vignettes/Functional_Enrichment_Analysis.R

In wulingyun/CopTea: Combinatorial OPTimization based Enrichment Analysis

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library(CopTea)
options(scipen=0)

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load("GO_BP_300.RData")

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dim(annotation)

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load("PPI.RData")	

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dim(adj_matrix)

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load("active_gene.RData")

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True_Categories <- c("GO:0019614", "GO:1903249", "GO:2000506", "GO:0015985", "GO:0071962")

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Enriched_Categories <- netgen(annotation, adj_matrix, active_gene,
                              p1 = 0.8, p2 = 0.1, q = 0.001, alpha = 5, trace=TRUE)

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Enriched_Categories

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setdiff(True_Categories, Enriched_Categories[,1])

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p1 <- c(0.5, 0.8)
p2 <- c(0.1, 0.3)
q  <- 0.001
Enriched_Categories <- netgen(annotation, adj_matrix, active_gene,
                              p1, p2, q, alpha = 3, trace=FALSE)

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Enriched_Categories$Term_combined_pvalue

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Enriched_Categories$mix_result[which.min(Enriched_Categories$Term_combined_pvalue)]

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library(CopTea)
options(scipen=0)

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load("GO_BP_300.RData")
load("active_gene.RData")

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True_Categories <- c("GO:0019614", "GO:1903249", "GO:2000506", "GO:0015985", "GO:0071962")

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sum(active_gene %in% rownames(annotation))

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Enrich_result <- CEA(annotation, active_gene, d = 0, times = 5, trace = TRUE)

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names(Enrich_result)

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Enrich_result$category[1:5]

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Enrich_result$p.values[1:5]
Enrich_result$coverage[1:5]

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setdiff(True_Categories, Enrich_result$category[[1]])

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Enrich_result <- CEA(annotation, active_gene, d = 1, times = 500, trace = FALSE)

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Enrich_result$p.values[1]