这个得看你用的什么包分析的结果,从你给的表格上来看
Term Ont N Up Down P.Up P.Down
分别为:GO ID,GO 描述,这个GO Term中的注释的基因个数,其中上调的基因个数,其中下调的基因个数,上调的基因的富集显著性p值,下调的基因富集显著性p值
qlf <- glmQLFTest(fit, coef=2)
go <- goana(qlf)
topGO(go,ont="BP",sort="Up", n=10)
keg <- kegga(qlf)
topKEGG(keg)
Term Ont N Up Down P.Up P.Down
GO:0006839 mitochondrial transport BP 166 119 27 8.868681e-05 0.9601505
GO:0098586 cellular response to virus BP 31 27 3 3.853013e-04 0.9747580
GO:0090503 RNA phosphodiester bond hydrolysis, exonucleolytic BP 13 13 0 7.301538e-04 1.0000000
GO:0010951 negative regulation of endopeptidase activity BP 166 115 30 1.009929e-03 0.8740812
GO:0003215 cardiac right ventricle morphogenesis BP 17 16 1 1.077424e-03 0.9832600
GO:0039528 cytoplasmic pattern recognition receptor signaling pathway in response to virus BP 12 12 0 1.273176e-03 1.0000000
GO:0044764 multi-organism cellular process BP 28 24 3 1.365170e-03 0.9566019
GO:0010466 negative regulation of peptidase activity BP 170 116 31 2.257038e-03 0.8650625
GO:0042982 amyloid precursor protein metabolic process BP 33 27 2 2.769479e-03 0.9964603
GO:0007006 mitochondrial membrane organization BP 81 59 10 2.847759e-03 0.9875908