一步运行
data_hyper <- runHyper(data_deg,dir = "hyper_out",
GO = TRUE,
KEGG = TRUE)
分解动作
富集分析运算
## step only resolve
data_hyper <- hyperResolve(
data_deg,
GO = FALSE,
KEGG = TRUE)
结果输出
## step summary results
hyperSummary(
data_hyper,
dir = "hyper_out",
prefix = "3-runHyper",
top = 10)
衍生工具
enrichGO2(
gene,
TERM2GENE,
TERM2NAME = NA,
organism = "UNKNOW",
keyType = "SYMBOL",
ont = "ALL",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe,
qvalueCutoff = 0.2,
minGSSize = 10,
maxGSSize = 500,
pool = FALSE
)
可视化
适用于GO的柱状图
gob <- hyperRes(data_hyper)[["hyperGO_res"]][["limma"]][["Up"]]
p_hypergbar <- enrichBar(gob, top = 10, plot_title = "limma")
ggplot2::ggsave(p_hypergbar, filename = "hyper_go_bar.pdf", width = 3000, height = 3600, units = "px", limitsize = FALSE, device = cairo_pdf, dpi = 300)
通用柱状图
eob <- hyperRes(data_hyper)[["hyperKEGG_res"]][["limma"]][["Up"]]
p_hyperebar <- hyperBar(eob, top = 10)
ggplot2::ggsave(p_hyperbar, filename = "hyper_kegg_bar.pdf", width = 2100, height = 2400, units = "px", limitsize = FALSE, device = cairo_pdf)
圈图
## how many Common GO IDs among limma edgeR EDSeq2 merge
dat_d <- modelEnrich(data_hyper, dataBase = "KEGG", orderBy = "pvalue", head = 10)[["diff"]]
cc_file_name <- "KEGG_compareEnrichCircle.pdf"
compareEnrichCircle(result_g = dat_d, filename = cc_file_name, mar = c(8, 0, 0, 19))
