ego <- enrichGO(gene = DEG$ENTREZID, OrgDb = org.Hs.eg.db, ont = "all", pAdjustMethod = "BH", minGSSize = 1, pvalueCutoff =0.05, qvalueCutoff =0.05, readable = TRUE)barplot(ego, showCategory = 10,color = "pvalue") 输出结果的纵坐标是啥
时间: 2024-02-14 20:12:44 浏览: 23
barplot(ego, showCategory = 10, color = "pvalue")中输出结果的纵坐标是"GO terms",也就是基因本体论(Gene Ontology)的注释名称。GO terms是一种对基因和蛋白质功能进行描述的标准化分类体系,可帮助研究人员更好地理解生物系统的功能和相互作用。在这段代码中,它们被用来分析差异表达基因在GO term上的富集情况。
相关问题
<include file="$(find ego_planner)/launch/run_in_xtdrone.launch"> <arg name="drone_id" value="0"/>
这是一个ROS(机器人操作系统)中的launch文件中的一部分,其中通过include指令包含了另一个launch文件run_in_xtdrone.launch,并定义了一个参数drone_id,赋值为0。
include指令可以将其他launch文件包含进来,方便我们组合不同的功能组件。在这个例子中,通过包含run_in_xtdrone.launch,将其中定义的节点和参数等信息引入到当前的launch文件中。
参数drone_id用于指定当前机器人的ID,这个参数通常由外部系统传入,用于区分不同的机器人或设备。在运行节点时,可以通过$(arg drone_id)的方式引用这个参数,例如在命令行中输入:
```
roslaunch my_package my_launch_file.launch drone_id:=1
```
这样就可以将drone_id参数的值设为1,并传递给相关的节点使用。
how to use the enrichplot pakcage for the Gene Ontology (GO) enrichment analysis in RNA-seq analysis? please provide five case studies including their codes and steps
Enrichplot 包是一个用于可视化基因本体(GO)富集分析结果的 R 语言包。以下是五个案例研究及其代码和步骤:
1. Case 1: GO enrichment analysis of differentially expressed genes (DEGs) in breast cancer
Code:
```
library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)
# Load DEGs data
data <- read.csv("breast_cancer_DEGs.csv")
# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "BP", pvalueCutoff = .05, qvalueCutoff = .05)
# Plot the results
barplot(ego, showCategory = 10)
```
2. Case 2: GO enrichment analysis of DEGs in liver cancer
Code:
```
library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)
# Load DEGs data
data <- read.csv("liver_cancer_DEGs.csv")
# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "MF", pvalueCutoff = .05, qvalueCutoff = .05)
# Plot the results
dotplot(ego, showCategory = 10)
```
3. Case 3: GO enrichment analysis of DEGs in lung cancer
Code:
```
library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)
# Load DEGs data
data <- read.csv("lung_cancer_DEGs.csv")
# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "CC", pvalueCutoff = .05, qvalueCutoff = .05)
# Plot the results
cnetplot(ego, showCategory = 10)
```
4. Case 4: GO enrichment analysis of DEGs in prostate cancer
Code:
```
library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)
# Load DEGs data
data <- read.csv("prostate_cancer_DEGs.csv")
# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "BP", pvalueCutoff = .05, qvalueCutoff = .05)
# Plot the results
vennpie(ego, showCategory = 10)
```
5. Case 5: GO enrichment analysis of DEGs in colon cancer
Code:
```
library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)
# Load DEGs data
data <- read.csv("colon_cancer_DEGs.csv")
# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "MF", pvalueCutoff = .05, qvalueCutoff = .05)
# Plot the results
enrichMap(ego, showCategory = 10)
```