肿瘤样本的高低风险组样本进行GSEA分析代码

以下是一份使用R语言进行GSEA分析的示例代码，供参考：

# 导入所需的R包
library(ggplot2)
library(fgsea)
library(org.Hs.eg.db)

# 读取数据和基因注释信息
data <- read.table("data.txt", header = TRUE, row.names = 1)
gene_symbols <- rownames(data)
gene_entrez_ids <- mapIds(org.Hs.eg.db, gene_symbols, "ENTREZID", "SYMBOL")

# 将数据按照基因表达水平从高到低排序
data_sorted <- data[order(rowMeans(data), decreasing = TRUE), ]

# 定义高低风险组
high_risk_samples <- c("sample1", "sample2", "sample3")
low_risk_samples <- c("sample4", "sample5", "sample6")

# 计算高低风险组的基因表达平均值
high_risk_mean <- rowMeans(data_sorted[, high_risk_samples])
low_risk_mean <- rowMeans(data_sorted[, low_risk_samples])

# 计算基因表达差异
diff_expression <- high_risk_mean - low_risk_mean

# 进行GSEA分析
gene_sets <- read.gmt("gene_sets.gmt")
fgsea_res <- fgsea(gene_sets, diff_expression, nperm = 10000)

# 绘制富集分析结果图
enrichment_plot <- ggplot(fgsea_res, aes(x = pathway, y = NES, fill = pval)) +
  geom_bar(stat = "identity", alpha = 0.8) +
  scale_fill_gradient(low = "blue", high = "red") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  labs(x = "Pathway", y = "Normalized Enrichment Score (NES)", fill = "Adjusted p-value")
enrichment_plot

其中，`data.txt`是包含肿瘤样本基因表达数据的文件，`gene_sets.gmt`是包含基因集的GMT格式文件。在上述代码中，我们首先读取并排序基因表达数据，然后定义高低风险组，并计算基因表达差异。接着，我们使用`fgsea`函数进行GSEA分析，并绘制结果图。在绘制图表时，我们使用`ggplot2`包进行可视化，将富集分析结果按照`NES`和`adjusted p-value`进行着色。