Commit f7842664 authored by Matt Rogon's avatar Matt Rogon

Initial commit

parent 4c5cd136
......@@ -120,10 +120,68 @@ union_network_results= getBM(attributes = c("mgi_symbol","entrezgene_id", "ensem
mart = ensembl)
# - using the cluster identifier (expected_up, expected_down, transient early, transient late, intermediate, shoulder)
# - split the network into subnetworks (how well connected are they?)
# - on each cluster - run enrichment analysis against KEGG and Reactome (do a multicluster analysis rather than investigating them individually)
# - Extract Notch pathway
# - run enrichment analysis against KEGG and Reactome
# - Extract top pathway
# clusterProfiler
library(clusterProfiler)
library(pathview)
library(org.Mm.eg.db)
library(DOSE)
# extract entrez id's from the annotation frame 'results' created with BioMart earlier
list_Entrez <- as.list(union_network_results$entrezgene_id)
kk <- enrichKEGG(list_Entrez, organism="mouse", pvalueCutoff=0.05, pAdjustMethod="BH", qvalueCutoff=0.1)
enrichKegg_results <- as.data.frame(kk)
mmu03030 <- pathview(gene.data = as.character(list_Entrez), pathway.id = "mmu03030",species = "mouse")
# repeat for gene ontology
kg <- enrichGO(list_Entrez, OrgDb = "org.Hs.eg.db", ont="BP", pvalueCutoff=0.05, pAdjustMethod="BH", qvalueCutoff=0.1, minGSSize = 5)
head(summary(kg))
summary(kg)
library(enrichplot)
# cnetplot depicts the associations of genes with biological concepts
# i.e. gene to term (GO/pathway)
cnetplot(kk)
# relationships between terms
emapplot(kk)
# The upsetplot is an alternative to cnetplot for visualizing the complex association
# between genes and gene sets. It emphasizes the gene overlapping among different gene
# sets.
upsetplot(kk)
heatplot(kk)
dotplot(kk)
barplot(kk, showCategory = 15)
dotplot(kg)
barplot(kg, showCategory = 15)
write.table(kk, file="kegg_on_hits.txt", row.names = F, quote = FALSE, sep="\t")
write.table(kg, file="go_on_hits.txt", row.names = F, quote = FALSE, sep="\t")
result <- summary(kk)
write.table(result, file="KEGG_enrichment_2019.txt", row.names = F, quote = FALSE, sep="\t")
# export entrez ID's for the hit list (will be used for filtering in Cytoscape)
lapply(list_Entrez, write, "entrez_list_hits_2019.txt", append=TRUE, ncolumns=1000)
- transfer the resulting merged and notch networks to Cytoscape
- use cyREST
......
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