat("\n========== Analysis Complete ==========\n")
+cat("Output directory:", output_dir, "\n")
+cat("\nGenerated files:\n")
+cat("  - Figure_SAD_B19_pvalues_above_bars.pdf (p值标注在柱上)\n")
+cat("  - Figure_SAD_B19_pvalues_bracket.pdf (bracket标注)\n")
+cat("  - Figure_SAD_B19_stars.pdf (星号标注，推荐)\n")
+cat("  - Figure_SAD_B19_combined.pdf (组合图)\n")
+cat("  - Summary_statistics.csv\n")
+cat("  - Pairwise_comparison_results.csv\n")
+```