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RNA-seq分析(5)之GO富集分析

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接上一篇KEGG富集分析，使用clusterProfiler包进行GO通路富集分析。

1. 上调基因通路富集

enrichGO进行上调基因的通路富集，并提取富集结果。

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ego_up <- enrichGO(gene          = gene_up,
                OrgDb         = org.Dm.eg.db,
                keytype = "ENTREZID",
                ont           = "ALL" ,
                universe      = gene_all,
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.99,
                qvalueCutoff  = 0.99,
                readable      = TRUE)

head(ego_up[,1:6])
ego_up_result <- ego_up@result[order(ego_up@result$pvalue),]

选择前10个BP、CC、MF合并到一个dataset中并绘图。

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ego_up_sig <- rbind(head(subset(ego_up_result, ONTOLOGY == "BP"), 10),
                    head(subset(ego_up_result, ONTOLOGY == "CC"), 10),
                    head(subset(ego_up_result, ONTOLOGY == "MF"), 10))
ego_up_sig$pvalue <- -log10(ego_up_sig$pvalue)
ego_up_sig$Description <- factor(ego_up_sig$Description,
  levels = as.character(rev(ego_up_sig$Description)))

# GO富集分析柱状图，柱子颜色根据BP、CC、MF区分
ggplot(ego_up_sig, aes(x = Description, y = pvalue, fill = ONTOLOGY)) +
  geom_col() +
  coord_flip() +
  theme_classic() +
  facet_grid(ONTOLOGY~., scales = "free") +
  xlab("Up regulated") +
  ylab("-log10 pvalue")

# GO富集分析柱状图，柱子颜色变化依据p.adjust值
ggplot(ego_up_sig,
  aes(x = Description, y = pvalue, fill = p.adjust)) +
  scale_fill_gradient(low = "red",high = "blue")+
  geom_col() +
  coord_flip() +
  theme_classic() +
  facet_grid(ONTOLOGY~., scales = "free") +
  xlab("Down regulated")+
  ylab("-log10 pvalue")

# GO富集分析气泡图
ggplot(ego_up_sig,
  aes(x = GeneRatio, y = Description, color =p.adjust, size = Count)) +
  geom_point()+
  scale_size(range = c(2,8))+
  scale_color_gradient(low = "red",high = "blue")+
  theme_classic() +
  facet_grid(ONTOLOGY~., scales = "free") +
  xlab("Gene ratio")

2. 下调基因通路富集

下调基因同理。

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ego_down <- enrichGO(gene          = gene_down,
                   OrgDb         = org.Dm.eg.db,
                   ont           = "ALL" ,
                   universe      = gene_all,
                   pAdjustMethod = "BH",
                   pvalueCutoff  = 0.99,
                   qvalueCutoff  = 0.99,
                   readable      = TRUE)

head(ego_down[,1:6])
ego_down_result <- ego_down@result[order(ego_down@result$pvalue),]
ego_down_sig <- rbind(head(subset(ego_down_result, ONTOLOGY == "BP"), 10),
                    head(subset(ego_down_result, ONTOLOGY == "CC"), 10),
                    head(subset(ego_down_result, ONTOLOGY == "MF"), 10))
ego_down_sig$pvalue <- -log10(ego_down_sig$pvalue)
ego_down_sig$Description <- factor(ego_down_sig$Description, 
  levels = as.character(rev(ego_down_sig$Description)))

ggplot(ego_down_sig, aes(x = Description, y = pvalue, fill = ONTOLOGY)) +
  geom_col() +
  coord_flip() +
  theme_classic() +
  facet_grid(ONTOLOGY~., scales = "free") +
  xlab("Down regulated") +
  ylab("-log10 pvalue")

# GO富集分析柱状图，柱子颜色变化依据p.adjust值
ggplot(ego_down_sig,
  aes(x = Description, y = pvalue, fill = p.adjust)) +
  scale_fill_gradient(low = "red",high = "blue")+
  geom_col() +
  coord_flip() +
  theme_classic() +
  facet_grid(ONTOLOGY~., scales = "free") +
  xlab("Down regulated")+
  ylab("-log10 pvalue")

# GO富集分析气泡图
ggplot(ego_down_sig,
  aes(x = GeneRatio, y = Description, color =p.adjust, size = Count)) +
  geom_point()+
  scale_size(range = c(2,8))+
  scale_color_gradient(low = "red",high = "blue")+
  theme_classic() +
  facet_grid(ONTOLOGY~., scales = "free") +
  xlab("Gene ratio")