一步运行

  1. data_hyper <- runHyper(data_deg,dir = "hyper_out",
  2.                        GO = TRUE,
  3.                        KEGG = TRUE)

分解动作

富集分析运算

  1. ## step only resolve
  2. data_hyper <- hyperResolve(
  3.   data_deg, 
  4.   GO = FALSE, 
  5.   KEGG = TRUE)

结果输出

  1. ## step summary results
  2. hyperSummary(
  3.   data_hyper, 
  4.   dir = "hyper_out", 
  5.   prefix = "3-runHyper", 
  6.   top = 10)

衍生工具

  1. enrichGO2(
  2. gene,
  3. TERM2GENE,
  4. TERM2NAME = NA,
  5. organism = "UNKNOW",
  6. keyType = "SYMBOL",
  7. ont = "ALL",
  8. pvalueCutoff = 0.05,
  9. pAdjustMethod = "BH",
  10. universe,
  11. qvalueCutoff = 0.2,
  12. minGSSize = 10,
  13. maxGSSize = 500,
  14. pool = FALSE
  15. )

可视化

适用于GO的柱状图

  1. gob <- hyperRes(data_hyper)[["hyperGO_res"]][["limma"]][["Up"]]
  2. p_hypergbar <- enrichBar(gob, top = 10, plot_title = "limma")
  3. ggplot2::ggsave(p_hypergbar, filename = "hyper_go_bar.pdf", width = 3000, height = 3600, units = "px", limitsize = FALSE, device = cairo_pdf, dpi = 300)

image.png

通用柱状图

  1. eob <- hyperRes(data_hyper)[["hyperKEGG_res"]][["limma"]][["Up"]]
  2. p_hyperebar <- hyperBar(eob, top = 10)
  3. ggplot2::ggsave(p_hyperbar, filename = "hyper_kegg_bar.pdf", width = 2100, height = 2400, units = "px", limitsize = FALSE, device = cairo_pdf)

image.png

圈图

  1. ## how many Common GO IDs among limma edgeR EDSeq2 merge
  2. dat_d <- modelEnrich(data_hyper, dataBase = "KEGG", orderBy = "pvalue", head = 10)[["diff"]]
  3. cc_file_name <- "KEGG_compareEnrichCircle.pdf"
  4. compareEnrichCircle(result_g = dat_d, filename = cc_file_name, mar = c(8, 0, 0, 19))

image.png