# 1.1.Set up a directory
my_dir <- "/Users/francescavitali/Box Sync/Class_RNAseq_FV/ClassMaterial/data"
library(ggplot2)
library(dplyr)
library(clusterProfiler)
library(org.Mm.eg.db)
#set a theme
theme_set(theme_bw() + theme(legend.title = element_blank(), panel.grid.minor = element_blank()))
Load DATA from Differential Expression Analyses (DE)
# 1.2 Read file and check the data
MyData <-read.csv( file.path(my_dir, '/DEGs_ Raloxifene_Vehicle .txt'), sep="\t" )
nameComparison="Raloxifene_Vehicle"
summary(MyData)
## genes logFC AveExpr t
## Length:10880 Min. :-1.82987 Min. : 0.1153 Min. :-8.967941
## Class :character 1st Qu.:-0.19842 1st Qu.: 3.9153 1st Qu.:-2.004450
## Mode :character Median :-0.02408 Median : 5.2316 Median :-0.259552
## Mean :-0.01432 Mean : 5.2255 Mean :-0.002511
## 3rd Qu.: 0.16788 3rd Qu.: 6.4954 3rd Qu.: 1.842697
## Max. : 1.65940 Max. :12.1890 Max. : 9.402697
## P.Value adj.P.Val B
## Min. :0.000000 Min. :0.000099 Min. :-6.933
## 1st Qu.:0.004172 1st Qu.:0.016682 1st Qu.:-5.587
## Median :0.069772 Median :0.139531 Median :-4.471
## Mean :0.222213 Mean :0.279360 Mean :-3.529
## 3rd Qu.:0.376657 3rd Qu.:0.502174 3rd Qu.:-2.100
## Max. :0.998683 Max. :0.998683 Max. : 9.034
# 1.3 Check the number of unique genes
length(unique(MyData$genes))
## [1] 10880
Point 1 - Pathway analysis - run GSEA
# Gene set enrichment analysis (GSEA) (also called functional enrichment analysis or pathway enrichment analysis) is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins, and may have an association with different phenotypes. It requires as input a list of genes along with the realtive fold change. As it accounts for the expression values where if a gene set falls at either the top (over-expressed) or bottom (under-expressed), it is thought to be related to the phenotypic differences.
#1.4 Create a data frame containing the genes ordered by the relative values of Fold Change
# create gene list with fold change values
gene_list <- MyData$logFC
# name the vector rows with gene names
names(gene_list) <- MyData$genes
# remove duplicates if exists
gene_list = gene_list[!duplicated(names(gene_list))]
# sort the list in decreasing order (required for GSEA to run with clusterProfiler package)
gene_list = sort(gene_list, decreasing = TRUE)
gse <- gseGO(geneList=gene_list,
ont ="BP",
keyType = "SYMBOL",
minGSSize = 20,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = TRUE,
OrgDb = org.Mm.eg.db,
pAdjustMethod = "BH")
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
gse_summ <- data.frame(gse)
summary(gse_summ)
## ID Description setSize enrichmentScore
## Length:87 Length:87 Min. : 20.0 Min. :-0.61554
## Class :character Class :character 1st Qu.: 82.5 1st Qu.:-0.33184
## Mode :character Mode :character Median :139.0 Median : 0.26567
## Mean :182.9 Mean : 0.03466
## 3rd Qu.:292.5 3rd Qu.: 0.38485
## Max. :499.0 Max. : 0.67204
## NES pvalue p.adjust qvalue
## Min. :-2.0418 Min. :1.400e-08 Min. :4.798e-05 Min. :4.116e-05
## 1st Qu.:-1.6941 1st Qu.:7.634e-05 1st Qu.:1.159e-02 1st Qu.:9.946e-03
## Median : 1.5103 Median :2.358e-04 Median :1.831e-02 Median :1.571e-02
## Mean : 0.1765 Mean :4.110e-04 Mean :2.307e-02 Mean :1.979e-02
## 3rd Qu.: 1.8163 3rd Qu.:7.043e-04 3rd Qu.:3.674e-02 3rd Qu.:3.153e-02
## Max. : 2.3261 Max. :1.242e-03 Max. :4.878e-02 Max. :4.185e-02
## rank leading_edge core_enrichment
## Min. :1178 Length:87 Length:87
## 1st Qu.:1990 Class :character Class :character
## Median :2527 Mode :character Mode :character
## Mean :2444
## 3rd Qu.:2959
## Max. :3660
# 1.5 Save the results in a csv file
write.csv(gse_summ, file = file.path(my_dir,"results_gsea",paste(nameComparison,"_gsea.csv")), row.names=FALSE)
# 1.6 Create a dotplot with the results
pdf(file.path(my_dir,"results_GSEA",paste(nameComparison,"_gsea.pdf")),
height=10, width = 10)
print(dotplot(gse, showCategory=400, split=".sign",
label_format = function(x) stringr::str_wrap(x, width=100),font.size=8) + facet_grid(.~.sign))
dev.off()
## quartz_off_screen
## 2
graphics.off()
# 1.7 In some cases GSEA analyses can result in hundreds enriched pathways. However, lots of them might be similar, subset of others or redundant. There is the option of filtering pathways that are similar using the simplify function.
gse_simply<-simplify(gse, cutoff=0.7, by='p.adjust', select_fun=min)
pdf(file.path(my_dir,"results_GSEA",paste(nameComparison,"_gsea_simplified.pdf")),
height=7, width = 7)
print(dotplot(gse_simply, showCategory=400, split=".sign",
label_format = function(x) stringr::str_wrap(x, width=100),font.size=8) + facet_grid(.~.sign))
dev.off()
## quartz_off_screen
## 2
graphics.off()
Point 2 - Pathway analysis on a list of genes
# Enrichment analysis of a list of Genes
# 2.1 Select DEGs that are with pvalue<0.05
adj_p_15FC=MyData[which(MyData["P.Value"]<=0.05 ),]
#adj_p_15FC=MyData[which(MyData["P.Value"]<=0.05 & (MyData["logFC"]<=-1.5|MyData["logFC"]>=1.5)),]
# 2.2 Convert the list of genes from Gene Symbol into Entrez ID
adj_p_15FC.entrez <- bitr(adj_p_15FC$genes, fromType = "SYMBOL",
toType = c("ENTREZID"),
OrgDb = org.Mm.eg.db)
## 'select()' returned 1:1 mapping between keys and columns
## Warning in bitr(adj_p_15FC$genes, fromType = "SYMBOL", toType = c("ENTREZID"),
## : 8.74% of input gene IDs are fail to map...
# 2.3 Enrichement analyses of the list of Genes
DEGs_enrichment <- enrichGO(gene= adj_p_15FC.entrez$ENTREZID,
OrgDb = org.Mm.eg.db,
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05,
readable = TRUE,
minGSSize=15,
maxGSSize=500)
head(DEGs_enrichment)
## ID
## GO:0050808 GO:0050808
## GO:0007409 GO:0007409
## GO:0006397 GO:0006397
## GO:0043161 GO:0043161
## GO:1903829 GO:1903829
## GO:0016358 GO:0016358
## Description
## GO:0050808 synapse organization
## GO:0007409 axonogenesis
## GO:0006397 mRNA processing
## GO:0043161 proteasome-mediated ubiquitin-dependent protein catabolic process
## GO:1903829 positive regulation of protein localization
## GO:0016358 dendrite development
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0050808 233/4445 494/28814 2.748868e-63 1.426113e-59 5.755262e-60
## GO:0007409 206/4445 493/28814 1.244984e-45 3.229488e-42 1.303301e-42
## GO:0006397 200/4445 472/28814 1.959690e-45 3.388958e-42 1.367657e-42
## GO:0043161 188/4445 430/28814 3.840698e-45 4.981385e-42 2.010302e-42
## GO:1903829 201/4445 491/28814 6.199341e-43 6.432436e-40 2.595893e-40
## GO:0016358 150/4445 317/28814 2.943346e-41 2.545013e-38 1.027073e-38
## geneID
## GO:0050808 Rock2/Ube3a/Ptn/Gabra1/Zdhhc2/Lrrtm2/Ntng2/Vps35/Stau2/Gpm6a/Marcks/Ywhaz/Dgkz/Pten/Fmr1/Slitrk4/Srcin1/Dnm1l/Cntn2/Ube2v2/Dctn1/Slc8a2/Slc25a46/Dhx36/Dip2a/Pafah1b1/Gabra2/Lrrc4b/Cdc42/Ephb2/Eef2k/Cacnb4/Arhgef9/Abi2/Pak3/Wasl/Homer1/Apbb1/Dab2ip/Lrrn3/Shank3/Pgrmc1/Ppfia2/Bdnf/Dvl1/Fgf13/Nlgn1/Actb/Opa1/Cacnb3/Nrxn2/Dgkb/Nrcam/Sptbn2/Gsk3b/Tuba1a/Itpka/Add2/Palm/Hnrnpk/Il1rapl1/Pdgfb/Bhlhb9/C1ql1/App/Adnp/Syngap1/Dlgap3/Mfn2/Picalm/Flrt3/Lrfn1/Slc7a11/Chrnb2/Bsn/Actr2/Gabrg2/Wnt7b/Ptprs/Baiap2/Nrg2/Grin1/Tmeff1/Ptprf/Sez6l/Shank1/Ephb1/Dbn1/Bcan/Trem2/Lhfpl4/Nlgn2/Cacna2d2/Pak1/Actr3/Sema3e/Arhgap39/Prrt1/Tsc2/Cdk5r1/Hspa8/Fyn/Rheb/Nos1ap/Ntrk1/Lrrc24/Shisa6/Dlg4/Ppfia4/Neurod2/Elfn1/Pcdh8/Cntnap1/Ntn1/Pcdhgc3/Unc13a/Sez6l2/Synpo/Nedd4/Ntng1/Prmt3/Chchd10/Lrrn1/Sema4d/Ndrg1/Nptn/Mapk14/Lrrtm4/Apoe/Lrtm2/Slitrk1/Abl1/Nfasc/Snta1/Arf4/Cdh9/Shank2/Epha5/Lrrtm3/Glrb/Tubb5/Pfn1/Ephb3/Slitrk2/Sdcbp/Snx27/Cacna1a/Cabp1/Prkca/Sdk2/Clstn3/C1ql3/Taok2/Agrn/Kalrn/Fzd9/Caprin1/Pclo/Plxnb2/Vstm5/Nptx1/Zdhhc15/Adam10/Mdga2/Amigo3/Pin1/Slitrk3/Cript/Pfn2/Gabrb2/Iqsec3/Cbln4/Lrfn3/Lgi2/Sorbs2/Mfn1/Ctnnb1/Rims3/Nrxn3/Lrrc4c/Wasf2/Rhoa/Erbb2/Camk2b/Clstn1/Drd1/Gabrb3/Crkl/Igsf21/Tlr2/Myo5a/Camkv/Rer1/Ctnnd2/Slitrk5/Dok7/Magi2/Gap43/Ache/Dbnl/Cask/Rest/Cacnb1/Ppfia3/Rims4/Nefl/Prnp/Syn1/Sncb/Htr1a/Lgmn/Shisa7/Plxnd1/Lamb2/Mecp2/Col4a1/Gphn/Anapc2/Cacnb2/Igf1r/Pik3r1/Sparcl1/Tsc1/Cntnap4/Sez6/Ppfibp1/Setd5/Cnksr2/Drp2/Ntrk2/Lrrk2/Gripap1/Rph3a
## GO:0007409 Mbp/Nrn1/Mag/Aatk/Cnp/Ntng2/Metrn/Numbl/Usp9x/Top2b/Hsp90aa1/Rgma/Etv1/Pten/Notch1/Zdhhc17/Slitrk4/Lrp1/Aplp1/Rab21/Cntn2/Sema6c/Map2k2/Rab11a/Slc25a46/Usp33/Pafah1b1/Ephb2/Unc5b/Alcam/Omg/Pak3/Sema5b/Apbb1/Dcx/Llgl1/Atl1/Rufy3/Bdnf/Dvl1/Fgf13/Rtn4/Actb/Nrcam/Gsk3b/Scn1b/Pip5k1c/B4galt6/Sema3b/Adcy1/Dnm2/Mapk8ip3/Dclk1/App/Adnp/Syngap1/Picalm/Stk11/Flrt3/Itga4/Lgi1/Cdk5r2/Bcl11b/Chrnb2/Sptbn4/Sema6b/Trim46/Map3k13/Wnt7b/Tbce/Ulk1/Ptprs/Grin1/Ephb1/Rhog/Dbn1/Limk1/Rab10/Olfm1/Sema7a/Slit3/Pak1/Map1s/Kif5a/Raph1/Actr3/Sema3e/Tsc2/Cdk5r1/Ntrk1/Ntn3/Atg7/Rnd2/Mt3/Smo/Unc5c/Plxnb3/Robo2/Atp5g1/Cntnap1/Ephb6/Bsg/Ntn1/Vegfa/Map6/Rtn4r/Ntng1/Cdkl3/Clasp2/Itgb1/Sema4d/Fzd3/Nptn/Unc5a/Bmpr2/Eif2b2/Golga2/Zic2/Sema4b/Apoe/Lrtm2/Notch3/Kif13b/Srf/Sema3c/Slitrk1/Abl1/Nfasc/Nr4a2/Ccr5/Nefh/Epha5/Ephb3/Slitrk2/Arhgef25/Cacna1a/Prkca/Taok2/Agrn/Kalrn/Ythdf1/Celsr3/Plxnb2/Nptx1/Ext1/Dscaml1/Tubb3/Kif5b/Mark2/B3gnt2/Ptprz1/Fstl4/Slitrk3/Bhlhe22/Cntn6/Slit1/Crmp1/Trak1/Mgll/Tenm2/Map1a/Macf1/Lrrc4c/Cdh11/Erbb2/Dpysl2/Tbr1/Slit2/Xk/Fgfr3/Rtn4rl1/Lgr4/Sema4c/Thy1/Gdi1/Ifrd1/Efna2/Slitrk5/Gap43/Ache/Dbnl/Epha10/Plxna3/Sema5a/Sema4f/Map1b/Nefl/Ulk2/Ece1/Flot1/Plxnd1/Tctn1/Lamb2/Ptpn11/Map2/Brsk1/Anapc2/Tubb2b/Igf1r/Cck/Spg20/Ntrk2/Apc/Adarb1/Nefm/Rpl4
## GO:0006397 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
## GO:0043161 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
## GO:1903829 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
## GO:0016358 Rock2/Ube3a/Ptn/Fbxo31/Stau2/Numbl/Uba6/Marcks/Camk2a/Acsl4/Pten/Fmr1/Srcin1/Dnm1l/Rab21/Slc25a46/Dhx36/Dip2a/Pafah1b1/Cdc42/Tmem106b/Ephb2/Eef2k/Abi2/Camsap2/Pak3/Wasl/Crtc1/Dab2ip/Dcx/Shank3/Ppfia2/Bdnf/Dvl1/Nlgn1/Opa1/Celsr2/Ppp1r9b/Gsk3b/Itpka/Mapk8/Palm/Hnrnpk/Il1rapl1/Bhlhb9/Dclk1/App/Syngap1/Mfn2/Picalm/Stk11/Ss18l1/Abi1/Chrnb2/Actr2/Ptprs/Gsk3a/Baiap2/Actl6b/Carm1/Grin1/Ptprf/Shank1/Ephb1/Dbn1/Nlgn2/Mef2a/Pak1/Map1s/Actr3/Tsc2/Cdk5r1/Fyn/Ntn3/Dlg4/Slc12a5/Nr3c1/Ntn1/Sh3glb1/Mink1/Map6/Pacsin1/Nedd4/Cdkl3/Prmt3/Itgb1/Sema4d/Git1/Apoe/Abl1/Arf4/Fbxw8/Epha5/Sarm1/Hdac2/Ephb3/Plk2/Cacna1a/Camk1d/Taok2/Kalrn/Caprin1/Ankrd27/Sdc2/Zdhhc15/Adam10/Mpdz/Prex2/Pak4/Grin3a/Bbs4/Ptprz1/Fstl4/Mtor/Trak1/Elavl4/Map1a/Trpc6/Mfn1/Rhoa/Camk2b/Cit/Crkl/Pias2/Id1/Llph/Ppp3ca/Ctnnd2/Slitrk5/Ache/Dbnl/Cask/Rapgef2/Slc11a2/Met/Map1b/Gorasp1/Gpr37/Map2/Mecp2/Anapc2/Nedd4l/Ywhah/Kidins220/Sez6/Ntrk2/Asap1/Rere/Lrrk2/Nsmf
## Count
## GO:0050808 233
## GO:0007409 206
## GO:0006397 200
## GO:0043161 188
## GO:1903829 201
## GO:0016358 150