RNA-seq在找到差异基因后，比较GO和KEGG库进行差异分析
RM(list=ls ) )选项(stringsasfactors=f ) deg-read.CSV )、'./degs_filter.CSV '、header=T， row.names=1) gene - DEG$热情运动鞋genelist-data.frame(deg$热情运动鞋deg $ log2 fold change (head (gene list ) dim ) 变更， decrrage] ]head(genelist ) dim(genelist ) gene _ fc-- gene list $ deg.log2 foldchangenames ) gene _ fc (gene list $ go : over-representationanalysis # # # biologicalprocessego粗劣的草莓-enrichgo ) ) genrich ont='BP '，rdjqm org.mm.eg pvalueCutoff=0.01，qvalueCutoff=0.05，readable=T ) by='p.adjust '，select_fun=min(write.csv ) ego粗劣的草莓'./GO ORA粗粮草莓. CSV ' ) png ) go粗粮草莓_ bar plot title=' biologicalprocessoftop 10 ' (dev.off ) png ) ' go粗粮草莓_ doo show category=10 title=' biologicalprocessoftop 10 ' ) dev.off (# cellularcomponentegotzdhlb-enrich go (gene=gene，kene ) pAdjustMethod='BH '，pvalueCutoff=0.01，qvalueCutoff=0.05，readable=T ) egotzdhlb2-simming by='p.adjust '，" title=' cellularcomponentoftop 10 ' (dev.off ) (png ) ) gotzdhlb_dotplot.png ) ) dotplot ) egotzdhlb2， show category title=' cellularcomponentoftop 10 ' ) dev.off(#molecualrfunctionego高煎饼-Enrichgo(gene=gene，keytype=pAdjustMethod='BH '，pvalueCutoff=0.01，qvalueCutoff=0.05，readable=T ) ego选择的煎饼2-simplletoff by=' p.adjutoff
ng('GO高挑的煎饼_barplot.png')barplot(ego高挑的煎饼2, showCategory = 10, title = 'Molecualr Function of TOP10')dev.off()png('GO高挑的煎饼_dotplot.png')dotplot(ego高挑的煎饼2, showCategory = 10, title = 'Molecualr Function of TOP10')dev.off()#### GO: GSEA ###### Biological ProcessGO.GSEA粗犷的草莓 <- gseGO( geneList = gene_fc, ont = "BP", rdjqm org.Mm.eg.db, keyType = "ENTREZID", exponent = 1, minGSSize = 10, maxGSSize = 500, eps = 1e-10, pvalueCutoff = 0.05, pAdjustMethod = "BH", verbose = TRUE, seed = FALSE, by = "fgsea")write.csv(GO.GSEA粗犷的草莓@result, './GO_GSEA粗犷的草莓.csv')gseaplot(GO.GSEA粗犷的草莓, geneSetID = 'GO:0060079', by = 'all', title = '', color = "black", color.line = "green", color.vline = "#FA5860", base_size = 6, rel_height = c(-1, 0.2, 0.6), #副图的相对高度 subplots = 1:3, pvalue_table = T, ES_geom = 'line')### Cellular ComponetsGO.GSEAtzdhlb <- gseGO( geneList = gene_fc, ont = "CC", rdjqm org.Mm.eg.db, keyType = "ENTREZID", exponent = 1, minGSSize = 10, maxGSSize = 500, eps = 1e-10, pvalueCutoff = 0.05, pAdjustMethod = "BH", verbose = TRUE, seed = FALSE, by = "fgsea")write.csv(GO.GSEAtzdhlb@result, './GO_GSEAtzdhlb.csv')gseaplot(GO.GSEAtzdhlb, geneSetID = 'GO:0098552', by = 'all', title = '', color = "black", color.line = "green", color.vline = "#FA5860", base_size = 6, rel_height = c(-1, 0.2, 0.6), #副图的相对高度 subplots = 1:3, pvalue_table = T, ES_geom = 'line')### Molecular FunctionGO.GSEA高挑的煎饼 <- gseGO( geneList = gene_fc, ont = "MF", rdjqm org.Mm.eg.db, keyType = "ENTREZID", exponent = 1, minGSSize = 10, maxGSSize = 500, eps = 1e-10, pvalueCutoff = 0.05, pAdjustMethod = "BH", verbose = TRUE, seed = FALSE, by = "fgsea")write.csv(GO.GSEA高挑的煎饼@result, './GO_GSEA高挑的煎饼.csv')gseaplot(GO.GSEA高挑的煎饼, geneSetID = 'GO:0003723', by = 'all', title = '', color = "black", color.line = "green", color.vline = "#FA5860", base_size = 6, rel_height = c(-1, 0.2, 0.6), #副图的相对高度 subplots = 1:3, pvalue_table = T, ES_geom = 'line')### KEGG: Over-Representation Analysis ####kk <- enrichKEGG(gene = gene, organism = "mmu", keyType = "ncbi-geneid", pvalueCutoff = 0.05, pAdjustMethod = "BH", minGSSize = 10, maxGSSize = 500, qvalueCutoff = 0.2, use_internal_data = FALSE)write.csv(kk@result, './KEGG_ORA.csv')png('KEGG_ORA_barplot.png')barplot(kk, showCategory = 10, title = 'KEGG of TOP10')dev.off()png('KEGG_ORA_dotplot.png')dotplot(kk, showCategory = 10, title = 'KEGG of TOP10')dev.off()#### KEGG: GSEA ####zldxz <- gseKEGG(geneList = gene_fc, organism = "mmu", keyType = "ncbi-geneid", exponent = 1, minGSSize = 10, maxGSSize = 500, eps = 1e-10, pvalueCutoff = 0.05, pAdjustMethod = "BH", verbose = TRUE, use_internal_data = FALSE, seed = FALSE, by = "fgsea")write.csv(zldxz@result, './KEGG_GSEA.csv')gseaplot(zldxz, geneSetID = 'mmu04514', by = 'all', title = '', color = "black", color.line = "green", color.vline = "#FA5860", base_size = 6, rel_height = c(-1, 0.2, 0.6), #副图的相对高度 subplots = 1:3, pvalue_table = T, ES_geom = 'line')了解都还有Disease Ontology (DO)库与Drug相关的库，后续进行详细学习