GO enrichment analysis using DSeq2

library(DESeq2)
Counts <- read.delim("../count_table.csv", header = TRUE, row.names = 1, sep = ",")
Counts <- Counts[which(rowSums(Counts) > 0),]
condition <- factor(c("C","C","C","C", "S","S","S","S"))
coldata <- data.frame(row.names = colnames(Counts), condition)
dds <- DESeqDataSetFromMatrix(countData = Counts, colData = coldata, design = ~condition)
dds <- DESeq(dds)
res <- results(dds, contrast = c("condition", "S", "C"))
sigs <- na.omit(res)
sigs <- sigs[sigs$padj < 0.05 & sigs$baseMean > 50,]

sigs

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("clusterProfiler")

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("AnnotationDbi")

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("org.Hs.eg.db")

library(clusterProfiler)
library(org.Hs.eg.db)
library(AnnotationDbi)

genes_to_test <- rownames(sigs[sigs$log2FoldChange > 0.5,])

GO_results <- enrichGO(gene = genes_to_test, OrgDb = "org.Hs.eg.db", keyType = "ENSEMBL", ont = "BP")

as.data.frame(GO_results)

fit <- plot(barplot(GO_results, showCategory = 15))

png("out.png", res = 250, width = 1400, height = 1800)
print(fit)
dev.off()

fit