ggplot2可视化

library(ggplot2)
# 创建示例数据
data <- data.frame(
    gene = rep(c("GeneA", "GeneB", "GeneC"), each = 10),
    expression = c(rnorm(10, 10, 2), rnorm(10, 15, 3), rnorm(10, 8, 1)),
    group = rep(c("Control", "Treatment"), each = 5, times = 3)
)
# 散点图
p1 <- ggplot(data, aes(x = group, y = expression, color = group)) +
    geom_point(position = position_jitter(width = 0.2), size = 3) +
    geom_boxplot(alpha = 0.3, outlier.shape = NA) +
    facet_wrap(~gene) +
    labs(title = "Gene Expression Comparison",
         x = "Condition",
         y = "Expression Level") +
    theme_minimal()
print(p1)
# 热图（使用pheatmap包）
library(pheatmap)
expr_matrix <- matrix(rnorm(100), nrow = 10)
rownames(expr_matrix) <- paste0("Gene", 1:10)
colnames(expr_matrix) <- paste0("Sample", 1:10)
pheatmap(expr_matrix, 
         scale = "row",
         clustering_method = "ward.D2",
         color = colorRampPalette(c("navy", "white", "firebrick"))(50))
# 火山图（差异表达结果）
de_results <- data.frame(
    gene = paste0("Gene", 1:1000),
    log2FC = rnorm(1000, 0, 2),
    pvalue = runif(1000)
)
de_results$padj <- p.adjust(de_results$pvalue, method = "BH")
ggplot(de_results, aes(x = log2FC, y = -log10(padj))) +
    geom_point(aes(color = abs(log2FC) > 1 & padj < 0.05), alpha = 0.5) +
    scale_color_manual(values = c("grey", "red")) +
    geom_vline(xintercept = c(-1, 1), linetype = "dashed") +
    geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adjusted p-value") +
    theme_bw()