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cummeRbund-golplots-igraph.R
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## CummeRbund-Network-Analysis-and-GOplots
## GOplots for Significantly differentially expressed features: method 1
############################################################################
# arg-eval ####
############################################################################
args = commandArgs(TRUE)
for (i in 1:length(args)) {
eval(parse(text=args[[i]]))}
# These arguments are passed in on the command line via launchR.eachDiffDir.sh --> args:
# diffDir=\"${diffDir}\" inDir=\"${INPUT}/${diffDir}\" outDir=\"${OUTPUT}/${diffDir}\" under=\"${under}\"
# over=\"${over}\" Rplots=\"$\{OUTPUT}/${diffDir}/${R_PLOTS}\" FPKMmatrix=\"${OUTPUT}/${diffDir}/${FPKM_MATRIX}\"
# DiffTable=\"${OUTPUT}/${diffDir}/${DIFF_TABLE}\"
############################################################################
# Library-load ####
############################################################################
library(cummeRbund);library(edgeR);library(limma)
library(keggorthology);library(KEGGgraph);library(KEGG.db)
library(gage);library(gageData);library(GSEABase)
library(org.Hs.eg.db);library(biomaRt);library(reactome.db)
library(ReactomePA);library(clusterProfiler);library(DOSE)
library(igraph); library(pvclust); library(gplots)
library(GOstats);library(topGO);library(GO.db)
library(STRINGdb);library(pathview)
library(Hmisc);library(ellipse)
source("http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/my.colorFct.R")
source("http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/dendroCol.R")
# Import tree coloring function.
############################################################################
# readCufflinks-Initialization ####
############################################################################
#gtfFile<-file.path("/media/drew/67a0cf75-b688-4ee0-ac64-b8bbbb27d0c6/home/drew/umb_triley/urine1/cuffd")
#cuff <- readCufflinks(dir=myDir,gtfFile=gtfFile,genome="hg19",rebuild=T)
cuff <- readCufflinks(dir='.',genome="hg38",rebuild=T)
cuff
rna.seq.sum<-runInfo(cuff)
RNAseq.run.info<-rna.seq.sum[1,2]
RNAseq.run.info
replicates.info<-replicates(cuff)
replicates.info
groups<-factor(replicates.info$sample_name)
samples<-replicates.info$rep_name
groups
samples
over="LUTS"
under="CTRL"
############################################################################
# geneFPKMexpression-matrix ####
############################################################################
gene_exp.diff<-diffData(genes(cuff))
head(gene_exp.diff)
dim(gene_exp.diff)
g.rep.matrix<-repFpkmMatrix(genes(cuff))
head(g.rep.matrix);dim(g.rep.matrix)
iso.rep.matrix<-as.data.frame(repFpkmMatrix(isoforms(cuff)))
row.names(iso.rep.matrix)->tracking_id
head(iso.rep.matrix)
gene.rep.ma<-cbind(tracking_id, iso.rep.matrix)
head(gene.rep.ma)
gene.xloc.matrix<-featureNames(isoforms(cuff))
head(gene.xloc.matrix)
gene.list<-getGenes(cuff,geneId = gene.xloc.matrix)
gene.list
gene_annotation_data<-featureNames(gene.list@isoforms)
head(gene_annotation_data)
gene.features<-annotation(isoforms(cuff))
head(gene.features)
iso_exp.diff<-diffData(isoforms(cuff))
head(iso_exp.diff)
dim(iso_exp.diff)
############################################################################
# Sig-geneFPKMexpression-matrix ####
############################################################################
mySigGenes<-getSig(cuff,x=over,y=under,alpha=.05,level='genes')
head(mySigGenes)
length(mySigGenes)
sigGenes<-getGenes(cuff, mySigGenes)
length(sigGenes)
sig_genes_annot<-sigGenes@annotation
head(sig_genes_annot)
sig_genes_exp.diff<-diffData(sigGenes)
head(sig_genes_exp.diff);dim(sig_genes_exp.diff)
sig.genes_exp.diff<-as.data.frame(cbind(logFC=sig_genes_exp.diff$log2_fold_change,q_value=sig_genes_exp.diff$q_value),
row.names=sig_genes_exp.diff$gene_id)
head(sig.genes_exp.diff);dim(sig.genes_exp.diff)
gene.features<-gene.features[,c(1,2,4)]
head(gene.features)
g.exp.df<-as.data.frame(merge(x=gene.features, y=gene.rep.ma, by.x="isoform_id", by.y="tracking_id"))#,row.names = "gene_short_name")
head(g.exp.df)
G.exp.df<-as.data.frame(g.exp.df[,-c(1:2)],row.names = g.exp.df[,3])
g.rep.matrix<-G.exp.df[,-1]
head(g.rep.matrix)
dim(g.rep.matrix)
head(g.rep.matrix[unique(row.names(g.rep.matrix)),])
g.rep.ma<-g.rep.matrix[unique(row.names(g.rep.matrix)),]
head(g.rep.ma);dim(g.rep.ma)
g.rep.ma<-as.matrix(g.rep.ma)
over.group<-grep(pattern = over, x = colnames(g.rep.ma),ignore.case = T)
under.group<-grep(pattern = under, x = colnames(g.rep.ma),ignore.case = T)
g.under.matrix<-g.rep.ma[,under.group]
head(g.under.matrix)
dim(g.under.matrix)
g.over.matrix<-g.rep.ma[,over.group]
head(g.over.matrix)
dim(g.over.matrix)
sig_over_gene_data<-subset(sig_genes_exp.diff, (significant =='yes') & (log2_fold_change > 0))
head(sig_over_gene_data);dim(sig_over_gene_data)
sig_under_gene_data<-subset(sig_genes_exp.diff, (significant =='yes') & (log2_fold_change < 0))
head(sig_under_gene_data);dim(sig_under_gene_data)
head(g.rep.ma);dim(g.rep.ma)
dim(sig_genes_exp.diff)
sig.genes<-row.names(g.rep.ma) %in% row.names(sig.genes_exp.diff)
head(g.rep.ma[sig.genes,]);dim(g.rep.ma[sig.genes,])
s.g.rep.matrix<-g.rep.ma[sig.genes,]
g.under.genes<-row.names(g.rep.ma) %in% sig_under_gene_data$gene_id
g.u.rep.ma<-g.rep.ma[g.under.genes,]
head(g.u.rep.ma);dim(g.u.rep.ma)
g.over.genes<-row.names(g.rep.ma) %in% sig_over_gene_data$gene_id
g.o.rep.ma<-g.rep.ma[g.over.genes,]
head(g.o.rep.ma);dim(g.o.rep.ma)
s.g.over.matrix<-g.o.rep.ma[,over.group]
head(s.g.over.matrix);dim(s.g.over.matrix)
s.g.under.matrix<-g.u.rep.ma[,under.group]
head(s.g.under.matrix);dim(s.g.under.matrix)
############################################################################
# adj.matrix.graph-all-sig-genes ####
############################################################################
o.group<-grep(pattern = over, x = colnames(g.rep.matrix),ignore.case = T)
u.group<-grep(pattern = under, x = colnames(g.rep.matrix),ignore.case = T)
isexpr <- rowSums(cpm(g.count.matrix) > 1) >= 2
gse116969<-gse116969[isexpr,]
dim(gse116969)
sig.g.reps<-which(row.names(g.rep.matrix) %in% sigGenes@ids)
sig.g.rep.matrix<-as.matrix(g.rep.matrix[sig.g.reps,])
head(sig.g.rep.matrix)
dim(sig.g.rep.matrix)
min.samps<-(length(samples)/2)*.5
min.expr<-round(min.samps)
isexpr <- rowSums(cpm(sig.g.rep.matrix) > 1) >= min.expr | rowSums(cpm(sig.g.rep.matrix) > 1) >= min.expr
sig.g.rep.ma<-sig.g.rep.matrix[is.expr,]
is.o.expr <- rowSums(cpm(s.g.over.matrix) > 1) >= min.expr | rowSums(cpm(s.g.over.matrix) > 1) >= min.expr
s.g.o.rep.ma<-s.g.over.matrix[is.o.expr,]
head(s.g.o.rep.ma);dim(s.g.o.rep.ma)
is.u.expr <- rowSums(cpm(s.g.under.matrix) > 1) >= min.expr | rowSums(cpm(s.g.under.matrix) > 1) >= min.expr
s.g.u.rep.ma<-s.g.under.matrix[is.u.expr,]
head(s.g.u.rep.ma);dim(s.g.u.rep.ma)
############################################################################
# adj.matrix.graph-all-sig-genes ####
############################################################################
#Create a graph adjacency based on correlation distances between genes in pairwise fashion.
sig.graph <- graph.adjacency(as.matrix(as.dist(cor(t(s.g.rep.ma), method="pearson"))),
mode="undirected", weighted=TRUE, diag=FALSE)
#Colour negative correlation edges as blue
E(sig.graph)[which(E(sig.graph)$weight<0)]$color <- "darkblue"
#Colour positive correlation edges as red
E(sig.graph)[which(E(sig.graph)$weight>0)]$color <- "darkred"
#Convert edge weights to absolute values
E(sig.graph)$weight <- abs(E(sig.graph)$weight)
#Change arrow size
#For directed graphs only
E(sig.graph)$arrow.size <- 1.0
#Remove edges below absolute Pearson correlation 0.7
sig.graph <- delete_edges(sig.graph, E(sig.graph)[which(E(sig.graph)$weight<0.7)])
#Assign names to the graph vertices (optional)
V(sig.graph)$name <- V(sig.graph)$name
#Change shape of graph vertices
V(sig.graph)$shape <- "sphere"
#Change colour of graph vertices
V(sig.graph)$color <- "skyblue"
#Change colour of vertex frames
V(sig.graph)$vertex.frame.color <- "white"
#Scale the size of the vertices to be proportional to the level of expression of each gene represented by each vertex
#Multiple scaled vales by a factor of 10
scale01 <- function(x){(x-min(x))/(max(x)-min(x))}
vSizes <- (scale01(apply(s.g.rep.matrix, 1, mean)) + 1.0) * 10
#Amplify or decrease the width of the edges
edgeweights <- E(sig.graph)$weight * 2.0
#Convert the graph adjacency object into a minimum spanning tree based on Prim's algorithm
mst <- mst(sig.graph, algorithm="prim")
#Plot the tree object
plot(mst, layout=layout.fruchterman.reingold, edge.curved=TRUE,
vertex.size=vSizes, vertex.label.dist=-0.5, vertex.label.color="black",
asp=FALSE, vertex.label.cex=0.6, edge.width=edgeweights, edge.arrow.mode=0,
main="Sub-Network of All Significant Differentially Expressed Genes")
# code chunk number 4: Identify communities in the tree object based on 'edge betweenness'
#mst.communities <- edge.betweenness.community(mst, directed=T)
mst.communities <- edge.betweenness.community(mst, directed=T)
mst.clustering <- make_clusters(mst, membership=mst.communities$membership)
V(mst)$color <- mst.communities$membership + 1
par(mfrow=c(1,2))
plot(
mst.clustering, mst,
layout=layout.fruchterman.reingold,
edge.curved=TRUE,
vertex.size=vSizes,
vertex.label.dist=-0.5,
vertex.label.color="black",
asp=FALSE,
vertex.label.cex=0.6,
edge.width=edgeweights,
edge.arrow.mode=0,
main="Significant Gene Sub-Network"
)
plot(
mst,
layout=layout.fruchterman.reingold,
edge.curved=TRUE,
vertex.size=vSizes,
vertex.label.dist=-0.5,
vertex.label.color="black",
asp=FALSE,
vertex.label.cex=0.6,
edge.width=edgeweights,
edge.arrow.mode=0,
main="Significant Gene Sub-Network"
)
############################################################################
# adj.matrix.graph-all-over-genes ####
############################################################################
head(sig.g.o.rep.ma)
head(sig.g.u.rep.ma)
s.g.rep.matrix;s.g.under.matrix;s.g.over.matrix
#Create a graph adjacency based on correlation distances between genes in pairwise fashion.
over.graph <- graph.adjacency(as.matrix(as.dist(cor(t(s.g.over.matrix), method="pearson"))),
mode="undirected", weighted=TRUE, diag=FALSE)
#Simplfy the adjacency object
#over.graph <- simplify(over.graph, remove.multiple=TRUE, remove.loops=TRUE)
#Colour negative correlation edges as blue
E(over.graph)[which(E(over.graph)$weight<0)]$color <- "darkblue"
#Colour positive correlation edges as red
E(over.graph)[which(E(over.graph)$weight>0)]$color <- "darkred"
#Convert edge weights to absolute values
E(over.graph)$weight <- abs(E(over.graph)$weight)
#Change arrow size #For directed graphs only
E(over.graph)$arrow.size <- 1.0
#Remove edges below absolute Pearson correlation 0.7
over.graph <- delete_edges(over.graph, E(over.graph)[which(E(over.graph)$weight<0.7)])
#Assign names to the graph vertices (optional)
V(over.graph)$name <- V(over.graph)$name
#Change shape of graph vertices
V(over.graph)$shape <- "sphere"
#Change colour of graph vertices
V(over.graph)$color <- "skyblue"
#Change colour of vertex frames
V(over.graph)$vertex.frame.color <- "white"
#Scale the size of the vertices to be proportional to the level of expression of each gene represented by each vertex
#Multiple scaled vales by a factor of 10
scale01 <- function(x){(x-min(x))/(max(x)-min(x))}
vSizes <- (scale01(apply(s.g.over.matrix, 1, mean)) + 1.0) * 10
#Amplify or decrease the width of the edges
edgeweights <- E(over.graph)$weight * 2.0
#Convert the graph adjacency object into a minimum spanning tree based on Prim's algorithm
mst <- mst(over.graph, algorithm="prim")
#Plot the tree object
plot(mst, layout=layout.fruchterman.reingold, edge.curved=TRUE,
vertex.size=vSizes, vertex.label.dist=-0.5, vertex.label.color="black",
asp=FALSE, vertex.label.cex=0.6, edge.width=edgeweights, edge.arrow.mode=0,
main="Network of significant genes in Over group")
# code chunk number 4: Identify communities in the tree object based on 'edge betweenness'
#mst.communities <- edge.betweenness.community(mst, directed=T)
mst.communities <- edge.betweenness.community(mst, directed=T)
mst.clustering <- make_clusters(mst, membership=mst.communities$membership)
V(mst)$color <- mst.communities$membership + 1
par(mfrow=c(1,2))
plot(
mst.clustering, mst,
layout=layout.fruchterman.reingold,
edge.curved=TRUE,
vertex.size=vSizes,
vertex.label.dist=-0.5,
vertex.label.color="black",
asp=FALSE,
vertex.label.cex=0.6,
edge.width=edgeweights,
edge.arrow.mode=0,
main="Over Group Significant Gene Network"
)
plot(
mst,
layout=layout.fruchterman.reingold,
edge.curved=TRUE,
vertex.size=vSizes,
vertex.label.dist=-0.5,
vertex.label.color="black",
asp=FALSE,
vertex.label.cex=0.6,
edge.width=edgeweights,
edge.arrow.mode=0,
main="Over Group Significant Gene Network"
)
############################################################################
# adj.matrix.graph-all-under-genes ####
############################################################################
#under.graph <- graph.adjacency(as.matrix(as.dist(cor(t(s.g.under.matrix), method="pearson"))),
# mode="directed", weighted=TRUE, diag=FALSE)
#Create a graph adjacency based on correlation distances between genes in pairwise fashion.
under.graph <- graph.adjacency(as.matrix(as.dist(cor(t(s.g.under.matrix), method="pearson"))),
mode="undirected", weighted=TRUE, diag=FALSE)
#NB - Euclidean distances can also be used instead of correlation, but this changes the tutorial slightly:
#g <- graph.adjacency(as.matrix(dist(estrogenMainEffects, method="euclidean")),
#mode="undirected", weighted=TRUE, diag=FALSE)
#Simplfy the adjacency object
#under.graph <- simplify(under.graph, remove.multiple=TRUE, remove.loops=TRUE)
#Colour negative correlation edges as blue
E(under.graph)[which(E(under.graph)$weight<0)]$color <- "darkblue"
#Colour positive correlation edges as red
E(under.graph)[which(E(under.graph)$weight>0)]$color <- "darkred"
#Convert edge weights to absolute values
E(under.graph)$weight <- abs(E(under.graph)$weight)
#Change arrow size #For directed graphs only
E(over.graph)$arrow.size <- 1.0
#Remove edges below absolute Pearson correlation 0.7
under.graph <- delete_edges(under.graph, E(under.graph)[which(E(under.graph)$weight<0.7)])
#Assign names to the graph vertices (optional)
V(under.graph)$name <- V(under.graph)$name
#Change shape of graph vertices
V(under.graph)$shape <- "sphere"
#Change colour of graph vertices
V(under.graph)$color <- "skyblue"
#Change colour of vertex frames
V(under.graph)$vertex.frame.color <- "white"
#Scale the size of the vertices to be proportional to the level of expression of each gene represented by each vertex
#Multiple scaled vales by a factor of 10
scale01 <- function(x){(x-min(x))/(max(x)-min(x))}
vSizes <- (scale01(apply(s.g.under.matrix, 1, mean)) + 1.0) * 10
#Amplify or decrease the width of the edges
edgeweights <- E(under.graph)$weight * 2.0
#Convert the graph adjacency object into a minimum spanning tree based on Prim's algorithm
mst <- mst(under.graph, algorithm="prim")
#Plot the tree object
plot(mst, layout=layout.fruchterman.reingold, edge.curved=TRUE,
vertex.size=vSizes, vertex.label.dist=-0.5, vertex.label.color="black",
asp=FALSE, vertex.label.cex=0.6, edge.width=edgeweights, edge.arrow.mode=0,
main="Network of Significant Genes in Under Group")
## code chunk number 4: Identify communities in the tree object based on 'edge betweenness'
#mst.communities <- edge.betweenness.community(mst, directed=T)
mst.communities <- edge.betweenness.community(mst, directed=F)
mst.clustering <- make_clusters(mst, membership=mst.communities$membership)
#mst.m <- make_clusters(mst, membership=mst.communities$merges)
V(mst)$color <- mst.communities$membership + 1
par(mfrow=c(1,2))
plot(
mst.clustering, mst,
layout=layout.fruchterman.reingold,
edge.curved=TRUE,
vertex.size=vSizes,
vertex.label.dist=-0.5,
vertex.label.color="black",
asp=FALSE,
vertex.label.cex=0.6,
edge.width=edgeweights,
edge.arrow.mode=0,
main="Under Group Significant Gene Network"
)
plot(
mst,
layout=layout.fruchterman.reingold,
edge.curved=TRUE,
vertex.size=vSizes,
vertex.label.dist=-0.5,
vertex.label.color="black",
asp=FALSE,
vertex.label.cex=0.6,
edge.width=edgeweights,
edge.arrow.mode=0,
main="Under Group Significant Gene Network"
)
#Check the vertex degree, i.e., number of connections to each vertex
degree(mst)
#Output information for each community, including vertex-to-community assignments and modularity
commSummary <- data.frame(mst.communities$names,mst.communities$membership, mst.communities$modularity)
commSummary <- data.frame(mst.communities$names,mst.communities$membership, mst.clustering$modularity)
colnames(commSummary) <- c("Gene", "Community", "Modularity")
options(scipen=999);commSummary
#Compare community structures using the variance of information (vi) metric (not relevant here)
#Community structures that are identical will have a vi=0
compare(mst.communities, mst.communities, method="vi")
#########################################################################################################
# GOplots-genelists ####
#########################################################################################################
geneList<-mapIds(x = org.Hs.eg.db,
keys = rownames(g.rep.matrix),
column = "ENTREZID",
keytype = "SYMBOL",
multiVals="first")
genes<-row.names(sig.genes_exp.diff)
foldchange<-sig.genes_exp.diff[,"logFC"]
qval<-sig.genes_exp.diff[,"q_value"]
under.inf = which(foldchange == "-Inf" & qval < 0.05 | foldchange < -1 & foldchange != "-Inf" & qval < 0.05)
over.inf = which(foldchange == "Inf" & qval < 0.05 | foldchange > 1 & foldchange != "Inf" & qval < 0.05)
HIexp.inOVER<-as.data.frame(sig.genes_exp.diff[over.inf,])
head(HIexp.inOVER)
HI.exp.OVER.genes<-row.names(HIexp.inOVER)
length(HI.exp.OVER.genes)
Qval.hi_exprOVER<-HIexp.inOVER[,"q_value"]
names(Qval.hi_exprOVER)<-HI.exp.OVER.genes
#HI.exp.OVER.genes<-row.names(Qval.hi_exprOVER)
logfc.hi_exprOVER<-HIexp.inOVER[,"logFC"]
names(logfc.hi_exprOVER)<-HI.exp.OVER.genes
HIexp.inUNDER<-as.data.frame(sig.genes_exp.diff[under.inf,])
head(HIexp.inUNDER)
HI.exp.UNDER.genes<-row.names(HIexp.inUNDER)
length(HI.exp.UNDER.genes)
Qval.hi_exprUNDER<-HIexp.inUNDER[,"q_value"]
names(Qval.hi_exprUNDER)<-HI.exp.UNDER.genes
#HI.exp.UNDER.genes<-row.names(Qval.hi_exprUNDER)
logfc.hi_exprUNDER<-HIexp.inUNDER[,"logFC"]
names(logfc.hi_exprUNDER)<-HI.exp.UNDER.genes
#QvalnoOVER #QvalnoUNDER
ENTREZQval.hi_exprOVER<-mapIds(x = org.Hs.eg.db,
keys = names(Qval.hi_exprOVER),
column = "ENTREZID",
keytype = "SYMBOL",
multiVals="first")
ENTREZQval.hi_exprUNDER<-mapIds(x = org.Hs.eg.db,
keys = names(Qval.hi_exprUNDER),
column = "ENTREZID",
keytype = "SYMBOL",
multiVals="first")
ENTREZsiggenes<-mapIds(x = org.Hs.eg.db,
keys = sig_genes_exp.diff$gene_id,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals="first")
ENTREZsiggenes
head(sig.genes_exp.diff)
sig_gene_fold.df<-sig_genes_exp.diff[order(sig_genes_exp.diff$log2_fold_change,decreasing = T),]
sig.gene.logfold.df<-as.data.frame(sig_gene_fold.df)
rownames(sig.gene.logfold.df) = sigENTREZsiggenes
symbols<-names(ENTREZsiggenes)
#over.hilog<-c(sig_genes_exp.diff[,"log2_fold_change"])
#names(over.hilog)<-(ENTREZQvalOVERhi)
#over.hiqval<-sig_genes_exp.diff[,"q_value"]
#names(over.hiqval)<-ENTREZQvalOVERhi
#over.hipval<-sig_genes_exp.diff[,"p_value"]
#names(over.hipval)<-ENTREZQvalOVERhi
# rownames(sig_gene_fold.df)<-
#names(under.hilog)<-ENTREZsiggenes
#under.hiqval<-sig_genes_exp.diff[,"q_value"]
#names(under.hiqval)<-ENTREZQvalUNDERhi
#under.hipval<-(sig_genes_exp.diff[,"p_value"])
#names(under.hipval) <- ENTREZQvalUNDERhi
xx<-annFUN.org("BP", mapping = "org.Hs.eg.db", ID = "symbol")
topDiffGenes <- function(allScore) {
return(allScore < 0.01)}
#########################################################################################################
# GOplots-go-objects ####
#########################################################################################################
# Significantly Differentially Expressed by the Under group
# i.e Luts over Ctrl --> Ctrl = Under group, Under group highly
# expresses and No expression in the Over group
HIunder.BP.GOdata <- new("topGOdata",ontology = "BP",allGenes = Qval.hi_exprUNDER, nodeSize = 5,
annot = annFUN.org,mapping = "org.Hs.eg.db",geneSel = topDiffGenes,
ID = "symbol")
HIunder.MF.GOdata <- new("topGOdata",ontology = "MF",allGenes = Qval.hi_exprUNDER, nodeSize = 5,
annot = annFUN.org,mapping = "org.Hs.eg.db",geneSel = topDiffGenes,
ID = "symbol")
HIunder.CC.GOdata <- new("topGOdata",ontology = "CC",allGenes = Qval.hi_exprUNDER, nodeSize = 5,
annot = annFUN.org,mapping = "org.Hs.eg.db",geneSel = topDiffGenes,
ID = "symbol")
HIunder.BPtKS <- runTest(HIunder.BP.GOdata, algorithm = "classic", statistic = "ks")
HIunder.BPtKS
HIunder.BPFisher <- runTest(HIunder.BP.GOdata, algorithm = "classic", statistic = "fisher")
HIunder.BPFisher
HIunder.BPtKS.elim <- runTest(HIunder.BP.GOdata, algorithm = "elim", statistic = "ks")
HIunder.BPtKS.elim
HIunder.MFtKS <- runTest(HIunder.MF.GOdata, algorithm = "classic", statistic = "ks")
HIunder.MFtKS
HIunder.MFFisher <- runTest(HIunder.MF.GOdata, algorithm = "classic", statistic = "fisher")
HIunder.MFFisher
HIunder.MFFtKS.elim <- runTest(HIunder.MF.GOdata, algorithm = "elim", statistic = "ks")
HIunder.MFFtKS.elim
HIunder.CCtKS <- runTest(HIunder.CC.GOdata, algorithm = "classic", statistic = "ks")
HIunder.CCtKS
HIunder.CCFisher <- runTest(HIunder.CC.GOdata, algorithm = "classic", statistic = "fisher")
HIunder.CCFisher
HIunder.CCtKS.elim <- runTest(HIunder.CC.GOdata, algorithm = "elim", statistic = "ks")
HIunder.CCtKS.elim
#########################################################################################################
# GOplots-under-plots ####
#########################################################################################################
pdf("GOplots_grch38.CTRL_highly_expressed.pdf")
showSigOfNodes(HIunder.BP.GOdata, score(HIunder.BPFisher), firstSigNodes = 5, useInfo = "all")
showSigOfNodes(HIunder.BP.GOdata, score(HIunder.BPtKS), firstSigNodes = 5, useInfo = "all")
showSigOfNodes(HIunder.BP.GOdata, score(HIunder.BPtKS.elim), firstSigNodes = 5, useInfo = "all")
#printGraph(HIunder.BP.GOdata, HIunder.BPFisher, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIunder.BP.GOdata, HIunder.BPtKS, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIunder.BP.GOdata, HIunder.BPtKS.elim, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
showSigOfNodes(HIunder.MF.GOdata, score(HIunder.MFFisher), firstSigNodes = 5, useInfo = "all")
showSigOfNodes(HIunder.MF.GOdata, score(HIunder.MFtKS), firstSigNodes = 5, useInfo = "all" )
showSigOfNodes(HIunder.MF.GOdata, score(HIunder.MFFtKS.elim), firstSigNodes = 5, useInfo = "all")
#printGraph(HIunder.MF.GOdata, HIunder.MFFisher, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIunder.MF.GOdata, HIunder.MFFtKS.elim, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIunder.MF.GOdata, HIunder.MFtKS, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
showSigOfNodes(HIunder.CC.GOdata, score(HIunder.CCFisher), firstSigNodes = 5, useInfo = "all")
showSigOfNodes(HIunder.CC.GOdata, score(HIunder.CCtKS), firstSigNodes = 5, useInfo = "all")
showSigOfNodes(HIunder.CC.GOdata, score(HIunder.CCtKS.elim), firstSigNodes = 5, useInfo = "all")
#printGraph(HIunder.CC.GOdata, HIunder.CCFisher, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIunder.CC.GOdata, HIunder.CCtKS.elim, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIunder.CC.GOdata, HIunder.CCtKS, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
sigGOcc <- groupGO(gene=ENTREZQval.hi_exprUNDER, OrgDb=org.Hs.eg.db, ont="CC",
level = 3,readable = TRUE)
sigGOmf <- groupGO(gene=ENTREZQval.hi_exprUNDER, OrgDb=org.Hs.eg.db, ont="MF",
level = 3,readable = TRUE)
sigGObp <- groupGO(gene=ENTREZQval.hi_exprUNDER, OrgDb=org.Hs.eg.db, ont="BP",
level = 3,readable = TRUE)
barplot(sigGOcc, drop=TRUE, showCategory=12,args.legend="Sig Diff Expr Genes Grouping by GO-CC")
barplot(sigGOmf, drop=TRUE, showCategory=12,args.legend="Sig Diff Expr Genes Grouping by GO-MF")
barplot(sigGObp, drop=TRUE, showCategory=12,args.legend="Sig Diff Expr Genes Grouping by GO-BP")
dev.off()
#########################################################################################################
# GOplots-over-plots ####
#########################################################################################################
HIover.BP.GOdata <- new("topGOdata",ontology = "BP",allGenes = Qval.hi_exprOVER, nodeSize = 5,
annot = annFUN.org,mapping = "org.Hs.eg.db",geneSel = topDiffGenes,
ID = "symbol")
HIover.MF.GOdata <- new("topGOdata",ontology = "MF",allGenes = Qval.hi_exprOVER, nodeSize = 5,
annot = annFUN.org,mapping = "org.Hs.eg.db",geneSel = topDiffGenes,
ID = "symbol")
HIover.CC.GOdata <- new("topGOdata",ontology = "CC",allGenes = Qval.hi_exprOVER, nodeSize = 5,
annot = annFUN.org,mapping = "org.Hs.eg.db",geneSel = topDiffGenes,
ID = "symbol")
HIover.BPtKS <- runTest(HIover.BP.GOdata, algorithm = "classic", statistic = "ks")
HIover.BPtKS
HIover.BPFisher <- runTest(HIover.BP.GOdata, algorithm = "classic", statistic = "fisher")
HIover.BPFisher
HIover.BPtKS.elim <- runTest(HIover.BP.GOdata, algorithm = "elim", statistic = "ks")
HIover.BPtKS.elim
HIover.MFtKS <- runTest(HIover.MF.GOdata, algorithm = "classic", statistic = "ks")
HIover.MFtKS
HIover.MFFisher <- runTest(HIover.MF.GOdata, algorithm = "classic", statistic = "fisher")
HIover.MFFisher
HIoverMFtKS.elim <- runTest(HIover.MF.GOdata, algorithm = "elim", statistic = "ks")
HIoverMFtKS.elim
HIover.CCtKS <- runTest(HIover.CC.GOdata, algorithm = "classic", statistic = "ks")
HIover.CCtKS
HIover.CCFisher <- runTest(HIover.CC.GOdata, algorithm = "classic", statistic = "fisher")
HIover.CCFisher
HIover.CCtKS.elim <- runTest(HIover.CC.GOdata, algorithm = "elim", statistic = "ks")
HIover.CCtKS.elim
pdf("GOplots_grch38.LUTS_highly_expressed.pdf")
showSigOfNodes(HIover.BP.GOdata, score(HIover.BPFisher), firstSigNodes = 5, useInfo = "all" )
showSigOfNodes(HIover.BP.GOdata, score(HIover.BPtKS), firstSigNodes = 5, useInfo = "all" )
showSigOfNodes(HIover.BP.GOdata, score(HIover.BPtKS.elim), firstSigNodes = 5, useInfo = "all" )
#printGraph(HIover.BP.GOdata, HIover.BPFisher, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIover.BP.GOdata, HIover.BPtKS.elim, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIover.BP.GOdata, HIover.BPtKS, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
showSigOfNodes(HIover.MF.GOdata, score(HIover.MFFisher), firstSigNodes = 5, useInfo = "all" )
showSigOfNodes(HIover.MF.GOdata, score(HIover.MFtKS), firstSigNodes = 5, useInfo = "all" )
showSigOfNodes(HIover.MF.GOdata, score(HIoverMFtKS.elim), firstSigNodes = 5, useInfo = "all" )
#printGraph(HIover.MF.GOdata, HIover.MFFisher, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIover.MF.GOdata, HIoverMFtKS.elim, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIover.MF.GOdata, HIover.MFtKS, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
showSigOfNodes(HIover.CC.GOdata, score(HIover.CCFisher), firstSigNodes = 5, useInfo = "all" )
showSigOfNodes(HIover.CC.GOdata, score(HIover.CCtKS), firstSigNodes = 5, useInfo = "all" )
showSigOfNodes(HIover.CC.GOdata, score(HIover.CCtKS.elim), firstSigNodes = 5, useInfo = "all" )
#printGraph(HIover.CC.GOdata, HIover.CCFisher, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIover.CC.GOdata, HIover.CCtKS.elim, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
#printGraph(HIover.CC.GOdata, HIover.CCtKS, firstSigNodes = 5, fn.prefix = "tGO", useInfo = "all", pdfSW = TRUE)
sigGOcc <- groupGO(gene=ENTREZQval.hi_exprOVER, OrgDb=org.Hs.eg.db, ont="CC",
level = 3,readable = TRUE)
sigGOmf <- groupGO(gene=ENTREZQval.hi_exprOVER, OrgDb=org.Hs.eg.db, ont="MF",
level = 3,readable = TRUE)
sigGObp <- groupGO(gene=ENTREZQval.hi_exprOVER, OrgDb=org.Hs.eg.db, ont="BP",
level = 3,readable = TRUE)
barplot(sigGOcc, drop=TRUE, showCategory=12,args.legend="Sig Diff Expr Genes Grouping by GO-CC")
barplot(sigGOmf, drop=TRUE, showCategory=12,args.legend="Sig Diff Expr Genes Grouping by GO-MF")
barplot(sigGObp, drop=TRUE, showCategory=12,args.legend="Sig Diff Expr Genes Grouping by GO-BP")
dev.off()
mySigGenes<-getSig(cuff,x=over,y=under,alpha=.05,level='genes')
head(mySigGenes)
length(mySigGenes)
sigGenes<-getGenes(cuff, mySigGenes)
length(sigGenes)
sig_genes_annot<-sigGenes@annotation
head(sig_genes_annot)
sig_genes_exp.diff<-diffData(sigGenes)
sig.genes_exp.diff<-as.data.frame(cbind(logFC=sig_genes_exp.diff$log2_fold_change,q_value=sig_genes_exp.diff$q_value),
row.names=sig_genes_exp.diff$gene_id)
head(sig.genes_exp.diff)
s.o.gene_data<-subset(sig.genes_exp.diff, q_value < 0.05 & logFC > 0)
head(s.o.gene_data);dim(s.o.gene_data)
s.u.gene_data<-subset(sig.genes_exp.diff, q_value < 0.05 & logFC < 0)
head(s.u.gene_data);dim(s.u.gene_data)
sig_genes<-which(sig.genes_exp.diff[,"q_value"] < 0.05 & abs(sig.genes_exp.diff[,"logFC"]) > 1)
length(sig_genes)
head(sig.genes_exp.diff[sig_genes,])
logfold=sig.genes_exp.diff[sig_genes,c("logFC")]
sig.gene.List<-as.data.frame(logfold, row.names = genes)
head(sig.gene.List)
sorted.fold.vals<-order(logfold, decreasing = T)
gene.eg.fc<-genes[sorted.fold.vals]
sorted.fc<-logfold[sorted.fold.vals]
sig.gene.fold.list<-as.data.frame(sorted.fc,row.names = gene.eg.fc)
head(sig.gene.fold.list)
dim(sig.gene.fold.list)
de <- row.names(sig.gene.fold.list)[abs(sig.gene.fold.list) > 1.5]
head(de)
length(de)
eg = bitr(de, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
sym.eg<-as.factor(eg$SYMBOL)
dim(eg)
symbols<-row.names(sig.gene.fold.list)
head(symbols)
length(symbols)
entrez.syms<-which(row.names(sig.gene.fold.list) %in% sym.eg)
length(entrez.syms)
head(sig.gene.fold.list)
foldchange<-sig.gene.fold.list[entrez.syms,]
SYMBOL<-symbols[entrez.syms]
siggene.fc.eg<-cbind(SYMBOL, foldchange)
head(siggene.fc.eg)
siggene.eg.fc<-merge(eg, siggene.fc.eg, by.x="SYMBOL", by.y="SYMBOL")
SigGeneList<-as.data.frame(siggene.eg.fc[,"foldchange"], row.names=siggene.eg.fc$ENTREZID)
colnames(SigGeneList)<-"log2.fold.change"
head(SigGeneList)
head(row.names(SigGeneList))
de <- SigGeneList
head(de)
x <- enrichPathway(gene=row.names(de),pvalueCutoff=0.05, readable=T)
head(as.data.frame(x))
dim(as.data.frame(x))
## ----fig.height=6, fig.width=12------------------------------------------
barplot(x, showCategory=8)
dotplot(x, showCategory=15)
cnetplot(x, categorySize="pvalue", foldChange=fold.change)
SigGeneList<-SigGeneList[order(SigGeneList$log2.fold.change, decreasing = T),]
## ------------------------------------------------------------------------
y <- gsePathway(SigGeneList, nPerm=10000,
pvalueCutoff=0.2,
pAdjustMethod="BH", verbose=FALSE)
res <- as.data.frame(y); head(res)
## ----fig.height=8, fig.width=8-------------------------------------------
#emapplot(y, color="pvalue")
#gseaplot(y, geneSetID = "R-HSA-69242")
#viewPathway("E2F mediated regulation of DNA replication", readable=TRUE, foldChange=geneList)
gene.df <- bitr(names(geneList), fromType = "SYMBOL",toType = c("ENTREZID", "SYMBOL"),OrgDb = org.Hs.eg.db)
gene.df <- bitr(gene_diff_data$gene_id, fromType = "SYMBOL",toType = c("ENTREZID", "SYMBOL"),OrgDb = org.Hs.eg.db)
head(gene.df)
ggoCC <- groupGO(gene = de,
OrgDb = org.Hs.eg.db,
ont = "CC",
level = 3,
readable = TRUE)
ggoBP <- groupGO(gene = de,
OrgDb = org.Hs.eg.db,
ont = "BP",
level = 3,
readable = TRUE)
ggoMF <- groupGO(gene = de,
OrgDb = org.Hs.eg.db,
ont = "MF",
level = 3,
readable = TRUE)
head(ggo)
## ----fig.height=5, fig.width=9-------------------------------------------
barplot(ggoCC, drop=TRUE, showCategory=12)
barplot(ggoMF, drop=TRUE, showCategory=12)
barplot(ggoBP, drop=TRUE, showCategory=12)
barplot(ego, showCategory=8)
dotplot(ego)
## ----fig.cap="plotting gsea result", fig.align="center", fig.height=6, fig.width=8----
gseaplot(kk2, geneSetID = "hsa04145")
## ------------------------------------------------------------------------
ego <- enrichGO(gene = de,
universe = gene.df$ENTREZID,
OrgDb = org.Hs.eg.db,
ont = "CC",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05,
readable = TRUE)
head(ego)
cnetplot(ego, categorySize="pvalue")
## categorySize can be scaled by 'pvalue' or 'geneNum'
cnetplot(ego, categorySize="pvalue", foldChange=geneList)
## ----fig.height=12, fig.width=8, eval=FALSE------------------------------
goplot(ego)
## ----eval=FALSE----------------------------------------------------------
# ego2 <- enrichGO(gene = gene.df$ENSEMBL,
# OrgDb = org.Hs.eg.db,
# keyType = 'ENSEMBL',
# ont = "CC",
# pAdjustMethod = "BH",
# pvalueCutoff = 0.01,
# qvalueCutoff = 0.05)
## ----eval=FALSE----------------------------------------------------------
# ego2 <- setReadable(ego2, OrgDb = org.Hs.eg.db)
## ----eval=FALSE----------------------------------------------------------
egoCC <- gseGO(geneList = de,
OrgDb = org.Hs.eg.db,
ont = "CC",
nPerm = 1000,
minGSSize = 100,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = FALSE)
egoBP <- gseGO(geneList = de,
OrgDb = org.Hs.eg.db,
ont = "BP",
nPerm = 1000,
minGSSize = 100,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = FALSE)
egoMF <- gseGO(geneList = de,
OrgDb = org.Hs.eg.db,
ont = "MF",
nPerm = 1000,
minGSSize = 100,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = FALSE)
## ------------------------------------------------------------------------
kk <- enrichKEGG(gene = de,
organism = 'hsa',
pvalueCutoff = 0.05)
head(kk)
## ------------------------------------------------------------------------
kk2 <- gseKEGG(geneList = de,
organism = 'hsa',
nPerm = 1000,
minGSSize = 120,
pvalueCutoff = 0.05,
verbose = FALSE)
head(kk2)
## ----eval = FALSE--------------------------------------------------------
# mkk <- enrichMKEGG(gene = gene,
# organism = 'hsa')
## ----eval=FALSE----------------------------------------------------------
# mkk2 <- gseMKEGG(geneList = geneList,
# species = 'hsa')
## ----eval=FALSE----------------------------------------------------------
# david <- enrichDAVID(gene = gene,
# idType = "ENTREZ_GENE_ID",
# listType = "Gene",
# annotation = "KEGG_PATHWAY",
# david.user = "[email protected]")
## ------------------------------------------------------------------------
search_kegg_organism('ece', by='kegg_code')
ecoli <- search_kegg_organism('Escherichia coli', by='scientific_name')
dim(ecoli)
head(ecoli)
gmtfile <- system.file("extdata", "c5.cc.v5.0.entrez.gmt", package="clusterProfiler")
c5 <- read.gmt(gmtfile)
egmt <- enricher(gene, TERM2GENE=c5)
head(egmt)
egmt2 <- GSEA(geneList, TERM2GENE=c5, verbose=FALSE)
head(egmt2)
## ----fig.height=5, fig.width=9-------------------------------------------
barplot(ggo, drop=TRUE, showCategory=12)
## ----fig.height=5, fig.width=8-------------------------------------------
barplot(ego, showCategory=8)
## ------------------------------------------------------------------------
dotplot(ego)
## ----fig.cap="enrichment map of enrichment result", fig.align="center", fig.height=8, fig.width=8----
emapplot(ego)
## ----fig.height=8, fig.width=8-------------------------------------------
## categorySize can be scaled by 'pvalue' or 'geneNum'
cnetplot(ego, categorySize="pvalue", foldChange=geneList)
## ----fig.height=12, fig.width=8, eval=FALSE------------------------------
# goplot(ego)
## ----fig.cap="plotting gsea result", fig.align="center", fig.height=6, fig.width=8----
gseaplot(kk, geneSetID = "hsa04145")
## ----eval=FALSE----------------------------------------------------------
# browseKEGG(kk, 'hsa04110')
## ----eval=FALSE----------------------------------------------------------
# library("pathview")
# hsa04110 <- pathview(gene.data = geneList,
# pathway.id = "hsa04110",
# species = "hsa",
# limit = list(gene=max(abs(geneList)), cpd=1))
## ------------------------------------------------------------------------
data(gcSample)
lapply(gcSample, head)
## ------------------------------------------------------------------------
ck <- compareCluster(geneCluster = gcSample, fun = "enrichKEGG")
head(as.data.frame(ck))
## ------------------------------------------------------------------------
mydf <- data.frame(Entrez=names(geneList), FC=geneList)
mydf <- mydf[abs(mydf$FC) > 1,]
mydf$group <- "upregulated"
mydf$group[mydf$FC < 0] <- "downregulated"
mydf$othergroup <- "A"
mydf$othergroup[abs(mydf$FC) > 2] <- "B"
formula_res <- compareCluster(Entrez~group+othergroup, data=mydf, fun="enrichKEGG")
head(as.data.frame(formula_res))
## ----fig.height=7, fig.width=9-------------------------------------------
dotplot(ck)
dotplot(formula_res)
dotplot(formula_res, x=~group) + ggplot2::facet_grid(~othergroup)
###################################################
# CummeRbund-plots ####
###################################################
My.Sig.Genes<-getSig(cuff,x=over,y=under,alpha=.01,level='genes')
MY.sig.Genes<-getGenes(cuff, My.Sig.Genes)
sigIsos<-getSig(cuff,x=over,y=under,alpha=.01,level='isoforms')
mySigIsos<-getGenes(cuff, sigIsos)
sigTSS<-getSig(cuff,x=over,y=under,alpha=.01,level='TSS')
mySigTSS<-getGenes(cuff, sigTSS)
sigCDS<-getSig(cuff,x=over,y=under,alpha=.01,level='CDS')
mySigCDS<-getGenes(cuff, sigCDS)
mySigrelCDS<-getSig(cuff,x=over,y=under,alpha=.01,level='relCDS')
sigrelCDS<-getGenes(cuff, mySigrelCDS)
mySigPromoter<-getSig(cuff,x=over,y=under,alpha=.01,level='promoters')
head(mySigPromoter)
length(mySigPromoter)
sigPromoter<-getGenes(cuff, mySigPromoter)
length(sigPromoter)
mySigSplice<-getSig(cuff,x=over,y=under,alpha=.01,level='splicing')
head(mySigSplice)
length(mySigSplice)
sigSplice<-getGenes(cuff, mySigSplice)
length(sigSplice)
plot(csDendro(MY.sig.Genes, replicates=T,logMode = T),
main="Dendrogram built from the Significantly Diff-Expr Genes at alpha=0.01")
plot(csDendro(mySigIsos, replicates=T,logMode = T),
main="Dendrogram built from the Significantly Diff-Expr Isoforms at alpha=0.01")
plot(csDendro(mySigTSS, replicates=T,logMode = T),
main="Dendrogram built from the Significantly Diff-Expr TSS at alpha=0.01")
plot(csDendro(mySigCDS, replicates=T,logMode = T),
main="Dendrogram built from the Significantly Diff-Expr CDS at alpha=0.01")
plot(csDendro(sigrelCDS, replicates=T,logMode = T),
main="Dendrogram built from the Significantly Differentially loaded CDS features at alpha=0.01")
plot(csDendro(sigPromoter, replicates=T,logMode = T),
main="Dendrogram built from the Significantly Differentially loaded Promoters at alpha=0.01")
plot(csDendro(sigSplice, replicates=T,logMode = T),
main="Dendrogram built from the Significantly Differentially loaded Splicing at alpha=0.01")
plot(dispersionPlot(cuff) + ggtitle("Gene Level Dispersion"))
plot(dispersionPlot(isoforms(cuff)) + ggtitle("ISoform Level Dispersion"))
plot(dispersionPlot(CDS(cuff)) + ggtitle("CDS Level Dispersion"))
plot(dispersionPlot(TSS(cuff)) + ggtitle("TSS Level Dispersion"))
###################################################
# CummeRbund-fpkmSCV-plots ####
###################################################
plot(fpkmSCVPlot(isoforms(cuff)))
plot(fpkmSCVPlot(TSS(cuff)))
plot(fpkmSCVPlot(CDS(cuff)))
plot(csDensity(isoforms(cuff),replicates=T))
plot(csDensity(CDS(cuff),replicates=T))
plot(csDensity(TSS(cuff),replicates=T))
###################################################
# CummeRbund-fpkmSCV-plots ####
###################################################
plot(csBoxplot(isoforms(cuff),logMode = T, replicates=T))
plot(csBoxplot(TSS(cuff),logMode = T,replicates=T))