CRISPR-KO-KEGG-GO富集分析
代码如下：
三个R文件：
- Func-CRIPSR （Func_CRISPR_20250519-Huh7-RAG.R）
- Func-KEGG-GO （Func_KEGG_GO_20250520.R）
- use （use_vol_CRISPR-20250519.R）
起始文件为Mageck分析好的*.gene_summary.txt文件
如下图：
*.gene_summary.txt
内容示例如下：
Ctrl_Mock.gene_summary.txt 部分信息
切换工作目录到该系列文件所在路径
打开use文件3，source文件1和2，然后循环读取*.gene_summary.txt文件，并进行绘图
具体思路
循环读取*.gene_summary.txt文件
------CRISPR函数
-------------取前缀名vsname
-------------设置阈值P,FC
-------------提取成.CSV文件（两个文件，全部基因或差异基因）
-------------EnhancedVolcano绘制火山图（阈值函数内设定，与上述独立）
-------------创建文件夹，保存RDS，保存volcano
-------------返回此次.csv文件名（差异基因，只含up down）
------KEGG_GO函数
-------------读取CSV文件,获取up基因（CRISPR阴性筛选，其中只up有意义）
-------------提取文件名前缀作为文件夹名（注：CRISPR函数已经创建）
-------------KEGG分析，多个阈值测试(0.01, 0.05, 0.1, 0.2)
-------------KEGG绘图（bar,dot,emap）只用10个通路，保存PDF
-------------GO富集分析，p值测试(0.01, 0.05, 0.1, 0.2)，达到10个通路停止，如无，选用0.2
-------------绘图只用10个通路(BP,MF,CC三个dotplot)，保存PDF
部分结果如下：
KEGG_dotplot
KEGG_barplot
KEGG_emap
GO-BP
GO-MF
GO-CC
全部具体代码如下
- CRISPR绘制火山图，并提取up down基因代码，在如下文件中
Func_CRISPR_20250519-Huh7-RAG.R
library(tidyverse)
library(ggrepel)
set.seed(42)
require(EnhancedVolcano)
Vol_CRISPR <- function(file_path, pC, FCC){
# 指定CRISPR-MAGEECK分析的Counts 表格数据文件路径
# file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/Ctrl_Mock.gene_summary.txt"
# file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R2_Mock.gene_summary.txt"
# file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R5_Mock.gene_summary.txt"
# file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R2_Ctrl.gene_summary.txt"
# file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R5_Ctrl.gene_summary.txt"
# file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R5_R2.gene_summary.txt"
# 读取文件
data <- read.table(file_path,header=T,na.strings = c("NA"))
# 获取文件名
get_first_part <- function(file_path) {
# 获取文件名（含扩展名）
file_name <- basename(file_path)
# 使用 strsplit 按第一个点分割
parts <- strsplit(file_name, "\\.", fixed = FALSE)[[1]]
# 返回第一个部分
return(parts[1])
}
vsname <- get_first_part(file_path) # 输出: "file"
vsname
# 设置参数cutoff
# FC_cutoff = 1.333
# P_cutoff = 0.01
FC_cutoff = 1
P_cutoff = 0.01
data[which(abs(data$pos.lfc) <= FC_cutoff | data$pos.score >= P_cutoff),'sig'] <- 'none'
data[which(data$pos.lfc >= FC_cutoff & data$pos.score < P_cutoff),'sig'] <- 'up'
data[which(data$pos.lfc <= -FC_cutoff & data$pos.score < P_cutoff),'sig'] <- 'down'
#输出选择的差异基因总表
data_select <- subset(data, sig %in% c('up', 'down'))
dim(data_select)
#保存为csv--差异基因排序
TwoGroupFileName = paste0(vsname,"/",vsname,".csv")
# data_selecto <- data_select[order(data_select$padj, data_select$log2FoldChange, decreasing = c(FALSE, TRUE)), ]#排序
write.csv(data_select, file = TwoGroupFileName, row.names = TRUE)
#保存为csv--全部基因排序
TwoGroupFileName_all = paste0(vsname,"/",vsname,"_all.csv")
write.csv(data, file = TwoGroupFileName_all, row.names = TRUE)
data$id
vsname
title = paste0(vsname,' (CRISPR Results)')
title
subtitle = paste0("Differential expression")
subtitle
# pCutoff = 0.001,## pvalue
# FCcutoff = 1.333,## FC cutoff
##火山图， 里面确定pCut和FCcut
p_evo <- EnhancedVolcano(data,
lab = data$id,
x = "pos.lfc",
# y = "padj",
y = "pos.score",
#selectLab = rownames(lrt)[1:4],
xlab = bquote(~Log[2]~ "Fold Change"),
ylab = bquote(~-Log[10] ~ italic(p-value)),
# pCutoff = 0.001,## pvalue
# FCcutoff = 1,## FC cutoff
pCutoff = pC,## pvalue
FCcutoff = FCC,## FC cutoff
# xlim = c(-5,5),
# ylim = c(0,7.5),
xlim = c(-6,6),
ylim = c(0,8),
pointSize = 3.5,
labSize =3.5,
title = title,
subtitle = subtitle,
caption = 'FC cutoff, 1.333; p-value cutoff, 10e-4',
# legendPosition = "right",
legendLabSize = 14,
# col = c('grey30', 'forestgreen', 'royalblue', 'red2'),
colAlpha = 0.8,
drawConnectors = TRUE,
lengthConnectors = unit(0.05, "inches"),
# hline = c(10e-8),
widthConnectors = 0.5,
max.overlaps = 50,
maxoverlapsConnectors=50
)
p_evo
# 创建文件夹，两两分析
dir.create(vsname)
# 创建RDS文件名
f_name = paste0(vsname,"/",vsname,".RDS")
f_name
ggsave(paste0(vsname,"/",vsname,"-volcano.png"),p_evo,height = 10,width = 8)
# yuzhi = 0.01
# # 定义颜色
# labelcolor = ifelse(data$pos.score<yuzhi,"red1","black1")
# # 定义标签
# data$sign <- ifelse(data$pos.score < yuzhi ,data$id,NA)
# data$sign
#geom_text(aes(label = sign), size = 3)
# # 加颜色加大小 加标签
# ggplot(data=data, mapping=aes( x=pos.rank, y=-log(pos.score)))+
# geom_point(aes(col=labelcolor,size=-log(pos.score)))+
# geom_label_repel(aes(pos.rank,-log(pos.score),
# label=sign), max.overlaps=100,fontface="bold", color="red",
# box.padding=unit(0.35, "lines"), point.padding=unit(0.5, "lines"),
# segment.colour = "grey50")+ theme_classic(base_size = 16)+
# scale_color_manual(values=c("black", "red"))+
# # ggtitle("MEM2nd_Mock")
# # ggtitle("Ctrl_Mock")
# # ggtitle("R5_Ctrl")
# ggtitle("R5_Mock")
#
#
#
# labelcolor = ifelse(data$pos.score<0.0001,"red1","black1")
# # 加颜色家大小（原始冒泡）
# ggplot(data=data, mapping=aes( x=id, y=-log(pos.score)))+
# geom_point(aes(col=labelcolor,size=-log(pos.score)))+
# scale_color_manual(values=c("grey", "red"))+
# # ggtitle("R5_Ctrl")+
# # ggtitle("Top10_Mock")+
# ggtitle("R5_Mock")+
# geom_label_repel(aes(id,-log(pos.score),
# label=sign), max.overlaps=100,fontface="bold", color="red",
# box.padding=unit(0.35, "lines"), point.padding=unit(0.5, "lines"),
# segment.colour = "grey50")+ theme_classic(base_size = 16)+
# theme(axis.text.x = element_blank())
TwoGroupFileName
return(TwoGroupFileName)
}
- KEGG_GO富集分析绘图代码，在如下文件中
Func_KEGG_GO_20250520.R
# 加载包
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(ggplot2)
library(dplyr)
# TwoGroupFileName_all
# id num neg.score neg.p.value neg.fdr neg.rank neg.goodsgrna neg.lfc pos.score pos.p.value pos.fdr pos.rank pos.goodsgrna pos.lfc sig
# 1 AVPR1B 3 0.00017667 0.00062288 0.858213 1 2 -4.013100 0.71203 0.72674 0.993890 1575 1 -4.013100 none
# 2 ABCC10 5 0.00034953 0.00157350 0.858213 2 4 -9.956200 0.86124 0.88344 0.994943 1720 1 -9.956200 none
# 3 CTSK 5 0.00047611 0.00208880 0.858213 3 4 -9.761500 0.28164 0.48870 0.991282 928 1 -9.761500 none
# 4 TSHR 3 0.00052994 0.00170350 0.858213 4 1 -0.039356 0.32336 0.46631 0.990656 1000 1 -0.039356 none
# 5 ANXA5 4 0.00071592 0.00268410 0.858213 5 3 -10.911000 0.94272 0.94320 0.994943 1872 0 -10.911000 none
# 6 CCR1 4 0.00081369 0.00305430 0.858213 6 3 -10.909000 0.25278 0.43110 0.982588 871 1 -10.909000 none
KEGG_GO <- function(file_path){
# file_path 为全长 FUnc_CRISPR分析来的CSV 所有Up Down基因
# 处理文件名，后续分析保存文件用
# 获取文件名,从gene_summary,获取文件对比前缀，后续分析采用仅有up down的CSV
#test
# file_path <- "R2_Ctrl/R2_Ctrl.csv"
file_path <- filepath_csv_updown
get_first_part <- function(file_path) {
# 获取文件名（含扩展名）
file_name <- basename(file_path)
# 使用 strsplit 按第一个点分割
parts <- strsplit(file_name, "\\.", fixed = FALSE)[[1]]
# 返回第一个部分
return(parts[1])
}
vsname <- get_first_part(file_path) # 输出: "file"
vsname
TwoGroupFileName = paste0(vsname,"/",vsname,".csv")
TwoGroupFileName
get_first_part <- function(x) {
parts <- strsplit(x, "/", fixed = TRUE)[[1]] # 分割字符串
return(parts[1]) # 返回第一部分
}
go_name <- get_first_part(TwoGroupFileName)
go_name
#[1] "R2_Ctrl"
# 读取数据
gene_list <- read.csv(TwoGroupFileName, header = TRUE, stringsAsFactors = FALSE) %>% dplyr::select(id, sig)
colnames(gene_list) <- c("gene_symbol", "regulation")
# 基因ID转换
gene_entrez <- bitr(gene_list$gene_symbol, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)
gene_list <- merge(gene_list, gene_entrez, by.x = "gene_symbol", by.y = "SYMBOL")
gene_list
# > gene_list
# gene_symbol regulation ENTREZID
# 1 A2M none 2
# 2 AADAC none 13
# 3 AATK none 9625
# 4 ABAT none 18
up_genes <- gene_list
# 分离上下调基因
up_genes <- gene_list$ENTREZID[gene_list$regulation == "up"]
# down_genes <- gene_list$ENTREZID[gene_list$regulation == "down"]
# up_genes <- gene_list$ENTREZID
# KEGG富集分析------------------------------------------------------------------------------------------------------------------------
# 测试多个阈值
thresholds <- c(0.01, 0.05, 0.1, 0.2)
for (thr in thresholds) {
# KEGG富集分析
kegg_up <- enrichKEGG(gene = up_genes, organism = "hsa", pAdjustMethod = "BH", qvalueCutoff = thr, pvalueCutoff = thr)
print(paste("阈值 =", thr, "| 富集通路数 =", nrow(kegg_up)))
if (nrow(kegg_up) >= 10){
# KEGG条形图---------------------------------------------------------------------
pKEGG_barplot <- barplot(kegg_up,
showCategory = 10, # 显示前10个通路
font.size = 12, # 字体大小
title = "KEGG Pathway Enrichment")
pKEGG_barplot
# 保存KEGG条形图为PDF
timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S")
# timestamp
# [1] "20250527_103734"
ggsave(paste0(go_name,"/",go_name,"-KEGG_up_barplot-", timestamp,".pdf"),
plot = pKEGG_barplot,
width = 6,
height = 6,
dpi = 300)
# KEGG富集网络图-----------------------------------------------------------------
# 计算通路相似度矩阵（关键步骤）
kegg_up_sim <- pairwise_termsim(kegg_up)
# 可视化富集网络
pKEGG_emap <- emapplot(kegg_up_sim, showCategory = 20) +
ggtitle("KEGG Pathway Enrichment Map (Upregulated Genes)") +
theme(plot.title = element_text(hjust = 0.5)) # 标题居中
pKEGG_emap
# 保存KEGG富集网络图为PDF
ggsave(paste0(go_name,"/",go_name,"-KEGG_up_emap-", timestamp,".pdf"),
plot = pKEGG_emap,
width = 12,
height = 8,
dpi = 300)
# KEGG气泡图---------------------------------------------------------------------
pKEGG_dotplot <- dotplot(kegg_up, showCategory = 10) +
ggtitle("KEGG Pathway Enrichment (Upregulated Genes)") +
theme(plot.title = element_text(hjust = 0.5))
pKEGG_dotplot
# 保存KEGG气泡图为PDF
ggsave(paste0(go_name,"/",go_name,"-KEGG_up_dotplot-", timestamp,".pdf"),
plot = pKEGG_dotplot,
width = 6,
height = 6,
dpi = 300)
# 满足10条跳出循环
break
}
}
# GO富集分析------------------------------------------------------------------------------------------------------------------------
# 可视化----------------------------------------------------------------------------------------------------------------------------
# barplot(kegg_up, showCategory = 10)
# dotplot(go_up_BP, showCategory = 10)
# 总共6张图
# 1.KEGGbarplot
# 2.KEGGemap
# 3.KEGGdotplot
# 4.GO_BP_dotplot
# 4.GO_MF_dotplot
# 4.GO_CC_dotplot
# 绘制并保存GO气泡图外观 BP，MF，CC
# GO_BP------------------------------------------------------------------------
# 定义要测试的p值阈值
thresholds <- c(0.01, 0.05, 0.1, 0.2)
selected_threshold <- NULL # 存储最终选择的阈值
# 循环遍历各个阈值
for (thr in thresholds) {
# 执行GO富集分析 (BP类别)
go_up_BP <- enrichGO(
gene = up_genes, # 你的差异表达基因列表
OrgDb = org.Hs.eg.db, # 物种注释数据库
ont = "BP", # 只分析生物过程(BP)
pAdjustMethod = "BH", # p值校正方法
pvalueCutoff = thr, # 当前测试的p值阈值
qvalueCutoff = thr # q值阈值
)
# 检查富集结果是否有至少10个通路
if (!is.null(go_up_BP) && nrow(go_up_BP) >= 10) {
selected_threshold <- thr
print(paste("使用阈值", thr, "找到", nrow(go_up_BP), "个富集通路"))
# GO_BP 绘图
p1 <- dotplot(go_up_BP, showCategory = min(10, nrow(go_up_BP))) +
labs(title = "GO Biological Process Enrichment",
x = "Gene Ratio",
) +
theme(axis.text.y = element_text(size = 10, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
# p1
# 保存GO_MF气泡图为PDF
ggsave(paste0(go_name,"/",go_name,"-GO_BP_dotplot-", timestamp,".pdf"),
plot = p1,
width = 6,
height = 6,
dpi = 300)
break # 满足条件时跳出循环
} else {
print(paste("阈值", thr, "找到", ifelse(is.null(go_up_BP), 0, nrow(go_up_BP)), "个富集通路，继续尝试下一个阈值"))
}
}
# 如果所有阈值都不满足条件，则使用最后一个阈值
if (is.null(selected_threshold)) {
selected_threshold <- tail(thresholds, 1)
print(paste("所有阈值均未找到足够的富集通路，使用最大阈值", selected_threshold))
# 再次执行富集分析，确保使用最后一个阈值
go_up_BP <- enrichGO(
gene = up_genes,
OrgDb = org.Hs.eg.db,
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = selected_threshold,
qvalueCutoff = selected_threshold
)
# GO_BP 绘图
p1 <- dotplot(go_up_BP, showCategory = min(10, nrow(go_up_BP))) +
labs(title = "GO Biological Process Enrichment",
x = "Gene Ratio",
) +
theme(axis.text.y = element_text(size = 10, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
# p1
# 保存GO_MF气泡图为PDF
ggsave(paste0(go_name,"/",go_name,"-GO_BP_dotplot-", timestamp,".pdf"),
plot = p1,
width = 6,
height = 6,
dpi = 300)
break # 满足条件时跳出循环
}
# GO_MF------------------------------------------------------------------------
# 定义要测试的p值阈值
{
thresholds <- c(0.01, 0.05, 0.1, 0.2)
selected_threshold <- NULL # 存储最终选择的阈值
# 循环遍历各个阈值
for (thr in thresholds) {
# 执行GO富集分析 (BP类别)
go_up_BP <- enrichGO(
gene = up_genes, # 你的差异表达基因列表
OrgDb = org.Hs.eg.db, # 物种注释数据库
ont = "MF", # 只分析生物过程(BP)
pAdjustMethod = "BH", # p值校正方法
pvalueCutoff = thr, # 当前测试的p值阈值
qvalueCutoff = thr # q值阈值
)
# 检查富集结果是否有至少10个通路
if (!is.null(go_up_MF) && nrow(go_up_MF) >= 10) {
selected_threshold <- thr
print(paste("使用阈值", thr, "找到", nrow(go_up_BP), "个富集通路"))
# GO_BP 绘图
p1 <- dotplot(go_up_MF, showCategory = min(10, nrow(go_up_MF))) +
labs(title = "GO Molecular Function Enrichment",
x = "Gene Ratio",
) +
theme(axis.text.y = element_text(size = 10, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
# p1
# 保存GO_MF气泡图为PDF
ggsave(paste0(go_name,"/",go_name,"-GO_MF_dotplot-", timestamp,".pdf"),
plot = p1,
width = 6,
height = 6,
dpi = 300)
break # 满足条件时跳出循环
} else {
print(paste("阈值", thr, "找到", ifelse(is.null(go_up_MF), 0, nrow(go_up_MF)), "个富集通路，继续尝试下一个阈值"))
}
}
# 如果所有阈值都不满足条件，则使用最后一个阈值
if (is.null(selected_threshold)) {
selected_threshold <- tail(thresholds, 1)
print(paste("所有阈值均未找到足够的富集通路，使用最大阈值", selected_threshold))
# 再次执行富集分析，确保使用最后一个阈值
go_up_MF <- enrichGO(
gene = up_genes,
OrgDb = org.Hs.eg.db,
ont = "MF",
pAdjustMethod = "BH",
pvalueCutoff = selected_threshold,
qvalueCutoff = selected_threshold
)
# GO_MF
p2 <- dotplot(go_up_MF,
showCategory = 10, # 显示前15个功能
# color = "p.adjust", # 颜色映射为校正p值
# size = "Count"
) + # 点大小映射为基因数量
labs(title = "GO Molecular Function Enrichment",
x = "Gene Ratio", # x轴为基因比例
)+
theme(axis.text.y = element_text(size = 10, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
p2
# 保存GO_MF气泡图为PDF
ggsave(paste0(go_name,"/",go_name,"-GO_MF_dotplot-", timestamp,".pdf"),
plot = p2,
width = 6,
height = 6,
dpi = 300)
}
}
# GO_CC------------------------------------------------------------------------
{
# 定义要测试的p值阈值
thresholds <- c(0.01, 0.05, 0.1, 0.2)
selected_threshold <- NULL # 存储最终选择的阈值
# 循环遍历各个阈值
for (thr in thresholds) {
# 执行GO富集分析 (BP类别)
go_up_CC <- enrichGO(
gene = up_genes, # 你的差异表达基因列表
OrgDb = org.Hs.eg.db, # 物种注释数据库
ont = "CC", # 只分析生物过程(BP)
pAdjustMethod = "BH", # p值校正方法
pvalueCutoff = thr, # 当前测试的p值阈值
qvalueCutoff = thr # q值阈值
)
# 检查富集结果是否有至少10个通路
if (!is.null(go_up_CC) && nrow(go_up_CC) >= 10) {
selected_threshold <- thr
print(paste("使用阈值", thr, "找到", nrow(go_up_CC), "个富集通路"))
# GO_BP 绘图
p1 <- dotplot(go_up_CC, showCategory = min(10, nrow(go_up_CC))) +
labs(title = "GO Cellular Component Enrichment",
x = "Gene Ratio",
) +
theme(axis.text.y = element_text(size = 10, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
# p1
# 保存GO_MF气泡图为PDF
ggsave(paste0(go_name,"/",go_name,"-GO_CC_dotplot-", timestamp,".pdf"),
plot = p1,
width = 6,
height = 6,
dpi = 300)
break # 满足条件时跳出循环
} else {
print(paste("阈值", thr, "找到", ifelse(is.null(go_up_CC), 0, nrow(go_up_CC)), "个富集通路，继续尝试下一个阈值"))
}
}
# 如果所有阈值都不满足条件，则使用最后一个阈值
if (is.null(selected_threshold)) {
selected_threshold <- tail(thresholds, 1)
print(paste("所有阈值均未找到足够的富集通路，使用最大阈值", selected_threshold))
# 再次执行富集分析，确保使用最后一个阈值
go_up_CC <- enrichGO(
gene = up_genes,
OrgDb = org.Hs.eg.db,
ont = "CC",
pAdjustMethod = "BH",
pvalueCutoff = selected_threshold,
qvalueCutoff = selected_threshold
)
# GO_cc
p3 <- dotplot(go_up_CC,
showCategory = 10, # 显示前10个功能
# color = "p.adjust", # 颜色映射为校正p值
# size = "Count"
) + # 点大小映射为基因数量
labs(title = "GO Cellular Component Enrichment",
x = "Gene Ratio", # x轴为基因比例
)+
theme(axis.text.y = element_text(size = 10, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))
p3
# 保存GO_CC气泡图为PDF
ggsave(paste0(go_name,"/",go_name,"-GO_CC_dotplot-", timestamp,".pdf"),
plot = p3,
width = 6,
height = 6,
dpi = 300)
}
}
}
- 导入前两个R文件，并调用函数代码，在如下文件中
use_vol_CRISPR-20250519.R
source("./Func_CRISPR_20250519-Huh7-RAG.R")
source("./Func_KEGG_GO_20250520.R")
# 指定CRISPR-MAGEECK分析的Counts 表格数据文件路径
file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/Ctrl_Mock.gene_summary.txt"
file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R2_Mock.gene_summary.txt"
file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R5_Mock.gene_summary.txt"
file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R2_Ctrl.gene_summary.txt"
file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R5_Ctrl.gene_summary.txt"
file_path <- "E:/Coding/R_gzlab_docu/CrisprNGS/CRISPR-AQY/20250515/fenxi/R5_R2.gene_summary.txt"
{
# Vol_CRISPR <- function(file_path, pC, FCC)
filepath_csv_updown <- Vol_CRISPR(file_path,0.01,1)
filepath_csv_updown
KEGG_GO(filepath_csv_updown)
}
# filepath_csv_updown <- "R2_Ctrl/R2_Ctrl.csv"
# filepath_csv_updown
# 获取所有以.gene_summary.txt结尾的文件路径
file_paths <- list.files(
path = "./", # 目标文件夹
pattern = "\\.gene_summary\\.txt$", # 正则表达式匹配
full.names = TRUE # 返回全路径
)
for (file_path in file_paths){
filepath_csv_updown <- Vol_CRISPR(file_path,0.01,1)
KEGG_GO(filepath_csv_updown)
}
