传统生信分析方法之差异分析和基因富集分析

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Step 0: 安装和加载分析过程中需要的 R 包 & 准备数据

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[ ]

library(edgeR) # 差异表达分析

library(data.table) # 高效处理大规模数据集

library(dplyr) # 数据处理和操作

library(ggplot2) # 创建数据可视化图表

library(pheatmap) # 绘制热图

library(biomaRt) # 生物信息学数据访问和查询的工具包

library(clusterProfiler) # GO或KEGG富集分析使用

library(org.Hs.eg.db) # 转换基因ID使用

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我们根据微信文章里的说明下载了头颈癌（Head and Neck Cancer, HNSC） RNA-seq 数据: TCGA-HNSC.htseq_counts.tsv.gz

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[2]

expr <- fread("TCGA-HNSC.htseq_counts.tsv.gz", header = TRUE, sep = "\t", data.table = F)

gene_id <- fread("gencode.v22.annotation.gene.probeMap", header = TRUE, sep = "\t", data.table = F)

gene_id <- gene_id[ , c(1, 2)]

expr <- merge(gene_id, expr, by.y = "Ensembl_ID", by.x = "id" )

expr <- distinct(expr,gene, .keep_all = T) # 去重（如果存在重复行，直接保留第一行）

rownames(expr) <- expr$gene # 把 gene name 转换为行名

expr <- expr[,-c(1,2)] # 删除 Ensembl_ID 和 gene 列

dim(expr)

head(expr)

58387
546

A data.frame: 6 × 546
	TCGA-BB-4224-01A	TCGA-H7-7774-01A	TCGA-CV-6943-01A	TCGA-CN-5374-01A	TCGA-CQ-6227-01A	TCGA-CV-6959-01A	TCGA-F7-A61V-01A	TCGA-CV-7413-01A	TCGA-CV-7247-01A	TCGA-CR-5249-01A	⋯	TCGA-CV-6960-11A	TCGA-CV-A464-01A	TCGA-C9-A47Z-01A	TCGA-CN-6010-01A	TCGA-WA-A7GZ-11A	TCGA-CV-7235-01A	TCGA-CX-7086-01A	TCGA-CV-6935-11A	TCGA-P3-A6SW-01A	TCGA-HD-A6HZ-01A
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
TSPAN6	11.127994	11.420487	11.391781	11.027215	13.311748	10.173677	10.950556	10.915132	10.059344	11.310045	⋯	11.706064	9.861087	10.738092	11.115694	11.309476	8.945444	11.421013	13.052059	9.590587	9.851749
TNMD	1.584963	0.000000	0.000000	1.000000	3.321928	2.807355	1.000000	1.000000	1.000000	0.000000	⋯	0.000000	1.584963	0.000000	0.000000	5.392317	0.000000	0.000000	2.000000	1.000000	0.000000
DPM1	10.650154	10.724514	10.688250	11.188589	11.561288	11.434107	10.783817	10.704768	10.782179	10.309476	⋯	10.439831	11.187971	10.912141	10.621136	10.194757	9.932215	10.931476	10.624795	10.616549	10.209453
SCYL3	10.055282	9.651052	9.842350	10.332037	9.071462	9.044394	8.915879	9.392317	9.231221	10.059344	⋯	9.250298	8.885696	8.682995	9.736402	9.124121	8.558421	9.971544	9.677720	9.278449	9.247928
C1orf112	10.239599	8.312883	8.654636	10.111136	8.845490	9.546894	8.375039	8.897845	9.773139	9.893302	⋯	7.693487	8.596190	7.721099	9.511753	7.000000	8.507795	10.108524	8.139551	9.209453	8.430453
FGR	8.005625	8.370687	10.584963	9.321928	8.503826	9.824959	7.209453	8.900867	7.679480	9.853310	⋯	8.252665	8.375039	7.076816	9.105909	7.375039	8.933691	7.748193	8.714246	8.854868	9.854868

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Step 1: 样本分组

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TCGA 数据根据 Ensembl_ID 就可以进行分组：Ensembl_ID的14和15位置元素可以代表样本类型。

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[3]

sample_type <- substring(colnames(expr), 14, 15)

table(sample_type)

sample_type
 01  06  11 
500   2  44

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01 到 09 表示不同类型的肿瘤样本，而 10 到 19 表示不同类型的正常样本。

01: 原发性实体瘤; 11: 实体正常组织; 06: 表示转移。

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[4]

tumor <- expr[, as.numeric(sample_type) < 10]

normal <- expr[, as.numeric(sample_type) >= 10]

expr <- cbind(tumor, normal)

head(expr)

A data.frame: 6 × 546
	TCGA-BB-4224-01A	TCGA-H7-7774-01A	TCGA-CV-6943-01A	TCGA-CN-5374-01A	TCGA-CQ-6227-01A	TCGA-CV-6959-01A	TCGA-F7-A61V-01A	TCGA-CV-7413-01A	TCGA-CV-7247-01A	TCGA-CR-5249-01A	⋯	TCGA-HD-8635-11A	TCGA-CV-7424-11A	TCGA-CV-6936-11A	TCGA-CV-6938-11A	TCGA-CV-7238-11A	TCGA-CV-7177-11A	TCGA-CV-6962-11A	TCGA-CV-6960-11A	TCGA-WA-A7GZ-11A	TCGA-CV-6935-11A
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
TSPAN6	11.127994	11.420487	11.391781	11.027215	13.311748	10.173677	10.950556	10.915132	10.059344	11.310045	⋯	10.695228	13.397808	11.370687	11.244958	11.833681	14.100662	13.527721	11.706064	11.309476	13.052059
TNMD	1.584963	0.000000	0.000000	1.000000	3.321928	2.807355	1.000000	1.000000	1.000000	0.000000	⋯	2.807355	3.321928	4.643856	6.658211	6.918863	1.000000	2.321928	0.000000	5.392317	2.000000
DPM1	10.650154	10.724514	10.688250	11.188589	11.561288	11.434107	10.783817	10.704768	10.782179	10.309476	⋯	10.283088	10.622967	10.778898	10.812177	10.412570	10.812979	10.665336	10.439831	10.194757	10.624795
SCYL3	10.055282	9.651052	9.842350	10.332037	9.071462	9.044394	8.915879	9.392317	9.231221	10.059344	⋯	9.348728	10.284246	8.710806	10.148477	9.233620	9.933691	9.939579	9.250298	9.124121	9.677720
C1orf112	10.239599	8.312883	8.654636	10.111136	8.845490	9.546894	8.375039	8.897845	9.773139	9.893302	⋯	8.049849	8.321928	7.954196	8.434628	7.139551	8.499846	8.228819	7.693487	7.000000	8.139551
FGR	8.005625	8.370687	10.584963	9.321928	8.503826	9.824959	7.209453	8.900867	7.679480	9.853310	⋯	8.569856	8.459432	9.764872	9.539159	8.661778	7.507795	8.139551	8.252665	7.375039	8.714246

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[5]

groups <- c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal)))

groups <- factor(groups, levels = c("normal", "tumor"))

table(groups)

groups
normal  tumor 
    44    502

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在 UCSC Xena 下载的数据是 log2(count+1)，因此需要进行处理：

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[6]

counts <- 2^(expr) - 1

counts <- apply(counts, 2, as.integer)

rownames(counts) <- rownames(expr)

head(counts)

A matrix: 6 × 546 of type int
	TCGA-BB-4224-01A	TCGA-H7-7774-01A	TCGA-CV-6943-01A	TCGA-CN-5374-01A	TCGA-CQ-6227-01A	TCGA-CV-6959-01A	TCGA-F7-A61V-01A	TCGA-CV-7413-01A	TCGA-CV-7247-01A	TCGA-CR-5249-01A	⋯	TCGA-HD-8635-11A	TCGA-CV-7424-11A	TCGA-CV-6936-11A	TCGA-CV-6938-11A	TCGA-CV-7238-11A	TCGA-CV-7177-11A	TCGA-CV-6962-11A	TCGA-CV-6960-11A	TCGA-WA-A7GZ-11A	TCGA-CV-6935-11A
TSPAN6	2236	2739	2685	2085	10166	1153	1977	1930	1066	2538	⋯	1657	10792	2646	2425	3648	17567	11808	3339	2537	8492
TNMD	2	0	0	1	8	6	1	1	1	0	⋯	6	8	23	100	120	1	3	0	41	3
DPM1	1606	1691	1648	2333	3020	2766	1762	1668	1759	1268	⋯	1245	1576	1755	1797	1362	1798	1623	1387	1171	1578
SCYL3	1062	802	916	1288	536	527	482	670	600	1066	⋯	650	1246	418	1134	600	976	980	608	556	817
C1orf112	1208	316	402	1105	459	747	331	475	873	950	⋯	264	318	246	345	140	361	299	206	127	281
FGR	256	329	1535	638	362	906	147	476	204	924	⋯	378	350	869	742	404	181	281	303	164	419

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Step 2: 构建DGEList对象，用于存储基因表达数据和分组信息

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[7]

y <- DGEList(counts = counts, group = groups)

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Step 3: 过滤，删除低表达基因

根据每个基因在每个样本中的 CPM（Counts Per Million）值去除低表达基因:

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[8]

keep <- rowSums(cpm(y) > 1) >= 2

table(keep)

keep
FALSE  TRUE 
33857 24530

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去除低表达基因原因：：

低表达没有生物学意义

去除低表达数据可以对数据中均值-方差关系有更精确的估计

减少了观察差异表达下游分析中的运算量

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Step 4: 从 DGEList 对象中筛选出符合条件的基因

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y <- y[keep, , keep.lib.sizes = FALSE]

y$samples$lib.size <- colSums(y$counts)

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Step 5: 将Raw counts数据标准化为TMM

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y <- calcNormFactors(y)

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# 查看标准化的样本信息

head(y$samples)

A data.frame: 6 × 3
	group	lib.size	norm.factors
	<fct>	<dbl>	<dbl>
TCGA-BB-4224-01A	tumor	52284177	1.2654022
TCGA-H7-7774-01A	tumor	73130315	0.6582711
TCGA-CV-6943-01A	tumor	88702345	0.9005872
TCGA-CN-5374-01A	tumor	47773869	1.2144472
TCGA-CQ-6227-01A	tumor	77174766	1.0090867
TCGA-CV-6959-01A	tumor	80943233	0.9042725

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TMM（Trimmed Mean of M values）是一种用于规范化 RNA-seq 数据的方法，其原理基于以下步骤：

M值计算：对于每个基因，首先计算每个样本的基因表达值的对数比（log-ratio）。这个对数比被称为 M 值，表示样本间的相对差异。

过滤极端值：对于每个基因，计算 M 值的中位数。然后，根据中位数的绝对偏差，过滤掉那些具有极端 M 值的基因。这一步骤的目的是去除可能受到异常值影响的基因，以确保规范化是稳健的。

选择参考样本：从所有样本中选择一个参考样本，通常是在 M 值的中位数最小的样本。该样本被认为具有最接近平均表达水平的样本。

规范化因子计算：计算每个样本相对于参考样本的规范化因子。规范化因子是使得每个样本的 M 值的均值等于参考样本的 M 值均值的缩放因子。

应用规范化因子：将计算得到的规范化因子应用于每个样本的原始计数矩阵，以进行规范化。

TMM 规范化的原理在于，通过选择一个参考样本并计算规范化因子，可以使不同样本之间的 M 值分布趋向于均匀，从而减小了样本之间的技术偏差，使得数据更具可比性。

注意：归一化并不会直接在counts数值上修改，而是归一化系数会被自动存在 ysamplesnorm.factors

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Step 6: 创建差异表达分析的设计矩阵

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[12]

design <- model.matrix(~0+factor(groups))

rownames(design) <- colnames(y)

colnames(design) <- levels(factor(groups))

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Step 7: 估计数据的离散度

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[13]

y <- estimateGLMCommonDisp(y, design)

y <- estimateGLMTrendedDisp(y, design)

y <- estimateGLMTagwiseDisp(y, design)

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共同离散度（common dispersion）是用于描述数据中基因之间的共同变异性的参数。这一步骤有助于建立差异表达分析的模型，考虑到共同的变异性。

趋势离散度（trended dispersion）描述了数据中与基因的表达水平相关的变异性，可能因表达水平的不同而变化。这一步骤有助于更精确地建模差异表达。

标签特异的离散度（tagwise dispersion）用于考虑数据中不同基因的离散度可能不同的情况，以更好地建模不同基因之间的变异性。

不同的离散度估计方法允许更好地考虑数据的特性，提高了差异表达分析的准确性。

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[14]

names(y)

'counts'
'samples'
'common.dispersion'
'AveLogCPM'
'trend.method'
'trended.dispersion'
'span'
'prior.df'
'tagwise.dispersion'

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Step 8: 在估计的模型基础上进行广义线性模型 (GLM) 拟合

使用EdgeR时,官方建议bulk RNA-seq选择quasi-likelihood(QL) F-test tests，scRNA-seq 或是没有重复样品的数据选用 likelihood ratio test。

fit <- glmQLFit(dge, design, robust=T)

lt <- glmQLFTest(fit, contrast=c(-1,1))

fit <- glmFit(dge, design, robust=T)

lt <- glmLRT(fit, contrast=c(-1,1))

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[15]

fit <- glmQLFit(y, design)

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[16]

names(fit)

'coefficients'
'fitted.values'
'deviance'
'method'
'counts'
'unshrunk.coefficients'
'df.residual'
'design'
'offset'
'dispersion'
'prior.count'
'AveLogCPM'
'df.residual.zeros'
'df.prior'
'var.post'
'var.prior'
'samples'

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Step 9: 使用 LRT（Likelihood Ratio Test）计算差异表达

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[17]

lrt <- glmQLFTest(fit, contrast = c(-1, 1))

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[18]

names(lrt)

'coefficients'
'fitted.values'
'deviance'
'method'
'unshrunk.coefficients'
'df.residual'
'design'
'offset'
'dispersion'
'prior.count'
'AveLogCPM'
'df.residual.zeros'
'df.prior'
'var.post'
'var.prior'
'samples'
'table'
'comparison'
'df.test'
'df.total'

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lrt 对象包含有关差异表达检验的结果，这些结果将用于确定哪些基因在不同条件下表达差异显著。以下是 lrt 对象的一些重要属性和参数的解释：

coefficients: 这是模型的系数估计。对于差异表达分析，通常包括每个基因的估计均值（对应于不同条件）。

fitted.values: 这是模型的拟合值，表示每个观测值在拟合模型下的预测值。

deviance: 模型的偏差（deviance）是一种拟合度量，用于表示模型与数据的拟合程度。较小的偏差表示模型与数据的拟合较好。

method: 进行假设检验的方法，通常是似然比检验。

unshrunk.coefficients: 这是未收缩（unshrunk）的系数估计值。在一些情况下，这是为了更稳健的统计估计。

df.residual: 残差自由度，表示模型拟合残差的自由度。

design: 模型中使用的设计矩阵，描述了实验设计和条件。

offset: 偏移量，通常用于在模型中添加额外的线性效应。

dispersion: 离散度参数，用于描述数据的离散性，包括 common、trended 和 tagwise 离散度的估计。

prior.count 和 prior.df: 这些参数与贝叶斯统计模型相关，通常用于贝叶斯分析。

samples: 样本的信息，包括每个样本的条件和组别。

AveLogCPM: 每个基因的平均对数计数每百万（Counts Per Million，CPM）。它表示基因的相对表达水平。

table: 包含了模型拟合的详细统计结果，包括每个基因的统计显著性和调整后的 p 值。

comparison 和 df.test: 这些参数可能用于指定进行的比较和检验的自由度。

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Step 10: 从 LRT 计算结果中获取前 n 个顶部差异表达基因

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[19]

nrDEG <- topTags(lrt, n = nrow(y))

DEG <- as.data.frame(nrDEG)

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[20]

dim(DEG)

head(DEG)

24530
5

A data.frame: 6 × 5
	logFC	logCPM	F	PValue	FDR
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
PRR27	-12.729153	4.7686655	1907.597	1.419174e-180	3.481234e-176
AMY1B	-9.455292	-0.9231500	1857.642	3.964098e-178	4.861966e-174
BPIFA2	-12.962811	6.0797551	1512.774	1.035781e-159	8.469238e-156
KRTAP13-1	-8.472988	-2.0176758	1485.929	3.767642e-158	2.310507e-154
KRT35	-8.859156	-0.1181604	1460.096	1.251985e-156	6.142240e-153
DGAT2L6	-8.850426	-0.8143911	1446.543	8.011343e-156	3.275304e-152

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[21]

# 加change列，标记上下调基因

logFC = 2.5

P.Value = 0.01

k1 <- (DEG$PValue < P.Value) & (DEG$logFC < -logFC)

k2 <- (DEG$PValue < P.Value) & (DEG$logFC > logFC)

DEG <- mutate(DEG, change = ifelse(k1, "down", ifelse(k2, "up", "stable")))

table(DEG$change)

  down stable     up 
   982  22361   1187

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Step 11: 差异分析结果可视化

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[22]

# 火山图

p1 <- ggplot(data = DEG,

aes(x = logFC,

y = -log10(PValue))) +

geom_point(alpha = 0.4, size = 3.5,

aes(color = change)) +

ylab("-log10(Pvalue)")+

scale_color_manual(values = c("blue4", "grey", "red3"))+

geom_vline(xintercept = c(-logFC, logFC), lty = 4, col = "black", lwd = 0.8) +

geom_hline(yintercept = -log10(P.Value), lty = 4, col = "black", lwd = 0.8) +

theme_bw()

p1

#ggsave(filename = "volcano_plot_edgeR.pdf", plot = p, device = "pdf", width = 6, height = 5)

#dev.off()

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[23]

# 差异基因热图

deg_opt <- DEG %>% filter(change != "stable")

expr_heatmap <- expr %>% filter(rownames(expr) %in% rownames(deg_opt))

annotation_col <- data.frame(group = groups)

rownames(annotation_col) <- colnames(expr_heatmap)

p2 <- pheatmap(expr_heatmap, show_colnames = F, show_rownames = F,

scale = "row",

cluster_cols = F,

annotation_col = annotation_col,

breaks = seq(-3, 3, length.out = 100))

p2

#ggsave(filename = "heatmap_plot_edgeR.pdf", plot = p1, device = "pdf", width = 5, height = 6)

#dev.off()

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差异基因热图解释：

行代表基因，列代表样本。每个单元格的颜色表示相应基因在相应样本中的表达水平。高表达基因用明亮的颜色（如红色）表示，低表达基因用暗色（如蓝色）表示。

差异基因热图对基因和样本进行分层聚类，将具有相似表达模式的基因和样本分组在一起。这有助于发现具有相似表达模式的基因簇和样本簇。

差异基因的识别：通过观察热图，可以看到哪些基因在不同样本之间具有差异的表达模式。明亮的行或列通常表示这些行或列对应的基因或样本在不同组之间具有显著的差异。

验证：差异基因热图的结果通常需要验证。可以使用实验室实验（如RT-qPCR）或其他独立的RNA-Seq数据集来验证在热图中观察到的差异表达。

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基因差异分析的基本过程已经完成，我们已获得了不同分组间的差异基因相关信息。下一步，我们可以进行富集分析，以进一步深入探究这些差异基因的功能和生物学意义。

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Step 12: GO富集分析

代码

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[24]

GO_all <- enrichGO(gene = rownames(DEG), #基因列表

keyType = "SYMBOL", #指定的基因类型

OrgDb=org.Hs.eg.db, #物种对应的org包

ont = "ALL", #CC细胞组件，MF分子功能，BF生物学过程，ALL以上三个

pAdjustMethod = "fdr", #多重假设检验校正方式

pvalueCutoff = 0.01, #p值阈值

qvalueCutoff = 0.01, #q值阈值

readable = TRUE) #基因ID转换为基因名

GO_result <- data.frame(GO_all)

head(GO_result)

A data.frame: 6 × 10
	ONTOLOGY	ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<chr>	<int>
GO:0007264	BP	GO:0007264	small GTPase mediated signal transduction	465/14902	483/18614	2.879704e-26	1.860289e-22	1.028509e-22	NUCB2/MYOC/OBSCN/CGNL1/ABRA/ARHGEF10L/PHACTR4/ADRA1A/AMOT/CHP1/FOXM1/GNA12/CUL3/KIF14/VAV2/KALRN/SPRY1/RHOU/ECT2/ARHGAP6/SPRY4/EPS8L2/LIMK1/RHEBL1/FGF10/SYNPO2L/EPS8L1/RTN4R/USO1/RAB9B/RAF1/ARHGAP10/CDK2/IQGAP3/RTKN/CCDC125/ARHGAP20/PDPN/ABL2/ARHGAP11A/RACGAP1/CHN1/ABR/DOK5/EPHB2/ARHGEF1/GDI1/MYO9B/NUP62/STMN1/ARHGAP27/RGS19/CCNA2/NISCH/ARFGAP1/PLEKHG4B/GABARAP/ITGA3/RALGAPA2/PLEKHG7/ROPN1B/SRGAP2/DOCK9/RERG/F2RL2/GNB1/COL3A1/RND1/DOCK3/COL1A2/ARHGAP11B/HACE1/LPAR2/NKIRAS1/CHML/RASAL1/MET/CFL1/RASGRF1/RASGEF1A/SYNJ2BP/TRIO/ARHGAP32/ARHGAP12/PSD2/CCDC88A/LPAR6/F2R/ITGAV/KSR1/DOK3/EPS8L3/RELN/ARRB1/RHOC/KBTBD6/KNDC1/FAM13A/CDKN2A/NTRK1/ARF6/SPRY2/DOCK5/BRK1/PREX2/VAV3/GRAP/GIT1/ARHGAP22/TIAM1/CRKL/TNK2/MAPRE2/LZTR1/FGD4/F11R/SYDE2/PDGFRB/CDON/ARHGAP18/KANK1/RHOA/FLCN/DENND1A/RALGAPA1/RAC3/PLCE1/RAPGEF2/EPO/SYDE1/LAT/GNA13/RHOQ/RIN2/MAP4K4/GRB2/ARHGEF3/PLEKHG6/PPP2CB/ARHGAP4/ARHGAP44/RHOF/RALGPS2/RALGPS1/RND3/ARHGAP33/FAM13B/RRAD/SGSM3/SIPA1/IGF1/MCF2L/RAB39A/SRC/RHOT2/DENND3/AGTR1/DOCK11/RAB33A/RAP1GAP/CTNNAL1/RHOJ/RAP1B/ABCA1/ADCYAP1R1/RUNDC3A/PLEKHG4/GPR4/APOA1/SHOC2/RAB4A/DENND4C/PLEKHG1/NRAS/RUFY1/RASGEF1B/APOC3/TRIM67/SOS2/DBNL/GPR55/SIPA1L3/RANGRF/RHOT1/ITGB1/PLEKHG5/FBXO8/ARHGEF9/CELSR1/TNFAIP1/DHCR24/STMN3/BCL6/PLEKHG3/RAPGEFL1/RASA3/GARNL3/ARHGAP8/IQSEC2/RALGDS/CYTH3/ARHGEF15/SH3BP1/PSD/DOK4/CBL/ARHGEF12/NTN1/ARHGAP40/RAP1A/SH2B2/KPNB1/NGF/MRAS/RRAGC/KCTD13/USP8/ARHGAP25/ADRA2A/RGL2/ARHGAP5/CDH13/STARD13/DOK2/SSX2IP/DOCK6/DOK1/SLIT2/F2RL3/STARD8/ARHGAP9/RASA4/NKIRAS2/RAP2B/DIRAS3/CYTH4/SPRY3/SWAP70/SIAH2/WASF2/RHEB/YWHAQ/DEF6/NET1/GMIP/RASGRP1/RAP2A/SRGAP3/LPAR1/CHN2/NOTCH2/FGD1/DAB2IP/FGD5/ARHGEF25/MAPKAP1/RALA/ARAP1/RASIP1/RALB/F2RL1/MCF2/G3BP1/ARHGAP23/APOE/OGT/HEG1/DLC1/EPS8/PLD2/MYO9A/RASAL3/ARHGDIA/BRAP/ARHGEF17/ITSN1/RAPGEF4/PSD3/RHOV/AKAP13/RASGEF1C/SH2D3C/TGFB2/RFXANK/DOCK4/BCAR3/ARFGEF2/NF1/RAB35/ARHGEF19/MAPKAPK5/ARHGAP26/CYTH2/ROCK1/RAPGEF1/RAB38/WAS/ARHGEF28/PSD4/ARHGAP39/RAB15/PREX1/RAB39B/GPR174/RAB30/DNM2/ARHGAP21/G3BP2/IQSEC1/HRAS/FGD3/RASGRF2/GPR18/RAB33B/ARHGAP28/RASGRP3/DOCK2/ARL3/PIK3CG/MADD/OPHN1/SH2D3A/NRP1/ARHGEF2/WASF1/P2RY10/CYFIP1/GDI2/RAP2C/ABI2/RGL4/GPR35/RGL1/ARHGEF11/FGF2/KRIT1/ALS2/RASA2/RABL3/SPATA13/RAPGEF3/ARHGAP19/DOCK7/CSF1/TSC2/SOS1/ARHGEF10/ARFIP2/ROCK2/STK19/ITPKB/RABGEF1/TAX1BP3/MFN2/P2RY8/DOCK8/RABIF/RHOG/TGM2/RGL3/LPAR4/ARHGEF4/DAB1/NCKAP1/CRK/STAMBP/ARHGAP30/GPR65/GRAP2/DOCK10/SYNGAP1/SIPA1L1/TAGAP/GBF1/CDC42SE1/ABL1/KITLG/CD2AP/CADM4/ELMO1/BCR/RAB21/GIT2/KCTD10/ARHGDIG/CHM/SIPA1L2/AUTS2/GPR20/ARHGAP31/AIF1/ARHGAP35/CDC42SE2/RAC1/ARHGAP29/RHOBTB2/CDKN1A/PLK2/NGFR/RAB12/KANK2/DENND4B/RHOBTB1/KSR2/HMOX1/ARHGAP1/VAV1/RB1/ARHGEF40/ARFGEF1/FARP2/GPR17/RASSF1/ARHGAP15/RHOD/FBP1/RAB4B/DNMBP/RAB18/RHOB/ARHGAP17/SQSTM1/ARHGDIB/GPSM2/CCDC88C/TIAM2/DOK6/KBTBD7/RASL10A/SCAI/PLD1/DOCK1/ARHGEF5/SDCBP/RAPGEF6/ARHGAP42/CYTH1/RDX/RAB6C/RALGAPB/FGD2/RIT1/PARK7/RAB9A/RND2/ROBO1/ARHGEF18/KRAS/RAP1GAP2/TP53/RAPGEF5/RALBP1/NUCB1/DENND4A/PIK3CB/FRMD7/FLOT1/CDC42/NGEF/RASGRP4/ARHGAP24/DNAJA3/IQSEC3/RASGRP2/NOTCH1	465
GO:0050808	BP	GO:0050808	synapse organization	449/14902	466/18614	1.119414e-25	3.615706e-22	1.999037e-22	SLC6A1/ERBB4/LINGO4/MAPT/KY/CAP2/LRRTM1/PTK7/COL4A1/APPL1/CAST/SORT1/MYOT/CAMK2B/NFIA/CACNB4/TPBG/NRG3/AGRN/CDC20/ICAM5/ZDHHC15/PDLIM5/COL4A5/NTRK3/GLRB/CACNB1/CHRDL1/TUBB/CACNA1S/SNTA1/SPTB/STAU2/ACHE/HMCN2/EGLN1/RAB17/LRFN2/CX3CR1/LRFN4/CTTNBP2/ASAP1/C1QL1/SLC8A3/EPHB2/CTNND2/LRRK2/SPARCL1/SIX1/AMIGO2/DMPK/AMIGO1/DRD2/SLITRK5/ITGA3/GRM5/MEF2C/SLC1A1/TREM2/UNC13B/SRGAP2/ROR2/PCDHGC5/CACNB2/FBXO45/SEMA3E/FLNA/TNC/APBB2/CLSTN1/APP/WNT7B/SLC25A46/PRNP/ANK3/NEURL1/LRRC4C/NLGN2/CFL1/PDZRN3/RAPSN/LAMA5/ADAM10/PCDHB9/LZTS3/F2R/PFN1/LRP8/PCDH17/RELN/SDF4/NTRK1/ARF6/SYBU/SPG11/DSCAM/SDK2/PPFIBP2/DLGAP3/DNER/THBS2/PPFIA1/DHX36/ARHGAP22/SPTBN2/CAPRIN2/CRKL/IGF1R/EFNA5/ITPKA/SHISA7/CHCHD10/SYNDIG1/C3/PLXND1/ELFN1/GABRG2/RHOA/FLRT2/ACTN1/GHSR/CDH6/RAC3/RAB29/GNPAT/ADD2/NEUROD2/LRRTM2/ARHGAP44/SEMA4D/PFN2/PCDHB4/ARHGAP33/POU4F1/SLITRK4/CHRNB1/PCDHB14/UNC13A/PALM/SDK1/IGSF9/PPFIA4/NEFL/ABHD17B/TANC2/LAMB2/SLC8A2/INSR/SHISA6/NPTX1/ROBO2/RAP1B/ZDHHC12/CAMKV/CTTN/SNCB/SEZ6/UBE3A/DKK1/UNC13C/ITGAM/RPS6KA5/PCDHB10/NEFH/ST8SIA2/CDH2/DOK7/CDH10/SLC7A11/WNT7A/NRP2/AKT1/HDAC6/DBNL/SHANK1/SEMA3F/ITGB1/PTPRD/SYNPO/STK38/SLITRK2/SLITRK1/ARHGEF9/CDK5R1/EZR/NRCAM/MDGA1/RAB3A/LILRB2/VSTM5/LRTM2/WNT3A/RYK/CNKSR2/DBN1/FLRT3/PLXNC1/PTPRO/ARHGEF15/SRCIN1/L1CAM/LRRTM3/PPFIBP1/CNTNAP1/CACNG2/EIF4G1/ERBB2/NLGN4X/OXT/POTEKP/ZDHHC2/C1QC/EFNA1/C1QB/NTN1/IL1RAP/CBLN3/CRIPT/PTK2B/GPHN/MYCBP2/PTPN13/NEGR1/PLXNB1/CDK5/LRRC4B/CHD4/SEMA4A/CX3CL1/BAIAP2/CNTN5/CTNNA2/PAK3/ZC4H2/SRPX2/FYN/COLQ/DAG1/KIRREL3/SPOCK2/LRP4/PCDHB11/TLR2/FGF13/WASF2/SLC18A3/GAP43/C1QA/ERC2/NRXN2/KIF1A/RAP2A/SNCA/FZD1/ZNF804A/DVL1/FNTA/DAB2IP/GABRB2/LGI2/CDKL5/SEZ6L/PCDHB6/EFNB2/TANC1/APOE/LGMN/OGT/GPM6A/CUX2/CLSTN3/EPHB1/PRMT3/WASL/ACTG1/IL1RAPL2/C5AR1/ASIC2/LRFN3/IL10/CBLN2/AFG3L2/SLITRK3/DLG1/C1QL3/TNF/ADNP/ABI3/PCDHB16/CTNNB1/NEDD4/TSC1/BDNF/IL10RA/NF1/WNT5A/PIN1/ROCK1/LRRN1/CDH8/LZTS1/PGRMC1/SEZ6L2/PSEN1/CBLN1/SRGAP2C/ARHGAP39/ARF4/RAB39B/VCP/WASF3/TNR/PPFIA3/CAPRIN1/NLGN3/RER1/MTMR2/PCDHB2/YWHAZ/ARC/DRD1/HSPA8/STAU1/OPHN1/DNM3/C1QL2/ACTB/LMX1A/NRP1/EFNB3/NTNG1/NTRK2/INA/ERC1/WASF1/LHFPL4/CYFIP1/IGSF21/GHRL/ABI2/NRXN1/ALS2/PCDHGC4/MUSK/NTNG2/CHRNA1/TMEM108/LRRC4/LRFN5/DIP2A/DCTN1/CPNE6/GRID1/GRIPAP1/BCAN/NLGN1/SHANK3/PLXNA4/NFATC4/GRIN2B/FCGR2B/CBLN4/ITGB3/CRK/ANAPC2/NPTN/SHANK2/P2RX2/CTBP2/DOCK10/ZNF365/SYNGAP1/TUBA1A/DISC1/SIPA1L1/EEF2K/PCDHB5/ABL1/PRRT1/CD2AP/DLG4/VPS35/DLG5/NEDD9/KLK8/PAFAH1B1/ARF1/LRRN3/SYN1/CLN3/MAP1B/PLXNB2/GDNF/MAPK14/IL1RAPL1/SNAPIN/DTNBP1/ACTBL2/PICK1/EPHA4/STK38L/GPC4/NPAS4/CNTN2/CDH1/CHRNA7/BSN/CAMK1/GABRB3/PCDHB13/RHOB/SLITRK6/PTEN/PTPRS/EPHB3/SLIT1/CLSTN2/NOS1AP/SDCBP/GABRA1/PTPRF/FZD5/FRMPD4/PCLO/DRP2/SETD5/PCDH8/HIP1R/FZD9/HOMER1/OXTR/CACNB3/FRRS1L/CHRNB2/CDC42/REST/GRID2/PPFIA2/NGEF/FARP1/PCDHGC3/DNAJA3/GABRA2/NLGN4Y/MECP2/PCDHB3/SIX4/EPHA7/SNCG	449
GO:0048732	BP	GO:0048732	gland development	424/14902	441/18614	1.284632e-23	2.266953e-20	1.253344e-20	CSN2/RGN/ACADM/SLC46A2/KRT76/ERBB4/FRZB/HMGCS2/TYR/ACAT1/CEACAM1/TNFRSF11A/DNAAF1/COBL/ANXA1/TGFB1/AURKA/TGFBR3/CUL3/ID4/PLXNA1/EXT1/CCNB2/ATP7B/CACNB4/ACO2/NRG3/SNAI2/FA2H/FOXC1/E2F7/HOXA11/FGF10/EGF/MAPK3/HOXB9/ONECUT2/GDF7/RAF1/PITX1/IQGAP3/HOXA13/PSAPL1/CSN3/ADA/HOXD9/NKX3-1/FGF7/TBX3/SIX1/PCSK9/HOXD13/MSN/DRD2/NTN4/BRCA2/TFCP2L1/CAV3/FADD/PCNA/AR/TYMS/OAS2/ACER1/RHBDD3/CITED2/ONECUT1/CLCN2/ZBTB7B/SULF1/APLN/FBXW7/LSR/TNC/CAV1/WNT2/RAG1/WNT7B/PRKCSH/NEURL1/ALDH1A3/TNFSF11/NFIB/PBX1/ELF3/MET/PDGFA/NRG1/CIT/EPHA2/CDO1/SFRP1/LAMA5/DEAF1/NR5A1/ARID5B/BAX/TGFBR1/ARF6/RXFP1/PML/LAMA1/UMPS/HOXB13/CCDC40/VEGFA/STRA6/HNF1B/NPHP3/TP63/WT1/IGSF3/SULF2/CRKL/MAD1L1/LHX3/BMPR1A/SMARCB1/SERPINA10/ASXL1/PLXND1/ZDHHC21/PRDX2/PDX1/CCL11/TBX2/PTPN3/RREB1/RPS6KA1/EDAR/FOXB1/DUT/HIF1A/GSX1/CYP7B1/SRSF5/PTN/SLC7A5/PYGO2/GLI3/SERPINE2/RARG/HNF1A/TWSG1/IL6/FPGS/ESRP2/GLI1/PIK3CA/EZH2/SRC/NME1/ALDH1A2/HNRNPD/INSM1/DKK3/POU3F2/TNFAIP3/PGR/CSNK2A2/RAP1GAP/SRF/SALL1/INSR/IGFBP5/EGFR/OTP/ATP7A/UBE3A/PITX2/MTX1/APOA1/TGFB3/THRA/HOXA9/WNT10A/TSPO/PLAG1/CYP1A1/EDNRA/BMP2/BCL2L11/PNPT1/PAX6/BTRC/FASN/ZIC3/AKT1/TBX19/UGT1A8/CADM1/SEMA3C/CFLAR/MDK/GHRH/SMAD4/AKT2/MESP1/GFER/FGF8/IGF2R/QDPR/CAPN1/EAF2/FOXA1/WNT3A/BMP4/LIMS2/CCDC39/RIPK3/KDM5B/HES1/PTCD2/IRF6/BAAT/GLI2/ID2/SOD2/NR0B1/WLS/STAT5B/LBH/NKX2-3/SLC6A3/PCK1/NTN1/DDR1/CEBPB/MMP2/GATA3/JARID2/CLDN1/ORAI1/ARHGAP5/WNT3/SHH/DAG1/LMO4/FGFR2/UGT1A7/ALOX15B/UCP2/IHH/SP3/TCF21/VDR/TGFA/EDA/ARG1/NOTCH2/ATF2/LEF1/SLC29A2/ASCL1/MED1/MAN2A1/WNT4/PRL/ELK1/ZBTB1/HOXD3/CGA/IRS2/IL10/BCL11B/PCSK1/FKBP4/CEBPA/ASH1L/TNF/RBPJ/PAM/SERPINF1/CYP1B1/TGFB2/PKD1/SLC29A1/AIRE/FOXE1/CTNNB1/VTN/PSAP/CDK5RAP3/NF1/PCK2/WNT5A/TUBB1/CYP19A1/JUN/PSEN1/LRP5/HLX/GPX1/TDGF1/ISL1/NKX2-1/MAP2K1/SOX2/FRS2/POLB/PAX8/TGFBR2/ITGA2/CCND1/CDKN1B/FGL1/NOTCH4/SCRIB/HK2/GATA2/NRP1/BCL2/BRAF/NCOR2/CSMD1/NR3C1/MSX1/FGF2/HPN/SRP54/RPGRIP1L/SEC63/PKD2/EDN1/PRMT5/RB1CC1/HAND2/CEBPG/CSF1/MGMT/ZMPSTE24/AREG/HOXA5/UGT1A10/CREB1/CPT1A/MPST/SOX9/CRHR1/STK11/WNT1/MAP2K2/TGM2/ZNF703/PDGFRA/FGF1/JAK2/RELA/SMAD2/TBX1/SMAD3/SOD1/TG/PROX1/ABL1/PRLR/HOXB3/HAMP/GATA6/ATP2C2/CCKBR/HGF/FEM1B/MAFB/WNT11/UGT1A1/XDH/ARHGAP35/CSF1R/STAT5A/RPS15/HESX1/RARA/TAF10/BMP7/HMOX1/STAT6/SERPINB5/PHB2/CDH1/SOSTDC1/MSX2/BTBD7/CPB2/LATS1/ASS1/FOXF1/MAPK1/PTCH1/PHF2/NKX2-8/CRIP1/INHBB/IGF2/RTN4/EPHB3/NOG/CTC1/APRT/SOCS2/WDR77/FOXN1/ARMC5/CPS1/CDKN2B/SIX3/USF2/ROBO1/SRD5A1/NODAL/OXTR/ESR1/ATM/TPH1/UPB1/CDKN1C/NFKB1/UPF2/E2F8/SIX4/NKX2-5/HOXA3/NOTCH1/SMO	424
GO:0060562	BP	GO:0060562	epithelial tube morphogenesis	327/14902	334/18614	1.673567e-23	2.266953e-20	1.253344e-20	PHACTR4/DNAAF1/COBL/TGFB1/PTK7/FZD2/COL4A1/CXCR2/FOLR1/EXT1/SPRY1/OSR1/TIMELESS/KIF26B/ASB2/HOXA11/TCAP/SOX17/MMP14/HOXD11/STK3/FGF10/EGF/GBX2/HOXB7/ADAMTS12/GDF7/CTHRC1/STIL/NKX3-1/MICAL2/MTHFD1L/TBX3/SIX1/FOXD1/TEAD2/SALL4/CLIC4/CCM2/NPNT/PIK3CD/FZD6/CAV3/AR/MEF2C/SOX11/DVL3/PRKACA/GRHL3/HECTD1/CITED2/KAT2A/TACSTD2/SEMA3E/VASP/WNT2/AGT/PBX1/MET/LBX1/CEP290/EPHA2/WNK4/SFRP1/LHX1/FMN1/LAMA5/DEAF1/ACVR1/PFN1/GREM1/LIAS/ITGAX/PML/LAMA1/FOXH1/CCDC40/SPRY2/CC2D2A/VEGFA/CTSZ/RGMA/HNF1B/NPHP3/WT1/IFT172/FOXC2/CXCL10/PLXND1/KIF20B/STK4/GATA4/RHOA/CCL11/TBX2/MTHFR/PKHD1/HIF1A/GNA13/SETD2/ARL13B/GLI3/PRKD2/RARG/EYA1/RBM15/ESRP2/FOXN4/SRC/PRKACB/PGR/SETDB2/IFT122/SRF/SALL1/PAK1/FOXP1/SPINT1/SIRT6/WNT2B/DVL2/DLL4/LGR4/EDNRA/BMP2/TRIM71/BTRC/ZIC3/TBX20/SOX4/MDK/SMAD4/SEMA4C/MESP1/CELSR1/FGF8/GZF1/VANGL2/WNT6/TWIST1/CTNNBIP1/FOXA1/WNT3A/MIB1/BMP4/CCDC39/KDM5B/BCL10/HES1/FUZ/ADM/BMP5/GLI2/NDRG4/NTN1/DDR1/LUZP1/HAND1/GATA3/MTSS1/STARD13/IRX3/SLIT2/MMRN2/SHH/DAG1/MTHFD1/LMO4/FGFR2/RET/CXCR4/LHX2/IHH/TCF21/VDR/FZD1/PPP1CA/TBX6/DVL1/NOTCH2/KLHL3/RALA/RASIP1/GREB1L/EFNB2/HS2ST1/TRAF6/DLC1/LEF1/PAX2/SEC24B/MED1/SIX2/WNT4/OPA1/MAGED1/GLMN/BBS5/DLG1/LGR5/TNF/RBPJ/CASR/TGFB2/PKD1/CTNNB1/ACVRL1/RYR2/TSC1/RDH10/WNT5A/MYC/DCHS1/IFT52/BRD2/CSNK2B/PSEN1/LRP5/C2CD3/NKX2-1/HHIP/PAX8/TGFBR2/SUFU/ST14/TCTN1/NOTCH4/SCRIB/FZD3/SDC4/NRP1/BCL2/TULP3/CSMD1/LRP2/NR3C1/FGF2/PKD2/SFRP2/GRHL2/CTSH/EDN1/HAND2/CSF1/CDK20/AREG/HOXA5/TSC2/SOX18/SPINT2/IRX2/MED12/SOX9/ADAMTS16/PPP3R1/APAF1/WNT1/NFATC4/IRX1/BBS7/HS3ST3A1/FGF1/HS3ST3B1/SMAD3/MKKS/BBS4/PROX1/ABL1/ENG/ILK/RSPO2/DLG5/NRARP/MEGF8/MKS1/KDR/WNT11/KDM2B/PLXNB2/ARHGAP35/GDNF/CSF1R/GPC3/AHI1/HESX1/CASP3/RARA/SKI/BMP7/EPHA4/APLNR/PHB2/TMEM59L/SOSTDC1/MSX2/PODXL/IFT57/YAP1/RPS7/OVOL2/FOXF1/RHOB/PTCH1/NOG/MYCN/FKBPL/HES5/SOX8/RNF207/WNT9B/NODAL/KRAS/ESR1/STOX1/PRKX/TIE1/PRICKLE1/DLL1/CECR2/SIX4/NKX2-5/CLUAP1/EPHA7/NOTCH1/SMO/TMED2/CITED1	327
GO:1903829	BP	GO:1903829	positive regulation of protein localization	448/14902	468/18614	1.754608e-23	2.266953e-20	1.253344e-20	GPD1L/CFTR/PFKM/MYRIP/SORBS1/ERBB4/MYOM1/CHRM1/MAPT/KCNB1/SAR1B/TGFB1/CHP1/PLK1/EXPH5/MIEF2/CACNB4/TTN/TMEM132A/RHOU/PRKAA2/PINK1/PPARG/NDC80/ECT2/IL13/STAC3/EGF/BICD1/CDK1/PCM1/CDT1/ITPR1/ACHE/PIK3R1/APBB1/SYTL4/RAC2/SLC51B/ADAM8/SAE1/EPHB2/NNAT/MYO18A/CEP131/NUP62/AGR2/TERT/C2CD2L/SH3GLB1/ABLIM3/MSN/ATP2C1/PCNT/ARIH2/ITGA3/PPP3CB/HPCA/NKD2/STAC2/NKX6-1/CCT5/TREM2/GOLPH3L/VRK1/UNC13B/PRKACA/CDK9/F2RL2/DTX3L/ANG/LGALS3/FBXW7/KCNQ1OT1/NPEPPS/FLNA/CNPY4/PRNP/ANK3/NECAB2/TFRC/NLGN2/CEP290/PTP4A3/TRPM2/EPHA2/B3GAT3/ZDHHC8/RIC3/PARP1/GNL3/CCDC88A/OAZ2/CENPQ/CENPJ/EDEM2/PRR5L/PRKCD/ARF6/ANO1/GLUL/VEGFC/NMT1/VIL1/TRIM28/MYH10/EPB41/SLC5A3/CEP135/WRAP53/DOC2B/ANP32B/DZIP1/GPR137B/TRAPPC12/PRKAR1A/AKAP5/SEC16B/EXOC1/HPS4/KIF20B/BAG3/PDX1/VAMP2/MICALL2/RUFY3/GNAI1/HIF1A/MTCL1/RAB29/ABCG1/IL1A/CEP250/GLIS2/GCK/HTRA2/GLI3/DKC1/RAN/CCT3/PLS1/CCT6A/IL1B/ITGB1BP1/PPP1R9B/CCT4/IGF1/HSPA1L/XPO4/GPER1/SRC/RPH3AL/PDCD10/PTPN9/ABHD17B/OAZ3/PDCD5/PARP9/CSNK2A2/HSP90AA1/GPR68/IFNG/PPM1A/BBC3/GAS8/EGFR/SIRT6/MPC2/ANKRD1/ABCA7/LRIG2/ITGAM/TARDBP/HSP90AB1/ADCY8/GSK3B/TGFB3/KAT7/GIP/FFAR1/PTGS2/OXCT1/CARD10/APC/ATP13A2/PSMD9/RBP4/ATP2B4/GZMB/PPIA/WNT7A/BAIAP3/AKT1/MAVS/GNA11/RANGRF/SOX4/ITGB1/RAMP3/PTPN23/ATG13/TENM1/CROCC/AKT2/CDK5R1/EZR/ACSL3/CD38/CCT7/TSG101/ICE1/UBR5/VPS11/WNT3A/BCAP31/CCT2/ABCC8/EIF2AK3/CACNG2/UBE2L3/ERBB2/WLS/ZDHHC2/KCNN4/PECAM1/PLK3/OR51E2/PDPK1/ITGB2/ADORA2A/STX4/MIEF1/CDK5/ORAI1/PPARD/TMEM30B/DNM1L/TUNAR/PRKCE/CTDSPL2/CD247/MCU/FYN/CNST/TLR4/SHH/GSK3A/BLK/SPTBN1/PRKCI/MARK4/MEPCE/LRP4/TYROBP/TLR2/ADAM9/PRKCH/SORL1/CORO2B/TESK1/TRIM8/STX3/DVL1/PRKG2/C1QTNF3/UCN3/LAMTOR5/PYHIN1/HCLS1/F2RL1/NMD3/FIS1/BAD/CLSTN3/DPP10/SERP1/PARD6A/EDEM1/LRRC8A/CLIP3/IRS2/ZIC1/RAPGEF4/PCSK1/DLG1/TNF/CASR/TOMM7/SLC35D3/SAR1A/F2/TGFB2/PIK3R2/USP36/SLC2A2/AACS/GLUD1/TCP1/APBB3/CDK5RAP3/OAZ1/PCK2/WNT5A/TNFAIP6/SYT11/RAB38/GPR27/CEP295/PGRMC1/ABAT/PSEN1/LRP1/PDZK1/ISL1/KIF5B/FGA/MGAT3/HLA-DRB1/SESN2/MAP1A/RER1/SLC30A8/ABCA12/GIPR/HRAS/SNX33/RBM22/HDAC3/BCAS3/TM9SF4/HYAL2/TCF7L2/GHRL/ABHD17C/TM7SF3/TPR/ZDHHC5/UBE2D3/FFAR2/ZFAND1/SEC24A/CHP2/ERGIC3/RAPGEF3/TRPM4/ACSL4/LIMK2/WNK3/ROCK2/TRH/EMD/PRKCB/EPHA3/FGB/IPO5/VPS28/STK11/HCAR2/RHOG/LIF/UBE2J2/NUMA1/SREBF2/P2RX7/LARP7/ZC3H12A/CCT8/NPTN/TCAF1/FERMT2/JAK2/SMAD3/GNL3L/EFCAB7/CD2AP/FGG/DLG4/VPS35/NR0B2/ABHD17A/MYO1C/NEDD9/CD33/STOM/CLN3/WWTR1/HUWE1/IER3IP1/CHRM3/PRKCZ/TMEM30A/DMAP1/CIB1/MAPK14/TMED10/PPID/PINX1/MFF/NGFR/OSBP/NF2/GAS6/ORMDL3/ARHGEF16/SFN/UBL4B/PRKD1/LEP/CDH1/C2CD5/MAPK8/TCAF2/BAP1/CAMK1/VSNL1/YAP1/EPB41L2/CAPN10/SQSTM1/GPSM2/BMP6/RTN4/SRI/UBL5/LEPROT/DDRGK1/GOLPH3/ARHGEF5/PRKAA1/PLCB1/PLA2G6/FZD5/PFKFB2/RDX/YWHAE/MCRS1/PARK7/RNF31/GPLD1/STAC/ZPR1/CD81/CEP120/NR1H2/JUP/FRMD4A/CEMIP/SMO	448
GO:0048568	BP	GO:0048568	embryonic organ development	434/14902	453/18614	4.912005e-23	5.288592e-20	2.923935e-20	PIFO/ATP6V1B1/CHRNA10/FRZB/PHACTR4/DNAAF1/COBL/TGFB1/MFAP2/PTK7/FZD2/HOXC9/FOLR1/TBX15/COL27A1/HOXD10/CHST11/OSR1/ASB2/FOXC1/E2F7/HOXA11/NCOA1/TCAP/SOX17/MMP14/STK3/FGF10/GBX2/KIT/SHOX2/HOXB7/MAPK3/HOXB9/HOXC11/RSPO3/FOXI1/SLC44A4/HOXC4/EGLN1/CTHRC1/ADA/HOXD9/PLK4/STIL/MICAL2/MTHFD1L/EPHB2/TBX3/SIX1/DLX6/FBN2/CSF2/TEAD2/KCNQ4/COL11A1/FOXL2/FOXF2/MYO7A/FZD6/PDZD7/SOBP/MEF2C/SOX11/GRHL3/HECTD1/CITED2/KCNQ1/EN1/EN2/PBX4/POLE/PRDM1/WNT2/WNT7B/ALDH1A3/PBX1/SOCS3/PDGFA/LBX1/CEP290/EPHA2/HOXB8/LHX1/ACVR1/DLX2/SLC39A1/PCGF2/ZNF568/HMX2/TGFBR1/FOXH1/HIPK2/CCDC40/SPRY2/CC2D2A/VEGFA/TRIM28/LHFPL5/COL2A1/SENP2/PAX5/STRA6/HNF1B/NPHP3/IFT172/COL18A1/HOXD4/PPP1R35/FOXC2/PALB2/BMPR1A/STK4/JUNB/GATA4/TBX2/VPS52/ERCC3/HIF1A/HIPK1/CASP8/GRB2/SETD2/ECE1/THOC5/TEAD1/A2M/ARL13B/GLI3/TBX4/HOXA1/RARG/EYA1/SH2B3/FLVCR1/LRIG1/PLS1/RARB/TRIOBP/MYO3B/GLI1/FOXN4/TBX18/PCDH12/ALDH1A2/ATP8A2/NKX3-2/TPO/HOXB6/PDGFB/SETDB2/PPIL1/VAX2/IFT122/SCT/OSR2/SRF/SALL1/CDX2/CXCL8/TSHZ1/SPINT1/ID3/EGFR/WDR48/ASCL2/EOMES/PITX2/HEY1/TGFB3/HOXA2/MDFI/DVL2/DLX5/HOXA9/HOXB5/TPRN/RBP4/EDNRA/TH/PAX6/INSIG2/ZIC3/AKT1/SLC39A3/TBX20/TFAP2A/MMP16/MESP1/FGF8/VANGL2/TWIST1/CCDC134/SATB2/MYO6/ALX3/ZFPM1/ZEB1/HMX3/WNT3A/MIB1/BMP4/CHRNA9/TRA2B/CCDC39/HES1/FUZ/ALX1/ADM/BMP5/GLI2/ID2/ZFPM2/HOXA7/NDRG4/EFNA1/NTN1/MYO15A/HAND1/CEBPB/IRX5/GATA3/NDST1/HOXB2/SHH/MTHFD1/FGFR2/FLT3LG/EFEMP1/IHH/SP3/TCF21/PLCD3/INSIG1/TFEB/TBX6/DVL1/WDR19/RPL10/CRB2/NOTCH2/MBD2/LEF1/MYF5/PAX2/TEAD4/APELA/SEC24B/FGF9/RBBP6/MED1/SIX2/HYAL1/VASH1/HOXD3/USH1G/ZIC1/IL10/WNT16/BBS5/DLG1/CEBPA/TNF/RBPJ/OTOP1/RBPMS2/TGFB2/PKD1/ITGA8/FOXE1/CTNNB1/RYR2/RDH10/FOXG1/WNT5A/SNAI1/NAGLU/IFT52/HOXB4/PSEN1/C2CD3/HLX/DSCAML1/SOX15/TBC1D23/NR2F2/STRC/EIF4A3/ZFP36L1/MYO3A/MAP2K1/FRS2/NES/PAX8/TGFBR2/SUFU/CDH23/ST14/SCRIB/FZD3/GATA2/TEAD3/HOXB1/GSC/PRKRA/IFT140/RAD23B/PRRX1/TULP3/TAL1/MSX1/HPN/PKD2/GRHL2/EDN1/ATF4/POU3F4/CDC40/HAND2/NSDHL/PKDCC/CDK20/MBD3/HOXA5/HEY2/SOX18/EPN1/SPINT2/MED12/SOX9/VASH2/PLXNA4/WNT1/PBX3/LIF/BMI1/BPTF/PDGFRA/BBS7/SMAD2/TBX1/SMAD3/GJB6/MKKS/PBX2/BBS4/GGNBP2/SOD1/PROX1/KITLG/ENG/DNAJB6/HOXB3/BCR/MEGF8/LRIG3/KMT2A/OTX1/TTPA/TTC39C/MKS1/HS6ST1/KDR/MAFB/WNT11/NIPBL/KDM2B/GDNF/RPL38/PCSK5/BIRC6/AHI1/ARNT/HESX1/RARRES2/RNF112/EPAS1/RARA/TAF10/TIFAB/BMP7/APLNR/IFT57/YAP1/OVOL2/ALX4/FOXF1/MAPK1/PTCH1/BLOC1S5/SLITRK6/KRT8/IGF2/NOG/MYCN/PPP1R13L/FZD5/RNF207/WDPCP/RUNX2/HOXA4/FBN1/GJB5/SIX3/USH1C/SYF2/WNT9B/NODAL/TP53/HSCB/CHD7/STOX1/MFAP5/CDC42/PRICKLE1/ERCC1/CDKN1C/TUBB2B/FBXW8/E2F8/ATOH1/DLL1/PHLDA2/SIX4/KRT19/NKX2-5/CLUAP1/HOXA3/ARID2/NOTCH1/SMO/NKX2-6/TMED2/CITED1	434

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Step 13: 富集分析结果可视化

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[25]

barplot(GO_all,showCategory = 20)

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[26]

dotplot(GO_all, showCategory=15)

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参考资料：

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