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Urine1 RNA-Seq Differential Expression Notebook.Rmd
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---
title: "Urine1 RNA-Seq Differential Expression Notebook"
output: html_notebook
editor_options:
chunk_output_type: inline
---
```{r library-and-data-load, include=FALSE}
# Cran
library(dplyr);library(magrittr); library(tidyverse);library(rpart);library(rpart.plot)
library(gplots);library(ggplot2);library(igraph);library(corrplot);library(viridis)
library(stats);library(stats4);library(outliers);library(nortest);library(pvclust)
library(genefilter);library(cummeRbund);library(limma);library(edgeR)
library(DLBCL);library(DESeq2);library(enrichplot);library(biomaRt)
library(topGO);library(GOstats);library(gage);library(gageData)
library(STRINGdb);library(GenomicRanges);library(GenomicFeatures)
library(RColorBrewer);library(VennDiagram);library(parallel)
library(clusterProfiler);library(rtracklayer);library(DOSE)
library(org.Hs.eg.db);library(pathview);library(MASS)
library(reactome.db);library(ReactomePA)
# Data and gene sets
data(egSymb);data(bods);data(korg);data(carta.hs);data(interactome)
data(kegg.gs.dise);data(kegg.sets.hs);data(sigmet.idx.hs)
data(go.sets.hs);data(go.subs.hs);data(ko.ids)
data(gene.idtype.list);data(gene.idtype.bods)
data(kegg.met);data(kegg.gs)
```
```{r data-and-functions, include=FALSE, message=FALSE, echo=FALSE}
# Functions
scale01 <- function(x){(x-min(x))/(max(x)-min(x))}
#source("https://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/GOHyperGAll.txt")
# sequence data
genome.fa="/media/drew/easystore/umb_triley/Reference-Genomes/Human/UCSC_hg38/genome.fa"
refgtf="/media/drew/easystore/umb_triley/Reference-Genomes/Human/UCSC_hg38/genes.gtf"
inDir="/media/drew/easystore/umb_triley/urine1/cuffdiff_results_hg38_default/LUTS-over-CTRL"
cuff<-cummeRbund::readCufflinks(dir=inDir,genome=genome.fa,gtfFile=refgtf, rebuild=F)
#metadata
#rdcnt.all<-rbind(cnts.in=psych::describe(rdcnts$Read.Count.In/10^6),cnts.out=psych::describe(rdcnts$Read.Count.Out/10^6))
scrs=file.path("/media/drew/easystore/umb_triley/urine1/Sample-Library-Preparation/MichiganUrineSpecimensAUASIscores.csv")
AUASI<-read_csv(scrs,trim_ws = T,col_names = TRUE)
AUASI.df<-AUASI %>%
mutate(Sample.Num = paste("Sample", Samples, sep="_"))
lanes<-read_csv(file="/media/drew/easystore/umb_triley/urine1/Sample-Library-Preparation/lane-and-sample-numbers.csv", col_names = T,trim_ws = T)
pool<-read_delim(file="/media/drew/easystore/umb_triley/urine1/Sample-Library-Preparation/Pool-A-and-Pool-B-barcode-summary.csv", col_names = c("sample.number","NuGene.Adapter.Num", "barcode.seq","lanes"), trim_ws = T, delim=",")
# run data
lane<-as.factor(pool$lanes)
replicates.info<-cummeRbund::replicates(cuff)
groups<-replicates.info$sample_name
under=groups[1]
over=groups[((length(groups)/2)+1)]
grp.factor<-c(rep(0,9),rep(1,9))
grp.fac<-factor(grp.factor, levels=0:1, labels=c(under, over))
cuffdir<-dirname(replicates.info$file)
sample_number<-basename(cuffdir)
replicates.info<-replicates.info[-1]
replicates.info$sample_number<-basename(cuffdir)
replicates.info$sample_number <- gsub("_out","",replicates.info$sample_number)
replicate.df<-merge(replicates.info, pool, by.x="sample_number",by.y="sample.number")
replicates.df<-merge(replicate.df, AUASI.df, by.x="sample_number",by.y="Sample.Num")
replicates.df<-replicates.df[,-2,-8]
design <- model.matrix(~ 0 + Group, data=replicates.df)
colnames(design)<-c("CTRL","LUTS")
row.names(design) <- replicates.df$rep_name
# contrast matrix
contr.matrix <- makeContrasts(CTRL-LUTS, levels=design)
auasi.df="/media/drew/easystore/umb_triley/Thesis/AUASI.df.txt"
auasi.file = file.path(auasi.df)
#write.table(AUASI.df,file = auasi.file, sep = " ", row.names = F, col.names = T,quote = F)
gtfDir="/media/drew/easystore/umb_triley/urine1/cuffcompare_results_hg38_gtf_guided"
gtffile <- file.path(gtfDir,"cuffcmp.combined.gtf")
cuffcmp="/media/drew/easystore/umb_triley/urine1/cuffcompare_results_hg38_gtf_guided/cuffcmp.combined.gtf"
mergedgtf <- readGFF(cuffcmp)
hg38.genes.gtf<-as.data.frame(mergedgtf)
novelmerged<-hg38.genes.gtf[which(hg38.genes.gtf["class_code"] != "="),]
novel.hg38.granges<-makeGRangesFromDataFrame(novelmerged, keep.extra.columns=TRUE)
hg38.granges<-makeGRangesFromDataFrame(hg38.genes.gtf, keep.extra.columns=TRUE)
txdb <- makeTxDbFromGFF(gtffile, format="gtf", circ_seqs=character())
seqlevels(txdb)<-seqlevels0(txdb)
seq.txdb<-seqlevels(txdb)
tbg <- transcriptsBy(txdb,by="gene")
```
```{r diff-expr-initialize, message=FALSE, warning=FALSE, echo=FALSE}
g.rep.matrix<-repCountMatrix(cummeRbund::genes(cuff))
genes_exp.diff<-diffData(cummeRbund::genes(cuff))
g.cnt.df<-repCountMatrix(cummeRbund::genes(cuff))
cuff.table<-diffTable(cummeRbund::isoforms(cuff))
cuffnames<-gsub(x=colnames(cuff.table),pattern="LUTS_CTRL_", replacement ="")
colnames(cuff.table)<-cuffnames
# set Inf and -Inf with ceiling and floor
# set real number ceiling and floor
ma<-max(genes_exp.diff$log2_fold_change[is.finite(genes_exp.diff$log2_fold_change)])
mi<-min(genes_exp.diff$log2_fold_change[is.finite(genes_exp.diff$log2_fold_change)])
# set Inf and -Inf with ceiling and floor
genes_exp.diff$log2_fold_change<-replace(genes_exp.diff$log2_fold_change,
genes_exp.diff$log2_fold_change == "Inf", ma)
genes_exp.diff$log2_fold_change<-replace(genes_exp.diff$log2_fold_change,
genes_exp.diff$log2_fold_change == "-Inf", mi)
cuff.table$log2_fold_change<-replace(cuff.table$log2_fold_change,
cuff.table$log2_fold_change == "Inf", ma)
cuff.table$log2_fold_change<-replace(cuff.table$log2_fold_change,
cuff.table$log2_fold_change == "-Inf", mi)
cufftable.sig.df<-subset(cuff.table, cuff.table$significant=="yes")
cufftable.ok.df<-subset(cuff.table, cuff.table$status=="OK")
# sig gene expr data
sig_genes_exp.diff<-subset(genes_exp.diff, genes_exp.diff$significant=="yes")
mySigGenes<-getSig(cuff,x=over,y=under,alpha=0.05,level='genes')
sigGenes<-getGenes(cuff, mySigGenes)
# gene expr df and ma
g.cnt.df=as.data.frame(g.cnt.df)
g.cnt.ma=as.matrix(g.cnt.df)
# select directionally similar genes for each group
sig.h.genes_exp.diff<-subset(sig_genes_exp.diff,
sig_genes_exp.diff$log2_fold_change > 0
& sig_genes_exp.diff$q_value < 0.05)
sig.l.genes_exp.diff<-subset(sig_genes_exp.diff,
sig_genes_exp.diff$log2_fold_change < 0
& sig_genes_exp.diff$q_value < 0.05)
s.g.h.rep.matrix<-g.cnt.df[which(row.names(g.cnt.df) %in% sig.h.genes_exp.diff$gene_id),]
s.g.l.rep.matrix<-g.cnt.df[which(row.names(g.cnt.df) %in% sig.l.genes_exp.diff$gene_id),]
# factors for conditions
under.group<-grep(pattern=under, colnames(g.cnt.df))
over.group<-grep(pattern=over, colnames(g.cnt.df))
# get go ids
g.cnt.df$EntrezID<- mapIds(org.Hs.eg.db,
keys=rownames(g.cnt.df),
column="ENTREZID",keytype="SYMBOL",
multiVals="first")
g.cnt.df<-g.cnt.df[!duplicated(g.cnt.df$EntrezID),]
g.cnt.df<-g.cnt.df[!is.na(g.cnt.df$EntrezID),]
g.cnt.df$GOid<- mapIds(org.Hs.eg.db,
keys=rownames(g.cnt.df),
column="GO",keytype="SYMBOL",
multiVals="first")
g.cnt.df<-g.cnt.df[!is.na(g.cnt.df$GOid),]
inds <- which(!is.na(g.cnt.df$GOid) & !is.na(g.cnt.df$EntrezID))
# factors for conditions
g.cnt.df<-g.cnt.df[inds,]
g.cnt.ma<-g.cnt.ma[inds,]
g.o.cnt.df<-g.cnt.df[,over.group]
g.u.cnt.df<-g.cnt.df[,under.group]
sig.cnt.df<-g.cnt.df[which(row.names(g.cnt.df) %in% sig_genes_exp.diff$gene_id),]
s.g.h.rep.matrix<-s.g.h.rep.matrix[,over.group]
s.g.l.rep.matrix<-s.g.l.rep.matrix[,under.group]
s.g.h.rep.matrix<-s.g.h.rep.matrix[,c(1:9)]
s.g.l.rep.matrix<-s.g.l.rep.matrix[,c(1:9)]
upgenes<-file.path("/media/drew/easystore/umb_triley/Thesis/sig_up_genes.csv")
#write_lines(row.names(s.g.h.rep.matrix),path = upgenes)
downgenes<-file.path("/media/drew/easystore/umb_triley/Thesis/sig_down_genes.csv")
#write_lines(row.names(s.g.l.rep.matrix), path = downgenes)
#Create a graph adjacency based on correlation distances between genes in pairwise fashion.
oh.graph <- graph.adjacency(as.matrix(as.dist(cor(t(s.g.h.rep.matrix),method="pearson"))),
mode="undirected", weighted=TRUE, diag=T)
#Simplfy the adjacency object over.graph <- simplify(over.graph, remove.multiple=TRUE, remove.loops=TRUE)
#Colour negative correlation edges as blue
E(oh.graph)[which(E(oh.graph)$weight<0)]$color <- "yellowblue"
#Colour positive correlation edges as red
E(oh.graph)[which(E(oh.graph)$weight>0)]$color <- "blue"
#Convert edge weights to absolute values
E(oh.graph)$weight <- abs(E(oh.graph)$weight)
#Change arrow size #For directed graphs only
E(oh.graph)$arrow.size <- 2.0
#Remove edges below absolute Pearson correlation 0.9
oh.graph <- delete_edges(oh.graph, abs(E(oh.graph))[which(E(oh.graph)$weight<0.85)])
#Assign names to the graph vertices (optional)
V(oh.graph)$name <- V(oh.graph)$name
#Change shape of graph vertices
V(oh.graph)$shape <- "sphere"
#Change colour of graph vertices
V(oh.graph)$color <- "skyblue"
#Change colour of vertex frames
V(oh.graph)$vertex.frame.color <- "green"
#Scale the size of the vertices to be proportional to the level of expression of each gene represented by each
oh.vSizes <- (scale01(apply(s.g.h.rep.matrix, 1, mean)) + 1.0) * 7
#Amplify or decrease the width of the edges
oh.edgeweights <- E(oh.graph)$weight * 2
#Convert the graph adjacency object into a minimum spanning tree based on Prim's algorithm
oh.mst <- mst(oh.graph, algorithm="prim")
#Plot the tree object
#mst.communities <- edge.betweenness.community(mst, directed=T)
oh.mst.communities <- edge.betweenness.community(oh.mst, directed=F,edge.betweenness=T,
membership=T)
oh.mst.clustering <- make_clusters(oh.mst, membership=oh.mst.communities$membership)
V(oh.mst)$color <- oh.mst.communities$membership + 1
# Downregulated subnets
ol.graph <- graph.adjacency(as.matrix(as.dist(cor(t(s.g.l.rep.matrix),method="pearson"))),
mode="undirected",weighted=TRUE, diag=T)
#Simplfy the adjacency object over.graph <- simplify(over.graph, remove.multiple=TRUE, remove.loops=TRUE)
#Colour negative correlation edges as blue
E(ol.graph)[which(E(ol.graph)$weight<0)]$color <- "yellowblue"
#Colour positive correlation edges as red
E(ol.graph)[which(E(ol.graph)$weight>0)]$color <- "blue"
#Convert edge weights to absolute values
E(ol.graph)$weight <- abs(E(ol.graph)$weight)
#Change arrow size #For directed graphs only
E(ol.graph)$arrow.size <- 1.0
#Remove edges below absolute Pearson correlation 0.9
ol.graph <- delete_edges(ol.graph, abs(E(ol.graph))[which(E(ol.graph)$weight<0.85)])
#Assign names to the graph vertices (optional)
V(ol.graph)$name <- V(ol.graph)$name
#Change shape of graph vertices
V(ol.graph)$shape <- "sphere"
#Change colour of graph vertices
V(ol.graph)$color <- "skyblue"
#Change colour of vertex frames
V(ol.graph)$vertex.frame.color <- "green"
#Scale the size of the vertices to be proportional to the level of expression of each gene represented by each
ol.vSizes <- (scale01(apply(s.g.l.rep.matrix, 1, mean)) + 1.0) * 7
#Amplify or decrease the width of the edges
ol.edgeweights <- E(ol.graph)$weight * 2
#Convert the graph adjacency object into a minimum spanning tree based on Prim's algorithm
ol.mst <- mst(ol.graph, algorithm="prim")
#Plot the tree object
ol.mst.communities <- edge.betweenness.community(ol.mst, directed=F,
membership=T)
ol.mst.clustering <- make_clusters(ol.mst, membership=ol.mst.communities$membership)
V(ol.mst)$color <- ol.mst.communities$membership + 1
```
```{r}
head(s.g.h.rep.matrix)
head(s.g.l.rep.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.h.rep.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)$vertex.frame.color <- "white"
#Scale the size of the vertices to be proportional to the level of expression of each gene represented by each vertex
#Multiple scaled vales by a factor of 10
scale01 <- function(x){(x-min(x))/(max(x)-min(x))}
vSizes <- (scale01(apply(s.g.h.rep.matrix, 1, mean)) + 1.0) * 10
#Amplify or decrease the width of the edges
edgeweights <- E(over.graph)$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$membership + 1
par(mfrow=c(1,2))
plot(
mst.clustering, mst,
layout=layout.fruchterman.reingold,
edge.curved=TRUE,
vertex.size=vSizes,
vertex.label.dist=-0.5,
vertex.label.color="black",
asp=FALSE,
vertex.label.cex=0.6,
edge.width=edgeweights,
edge.arrow.mode=0,
main="Over Group Significant Gene Network"
)
plot(
mst,
layout=layout.fruchterman.reingold,
edge.curved=TRUE,
vertex.size=vSizes,
vertex.label.dist=-0.5,
vertex.label.color="black",
asp=FALSE,
vertex.label.cex=0.6,
edge.width=edgeweights,
edge.arrow.mode=0,
main="Over Group Significant Gene Network"
)
############################################################################
# adj.matrix.graph-all-under-genes ####
############################################################################
#under.graph <- graph.adjacency(as.matrix(as.dist(cor(t(s.g.l.rep.matrix), method="pearson"))),
# mode="directed", weighted=TRUE, diag=FALSE)
#Create a graph adjacency based on correlation distances between genes in pairwise fashion.
under.graph <- graph.adjacency(as.matrix(as.dist(cor(t(s.g.l.rep.matrix), method="pearson"))),
mode="undirected", weighted=TRUE, diag=FALSE)
#NB - Euclidean distances can also be used instead of correlation, but this changes the tutorial slightly:
#g <- graph.adjacency(as.matrix(dist(estrogenMainEffects, method="euclidean")),
#mode="undirected", weighted=TRUE, diag=FALSE)
#Simplfy the adjacency object
#under.graph <- simplify(under.graph, remove.multiple=TRUE, remove.loops=TRUE)
#Colour negative correlation edges as blue
E(under.graph)[which(E(under.graph)$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.l.rep.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")
```
```{r}
###################################################
# 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))
plot(csBoxplot(CDS(cuff),logMode = T, replicates=T))
```
```{r}
######################################################################
### code chunk number 3: Write repCOUNTMatrix and sig.repCOUNTmatrix
######################################################################
g.rep.ma;g.over.matrix;g.under.matrix
sig.rep.df<-as.data.frame(sig.g.rep.matrix)
sig.cor<-cor(sig.rep.df)
#plotcorr(sig.gse.cor, mar = c(0.1, 0.1, 0.1, 0.1))
# Do the same, but with colors corresponding to value
colorfun <- colorRamp(c("#CC0000","white","#3366CC"), space="Lab")
plotcorr(sig.cor, col=rgb(colorfun((sig.cor+1)/2), maxColorValue=255),
mar = c(0.1, 0.1, 0.1, 0.1))
sig.dist<-as.matrix(dist(t(sig.cor)))
loc <- cmdscale(sig.dist) # Performs MDS analysis on the geographic distances between European cities.
plot(loc[,1], -loc[,2], type="n", xlab="", ylab="", main="Euclidian MDS analysis")
text(loc[,1], -loc[,2], rownames(loc), cex=1,col=c("blue","darkgreen"))
# Plots the MDS results in 2D plot. The minus is required in this example to flip the plotting orientation.
mydatascale <- t(scale(t(sig.cor))) # Centers and scales data.
hr <- hclust(as.dist(1-cor(t(mydatascale), method="pearson")), method="complete") # Cluster rows by Pearson correlation.
hc <- hclust(as.dist(1-cor(mydatascale, method="spearman")), method="complete")
# Clusters columns by Spearman correlation.
heatmap(sig.cor, Rowv=as.dendrogram(hr), Colv=as.dendrogram(hc), scale="row") # col=my.colorFct(),
# Plot the data table as heatmap and the cluster results as dendrograms.
mycl <- cutree(hr, h=max(hr$height)/1.5)
mycolhc <- sample(rainbow(256))
mycolhc <- mycolhc[as.vector(mycl)]
heatmap(sig.cor, Rowv=as.dendrogram(hr), Colv=as.dendrogram(hc), scale="row", RowSideColors=mycolhc) #col=my.colorFct(),
# Cut the tree at specific height and color the corresponding clusters in the heatmap color bar.
#sig.dist.t<-as.matrix(cor(t(sig.genes.gse116969)))
#fit <- cmdscale(sig.dist.t,eig=TRUE, k=2) # k is the number of dim
#fit # view results
# plot solution
#x <- fit$points[,1];y <- fit$points[,2]
#plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2",
# main="Metric MDS", type="n")
#text(x, y, labels = row.names(sig.dist.t),cex=.7,col=c("blue","darkgreen"))
gse.scaled<-as.matrix(scale(t(sig.g.rep.matrix)))
heatmap(gse.scaled, Colv=F, scale='none')
gsecor<-cor(sig.g.rep.matrix)
round(gsecor,2)
# Do the same, but with colors corresponding to value
plot(hclust(dist(sig.g.rep.matrix,method="euclidian"),method="single"))
cl <- kmeans(sig.g.rep.matrix, 2)
plot(sig.g.rep.matrix, col = cl$cluster)
points(cl$centers, col = 1:2, pch = 8, cex=2)
gsecor<-cor(t(sig.genes.gse116969))
round(gsecor,2)
# Do the same, but with colors corresponding to value
plot(hclust(dist(sig.genes.gse116969,method="euclidian"),method="single"))
cl <- kmeans(sig.genes.gse116969, 2)
plot(sig.genes.gse116969, col = cl$cluster)
points(cl$centers, col = 1:2, pch = 8, cex=2)
c <- cor(t(sig.genes.gse116969), method="spearman")
d <- as.dist(1-c)
hr <- hclust(d, method = "complete", members=NULL)
# This is a short example of clustering the rows of the generated sample matrix 'y' with hclust. Seven different cluster joining
# methods can be selected with the 'method' argument: ward, single, complete, average, mcquitty, median and centroid. The
# object returned by hclust is a list of class hclust which describes the tree generated by the clustering process with the
# following components: merge, height, order, labels, method, call and dist.method.
par(mfrow = c(2, 2))
plot(hr, hang = 0.1)
plot(hr, hang = -1)
# The generated tree can be plotted with the plot() function. When the hang argument is set to '-1' then all leaves end on
# one line and their labels hang down from 0. More details on the plotting behavior is provided in the hclust help document (?hclust).
plot(as.dendrogram(hr), edgePar=list(col=3, lwd=4), horiz=T,)
# To plot trees horizontally, the hclust tree has to be transformed into a dendrogram object.
par(mfrow=c(2,1))
unclass(hr) # Prints the full content of the hclust object.
str(as.dendrogram(hr)) # Prints dendrogram structure as text.
hr$labels[hr$order] # Prints the row labels in the order they appear in the tree.
hrd1 <- as.dendrogram(hr); plot(hrd1)
hrd2 <- reorder(hrd1, sample(1:10))
plot(hrd2)
labels(hrd1)
labels(hrd2)
####################################################################################################
### code chunk number : Step 3: #Hierarchical clustering
####################################################################################################
# Import an alternative color scheme for the heatmap function.
mydatascale <- t(scale(t(g.over.matrix))) # Centers and scales data.
hr <- hclust(as.dist(1-cor(t(g.over.matrix), method="pearson")), method="complete") # Cluster rows by Pearson correlation.
hc <- hclust(as.dist(1-cor(g.over.matrix, method="spearman")), method="complete")
# Clusters columns by Spearman correlation.
heatmap(mydatascale, Rowv=as.dendrogram(hr), Colv=as.dendrogram(hc), col=my.colorFct(), scale="row")
# Plot the data table as heatmap and the cluster results as dendrograms.
mycl <- cutree(hr, h=max(hr$height)/1.5)
mycolhc <- sample(rainbow(256))
mycolhc <- mycolhc[as.vector(mycl)]
heatmap(g.over.matrix, Rowv=as.dendrogram(hr), Colv=as.dendrogram(hc),
col=my.colorFct(), scale="row", RowSideColors=mycolhc)
# Cut the tree at specific height and color the corresponding clusters in the heatmap color bar.
pv <- pvclust(scale(t(g.over.matrix)), method.dist="correlation", method.hclust="complete", nboot=10)
# Perform the hierarchical cluster analysis.
#Due to time resrictions, we are using here only 10 bootstrap repetitions.
# Usually, one should use at least 1000 repetitions.
plot(pv, hang=-1); pvrect(pv, alpha=0.95)
# Plots result as a dendrogram where the significant clusters are highlighted with red rectangles.
clsig <- unlist(pvpick(pv, alpha=0.95, pv="au", type="geq", max.only=TRUE)$clusters) # Retrieve members of significant clusters.
source("http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/dendroCol.R") # Import tree coloring function.
dend_colored <- dendrapply(as.dendrogram(pv$hclust), dendroCol, keys=clsig, xPar="edgePar", bgr="black", fgr="red", pch=20)
# Create dendrogram object where the significant clusters are labeled in red.
heatmap(sig.genes.gse116969, Rowv=dend_colored, Colv=as.dendrogram(hc), col=my.colorFct(), scale="row", RowSideColors=mycolhc)
# Plot the heatmap from above, but with the significant clusters in red and the cluster bins from the tree cutting step in
# the color bar.
x11(height=12); heatmap.2(mydata, Rowv=dend_colored, Colv=as.dendrogram(hc), col=my.colorFct(), scale="row", trace="none", RowSideColors=mycolhc) # Plot heatmap with heatmap.2() function which scales better for many entries.
mydatasort <- mydata[pv$hclust$labels[pv$hclust$order], hc$labels[hc$order]] # Sort rows in data table by 'dend_colored' and its colums by 'hc'.
x11(height=16, width=12); par(mfrow=c(1,2)); plot(dend_colored, horiz=T, yaxt="n"); image(scale(t(mydatasort)), col=my.colorFct(), xaxt="n",yaxt="n") # Plot heatmap with bootstrap tree in larger format using instead of heatmap the image function.
pdf("pvclust.pdf", height=21, width=6); plot(dend_colored, horiz=T, yaxt="n"); dev.off(); pdf("heatmap.pdf", height=20, width=6); image(scale(t(mydatasort)), col=my.colorFct(), xaxt="n",yaxt="n"); dev.off()
####################################################################################################
### code chunk number : Step 3: #Hierarchical clustering
####################################################################################################
# Import an alternative color scheme for the heatmap function.
mydatascale <- t(scale(t(g.over.matrix))) # Centers and scales data.
hr <- hclust(as.dist(1-cor(t(mydatascale), method="pearson")), method="complete") # Cluster rows by Pearson correlation.
hc <- hclust(as.dist(1-cor(mydatascale, method="spearman")), method="complete")
# Clusters columns by Spearman correlation.
heatmap(g.rep.ma, Rowv=as.dendrogram(hr), Colv=as.dendrogram(hc), col=my.colorFct(), scale="row")
# Plot the data table as heatmap and the cluster results as dendrograms.
mycl <- cutree(hr, h=max(hr$height)/1.5)
mycolhc <- sample(rainbow(256))
mycolhc <- mycolhc[as.vector(mycl)]
heatmap(sig.g.rep.matrix, Rowv=as.dendrogram(hr), Colv=as.dendrogram(hc),
col=my.colorFct(), scale="row", RowSideColors=mycolhc)
# Cut the tree at specific height and color the corresponding clusters in the heatmap color bar.
pv <- pvclust(scale(t(sig.g.rep.matrix)), method.dist="correlation", method.hclust="complete", nboot=10)
# Perform the hierarchical cluster analysis.
#Due to time resrictions, we are using here only 10 bootstrap repetitions.
# Usually, one should use at least 1000 repetitions.
plot(pv, hang=-1); pvrect(pv, alpha=0.95)
# Plots result as a dendrogram where the significant clusters are highlighted with red rectangles.
clsig <- unlist(pvpick(pv, alpha=0.95, pv="au", type="geq", max.only=TRUE)$clusters) # Retrieve members of significant clusters.
dend_colored <- dendrapply(as.dendrogram(pv$hclust), dendroCol, keys=clsig, xPar="edgePar", bgr="black", fgr="red", pch=20)
# Create dendrogram object where the significant clusters are labeled in red.
heatmap(sig.g.rep.matrix, Rowv=dend_colored, Colv=as.dendrogram(hc), col=my.colorFct(), scale="row", RowSideColors=mycolhc)
# Plot the heatmap from above, but with the significant clusters in red and the cluster bins from the tree cutting step in
# the color bar.
x11(height=12); heatmap.2(mydata, Rowv=dend_colored, Colv=as.dendrogram(hc), col=my.colorFct(), scale="row", trace="none", RowSideColors=mycolhc) # Plot heatmap with heatmap.2() function which scales better for many entries.
mydatasort <- mydata[pv$hclust$labels[pv$hclust$order], hc$labels[hc$order]] # Sort rows in data table by 'dend_colored' and its colums by 'hc'.
x11(height=16, width=12); par(mfrow=c(1,2)); plot(dend_colored, horiz=T, yaxt="n"); image(scale(t(mydatasort)), col=my.colorFct(), xaxt="n",yaxt="n") # Plot heatmap with bootstrap tree in larger format using instead of heatmap the image function.
pdf("pvclust.pdf", height=21, width=6); plot(dend_colored, horiz=T, yaxt="n"); dev.off(); pdf("heatmap.pdf", height=20, width=6); image(scale(t(mydatasort)), col=my.colorFct(), xaxt="n",yaxt="n"); dev.off()
```
```{r Tailor-pathway-analysis,echo=FALSE, message=FALSE, warning=FALSE, cache=FALSE}
s.g.h.rep.matrix$EntrezID <- mapIds(org.Hs.eg.db,
keys=rownames(s.g.h.rep.matrix),
column="ENTREZID",keytype="SYMBOL",
multiVals="first")
s.g.h.rep.matrix$GO <- mapIds(org.Hs.eg.db,
keys=rownames(s.g.h.rep.matrix),
column="GO",keytype="SYMBOL",
multiVals="first")
s.g.l.rep.matrix$EntrezID <- mapIds(org.Hs.eg.db,
keys=rownames(s.g.l.rep.matrix),
column="ENTREZID",keytype="SYMBOL",
multiVals="first")
s.g.l.rep.matrix$GO <- mapIds(org.Hs.eg.db,
keys=rownames(s.g.l.rep.matrix),
column="GO",keytype="SYMBOL",
multiVals="first")
over.grp.fpkm.ma<-s.g.h.rep.matrix[,1:9]
under.grp.fpkm.ma<-s.g.l.rep.matrix[,1:9]
genes_exp.diff$entrez <- mapIds(org.Hs.eg.db,keys=genes_exp.diff$gene_id,
column="ENTREZID",keytype="SYMBOL",
multiVals="first")
foldchanges<-genes_exp.diff$log2_fold_change
isntna <- g.cnt.df[!is.na(g.cnt.df$EntrezID),]
#foldchanges <-foldchanges[isntna]
folddown<-order(foldchanges, decreasing = T)
foldchanges <- foldchanges[na.omit(names(foldchanges[folddown]))]
gene <- names(foldchanges)[abs(foldchanges) > 2]
foldchanges<-genes_exp.diff$log2_fold_change
names(foldchanges)<-genes_exp.diff$entrez
isntna <- !is.na(names(foldchanges))
foldchanges <-foldchanges[isntna]
folddown<-order(foldchanges, decreasing = T)
genes <- names(foldchanges)[abs(foldchanges) > 2]
det <- foldchanges[abs(foldchanges) > 2]
fold.change<-genes_exp.diff[,"log2_fold_change"]
foldchange<-sig_genes_exp.diff[,"log2_fold_change"]
over.= which(foldchange > 0, foldchange == "Inf", foldchange != "-Inf")
under. = which(foldchange < 0, foldchange == "-Inf")
HIexp.inOVER<-as.data.frame(sig_genes_exp.diff[over.,])
HIexp.inUNDER<-as.data.frame(sig_genes_exp.diff[under.,])
ENTREZQvalUNDERhi<-mapIds(x = org.Hs.eg.db,
keys = HIexp.inUNDER$gene_id,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals="first")
ENTREZQvalOVERhi<-mapIds(x = org.Hs.eg.db,
keys = HIexp.inOVER$gene_id,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals="first")
genes_exp.diff$entrezids<-mapIds(x = org.Hs.eg.db,
keys = genes_exp.diff$gene_id,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals="first")
sig_genes_exp.diff$entrezids<-mapIds(x = org.Hs.eg.db,
keys = sig_genes_exp.diff$gene_id,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals="first")
sig_genes_exp<-cbind(entrezids=sig_genes_exp.diff$entrezids,
genesymbols = sig_genes_exp.diff$gene_id,
pval=sig_genes_exp.diff[,"p_value"],
qval=sig_genes_exp.diff[,"q_value"],
log2fold=sig_genes_exp.diff[,"log2_fold_change"])
sig.genes.exp<-sig_genes_exp[which(!is.na(sig_genes_exp))]
genes.exp<-genes_exp.diff[which(!is.na(genes_exp.diff$entrezids)),]
over.group<-grep(pattern = over, x = colnames(g.rep.matrix),ignore.case = T)
under.group<-grep(pattern = under, x = colnames(g.rep.matrix),ignore.case = T)
g.under.matrix<-g.rep.matrix[,under.group]
g.over.matrix<-g.rep.matrix[,over.group]
genes<-rownames(genes.exp)
foldchange<-genes.exp[,"log2_fold_change"]
qval<-genes.exp[,"q_value"]
under.in = which(foldchange < 0, foldchange >= "-Inf", qval < 0.05)
over.in= which(foldchange > 0, foldchange < "Inf", qval < 0.05)
HIexp.inOVER<-as.data.frame(genes_exp.diff[over.in,])
HIexp.inOVER$q_value<-HIexp.inOVER[,"q_value"]
HIexp.inOVER$logFC<-HIexp.inOVER[,"log2_fold_change"]
ma<-max(HIexp.inOVER$logFC[is.finite(HIexp.inOVER$logFC)])
mi<-min(HIexp.inOVER$logFC[is.finite(HIexp.inOVER$logFC)])
HIexp.inOVER$logFC<-replace(HIexp.inOVER$logFC, HIexp.inOVER$logFC == "Inf", ma)
HIexp.inOVER$logFC<-replace(HIexp.inOVER$logFC, HIexp.inOVER$logFC == "-Inf", mi)
HIexp.inOVER$entrezid<-mapIds(x = org.Hs.eg.db,keys = HIexp.inOVER$gene_id,
column = "ENTREZID",keytype = "SYMBOL",multiVals="first")
HIexp.inOVER <- HIexp.inOVER[which(!is.na(HIexp.inOVER$entrezid)),]
isntna <- unique(na.omit(row.names(HIexp.inOVER)))
HIexp.inOVER <-HIexp.inOVER[isntna,]
rownames(HIexp.inOVER)<-HIexp.inOVER$entrezid
HIexp.inOVER<-HIexp.inOVER[,-4]
OVERexp.qval<-HIexp.inOVER$q_value
names(OVERexp.qval)<-row.names(HIexp.inOVER)
HIexp.inUNDER<-as.data.frame(genes_exp.diff[under.in,])
HIexp.inUNDER$q_value<-HIexp.inUNDER[,"q_value"]
HIexp.inUNDER$logFC<-HIexp.inUNDER[,"log2_fold_change"]
ma<-max(HIexp.inUNDER$logFC[is.finite(HIexp.inUNDER$logFC)])
mi<-min(HIexp.inUNDER$logFC[is.finite(HIexp.inUNDER$logFC)])
HIexp.inUNDER$logFC<-replace(HIexp.inUNDER$logFC, HIexp.inUNDER$logFC == "Inf", ma)
HIexp.inUNDER$logFC<-replace(HIexp.inUNDER$logFC, HIexp.inUNDER$logFC == "-Inf", mi)
HIexp.inUNDER$entrezid<-mapIds(x = org.Hs.eg.db,keys = HIexp.inUNDER$gene_id,
column = "ENTREZID",keytype = "SYMBOL",multiVals="first")
HIexp.inUNDER <- HIexp.inUNDER[which(!is.na(HIexp.inUNDER$entrezid)),]
rownames(HIexp.inUNDER)<-HIexp.inUNDER$entrezid
HIexp.inUNDER<-HIexp.inUNDER[,-4]
UNDERexp.qval<-HIexp.inUNDER$q_value
names(UNDERexp.qval)<-row.names(HIexp.inUNDER)
hsagene <- enrichKEGG(gene = genes, organism='hsa', pvalueCutoff = 0.05)
keggres <- gage(det, gsets=kegg.sets.hs, same.dir=TRUE)
# Get the pathways
kegguppathways <- data.frame(id=rownames(keggres$greater), keggres$greater) %>%
tbl_df() %>%
filter(row_number()<=15) %>%
.$id %>%
as.character()
keggdownpathways <- data.frame(id=rownames(keggres$less), keggres$less) %>%
tbl_df() %>%
filter(row_number()<=15) %>%
.$id %>%
as.character()
keggupsids <- substr(kegguppathways, start=1, stop=8)
keggdownsids <- substr(keggdownpathways, start=1, stop=8)
# plot multiple pathways (plots saved to disk and returns a throwaway list object)
tmp1 = sapply(keggupsids, function(pid) pathview(gene.data=foldchanges,
gene.idtype="ENTREZID", pathway.id=pid,
species="hsa"))
tmp2 = sapply(keggdownsids, function(pid) pathview(gene.data=foldchanges,
gene.idtype="ENTREZID", pathway.id=pid,
species="hsa"))
```
```{r gene-ontology-analysis, fig.cap="**Gene Ontology Over Representationa Analysis**", fig.align='center', fig.show='hold', message=FALSE, warning=FALSE, echo=FALSE}
gocc <- enrichGO(genes,'org.Hs.eg.db', ont="CC",pAdjustMethod = "BH",pvalueCutoff = 0.05, qvalueCutoff = 0.05)
gobp <- enrichGO(genes,'org.Hs.eg.db', ont="BP",pAdjustMethod = "BH",pvalueCutoff = 0.05, qvalueCutoff = 0.05)
gomf <- enrichGO(genes,'org.Hs.eg.db', ont="MF",pAdjustMethod = "BH",pvalueCutoff = 0.05, qvalueCutoff = 0.05)
sigGOcc <- groupGO(gene=ENTREZQvalOVERhi, OrgDb=org.Hs.eg.db, ont="CC",
level=3,readable = TRUE)
sigGOmf <- groupGO(gene=ENTREZQvalOVERhi, OrgDb=org.Hs.eg.db, ont="MF",
level=3,readable = TRUE)
sigGObp <- groupGO(gene=ENTREZQvalOVERhi, OrgDb=org.Hs.eg.db, ont="BP",
level=3,readable = TRUE)
xx <- annFUN.org("BP", mapping = "org.Hs.eg.db", ID = "symbol")
topDiffGenes <- function(allScore) {
return(allScore < 0.05)}
Qvalunder.BP.GOdata <- new("topGOdata",ontology = "BP", allGenes = UNDERexp.qval,
nodeSize = 10, annot = annFUN.org,mapping = "org.Hs.eg.db",
geneSel = topDiffGenes,ID = "entrez")
Qvalunder.MF.GOdata <- new("topGOdata",ontology = "MF",allGenes = UNDERexp.qval,
nodeSize = 10,annot = annFUN.org,mapping = "org.Hs.eg.db",
geneSel = topDiffGenes,ID = "entrez")
Qvalunder.CC.GOdata <- new("topGOdata",ontology = "CC",allGenes = UNDERexp.qval,
nodeSize = 10, annot = annFUN.org,mapping = "org.Hs.eg.db",
geneSel = topDiffGenes, ID = "entrez")
HIunder.BPtKS <- runTest(Qvalunder.BP.GOdata, algorithm = "classic", statistic = "ks")
HIunder.BPFisher <- runTest(Qvalunder.BP.GOdata, algorithm = "weight", statistic = "fisher")
HIunder.BPtKS.elim <- runTest(Qvalunder.BP.GOdata, algorithm = "elim", statistic = "ks")
HIunder.CCtKS <- runTest(Qvalunder.CC.GOdata, algorithm = "classic", statistic = "ks")
HIunder.CCFisher <- runTest(Qvalunder.CC.GOdata, algorithm = "weight", statistic = "fisher")
HIunder.CCtKS.elim <- runTest(Qvalunder.CC.GOdata, algorithm = "elim", statistic = "ks")
HIunder.MFtKS <- runTest(Qvalunder.MF.GOdata, algorithm = "classic", statistic = "ks")
HIunder.MFFisher <- runTest(Qvalunder.MF.GOdata, algorithm = "weight", statistic = "fisher")
HIunder.MFFtKS.elim <- runTest(Qvalunder.MF.GOdata, algorithm = "elim", statistic = "ks")
underRes.BP <- GenTable(Qvalunder.BP.GOdata, classic = HIunder.BPFisher, KS = HIunder.BPtKS,
weight = HIunder.BPtKS.elim, orderBy = "weight", ranksOf = "classic",
topNodes = 10)
underRes.MF <- GenTable(Qvalunder.MF.GOdata, classic = HIunder.MFFisher, KS = HIunder.MFtKS,
weight = HIunder.MFFtKS.elim, orderBy = "weight", ranksOf = "classic",
topNodes = 10)
underRes.CC <- GenTable(Qvalunder.CC.GOdata, classic = HIunder.CCFisher, KS = HIunder.CCtKS,
weight = HIunder.CCtKS.elim, orderBy = "weight", ranksOf = "classic",
topNodes = 10)
#---------------------------------------------------------------------------------------
Qvalover.BP.GOdata <- new("topGOdata",ontology = "BP",allGenes = OVERexp.qval,
nodeSize = 10, annot = annFUN.org,mapping = "org.Hs.eg.db",
geneSel = topDiffGenes,ID = "entrez")
Qvalover.MF.GOdata <- new("topGOdata",ontology = "MF",allGenes = OVERexp.qval,
nodeSize = 10, annot = annFUN.org,mapping = "org.Hs.eg.db",
geneSel = topDiffGenes, ID = "entrez")
Qvalover.CC.GOdata <- new("topGOdata",ontology = "CC",allGenes = OVERexp.qval,
nodeSize = 10, annot = annFUN.org,mapping = "org.Hs.eg.db",
geneSel = topDiffGenes, ID = "entrez")
HIover.MFtKS <- runTest(Qvalover.MF.GOdata, algorithm = "classic", statistic = "ks")
HIover.MFFisher <- runTest(Qvalover.MF.GOdata, algorithm = "weight", statistic = "fisher")
HIover.MFFtKS.elim <- runTest(Qvalover.MF.GOdata, algorithm = "elim", statistic = "ks")
HIover.CCtKS <- runTest(Qvalover.CC.GOdata, algorithm = "classic", statistic = "ks")
HIover.CCFisher <- runTest(Qvalover.CC.GOdata, algorithm = "weight", statistic = "fisher")
HIover.CCtKS.elim <- runTest(Qvalover.CC.GOdata, algorithm = "elim", statistic = "ks")
HIover.BPtKS <- runTest(Qvalover.BP.GOdata, algorithm = "classic", statistic = "ks")
HIover.BPFisher <- runTest(Qvalover.BP.GOdata, algorithm = "weight", statistic = "fisher")
HIover.BPtKS.elim <- runTest(Qvalover.BP.GOdata, algorithm = "elim", statistic = "ks")
overRes.BP <- GenTable(Qvalover.BP.GOdata, classic = HIover.BPFisher, KS = HIover.BPtKS,
weight = HIover.BPtKS.elim, orderBy = "weight", ranksOf = "classic",
topNodes = 10)
overRes.MF <- GenTable(Qvalover.MF.GOdata, classic = HIover.MFFisher, KS = HIover.MFtKS,
weight = HIover.MFFtKS.elim, orderBy = "weight", ranksOf = "classic",
topNodes = 10)
overRes.CC <- GenTable(Qvalover.CC.GOdata, classic = HIover.CCFisher, KS = HIover.CCtKS,
weight = HIover.CCtKS.elim, orderBy = "weight", ranksOf = "classic",
topNodes = 10)
```
```{r Gene-Ontology-Dotplots-CC-BP-MF}
dotplot(gocc, orderBy="Count", showCategory = 25,color="p.adjust", x="GeneRatio",
title="Gene Ontology Dotplot of Cellular Components")
dotplot(gobp, orderBy="Count", showCategory = 25,color="p.adjust", x="GeneRatio",
title="Gene Ontology Dotplot of Biological Processes")
dotplot(gomf, orderBy="Count", showCategory = 25, color="p.adjust", x="GeneRatio",
title="Gene Ontology Dotplot of Molecular Functions")
```
```{r limma-linear-models, message=FALSE, warning=FALSE, echo=FALSE}
lm<-lmFit(g.cnt.ma,design)
fit.cnt <- eBayes(lmFit(g.cnt.ma,design))
ebayes.dt <- decideTests(fit.cnt)
summary(ebayes.dt)
fit.lm <- lmFit(g.cnt.ma,design)
fit.bayes <- eBayes(fit.lm)
limma.res.cnt=topTable(fit.bayes,coef=1,n=Inf,sort="p")
f.bayes.limma <- decideTests(fit.bayes,adjust.method = "BH",lfc = 1, p.value = 0.05)
tfit1 <- treat(fit.bayes)
limma.siggenes<-subset(tfit1$p.value,tfit1$F.p.value < 0.05)
limma.res.cnt.sig<-subset(limma.res.cnt,
(limma.res.cnt$adj.P.Val < 0.05) & (limma.res.cnt$logFC > 1))
summary(tfit1)
fdr.rate=as.numeric(sum(limma.res.cnt.sig$P.Value <0.01))-sum(limma.res.cnt.sig$adj.P.Val <0.01)
limmapower<-1-fdr.rate/length(limma.res.cnt.sig$P.Value < 0.01)
limmapower
y <- cpm(g.cnt.ma,log=TRUE,prior.count=1)
fit <- lmFit(y,design,weights=NULL)
fit <- eBayes(fit,trend=TRUE)
dt <- decideTests(fit)
summary(dt)
# limma notrend - ranked by lods
fit <- eBayes(fit,trend=FALSE)
o <- order(fit$lods[,2], decreasing=TRUE)
# t-test
t.ord <- fit$coef[,2]/fit$stdev.unscaled[,2]/fit$sigma
p.ord <- pt(abs(t.ord),df=4,lower.tail=FALSE)*2
fdr.ord <- p.adjust(p.ord,method="BH")
o <- order(p.ord)
sum(fdr.ord<0.5)
```
```{r limma-voom-linear-models,fig.align="center", message=FALSE, warning=FALSE, echo=FALSE}
dgel<- DGEList(counts=g.cnt.ma, group=factor(groups))
dge.norm <- calcNormFactors(dgel)
log2.cpm <- voom(dge.norm,design,plot=T)
fit.lm <- lmFit(log2.cpm,design)
fit.bayes <- eBayes(fit.lm)
tfit1 <- treat(fit.bayes)
f.bayes.voom <- decideTests(fit.bayes)
voom.siggenes<-subset(tfit1$p.value,tfit1$F.p.value < 0.05)
voom.res.cnt=topTable(fit.bayes,coef=2,n=Inf,sort="p")
voom.res.cnt.sig<-subset(voom.res.cnt,
(voom.res.cnt$adj.P.Val < 0.01) & (voom.res.cnt$logFC > 2))
fdr.rate<-sum(tfit1$p.value <0.01)-sum(tfit1$F.p.value <0.01)
voompower<-1-fdr.rate/length(tfit1$p.value)
voompower
```
```{r edgeR-Diff-Expr, fig.margin=TRUE,fig.align="center", message=FALSE, warning=FALSE, echo=FALSE}
# Classic Approach without filtering
g.cnt.ma<-g.cnt.ma[row.names(g.cnt.ma),]
#design <- model.matrix(~0 + lanes, data=replicates.df)
#colnames(design)<-c("CTRL_L1T4", "CTRL_L5T8", "LUTS_L1T4", "LUTS_L5T8")
#row.names(design) <- replicates.df$rep_names
# contrast matrix
#contr.matrix <- makeContrasts(CTRL_L1T4 - LUTS_L1T4, CTRL_L1T4 - LUTS_L5T8,
# CTRL_L5T8 - LUTS_L5T8, CTRL_L5T8 - LUTS_L1T4,
# levels = c("CTRL_L1T4", "CTRL_L5T8", "LUTS_L1T4", "LUTS_L5T8"))
# Classic Approach without filtering
dgeObj <- DGEList(counts=g.cnt.ma, group=grp.fac)
dgeObj <- calcNormFactors(dgeObj,method = "TMM")
e.disp<-estimateDisp(dgeObj, design)
plotBCV(e.disp, main="Coefficient of Variation of Unfiltered Counts per Million")
qlf <- glmQLFit(e.disp, coef=1.5, dispersion=0.05)
plotQLDisp(qlf)
qlft <- glmQLFTest(qlf, coef=1.5)
topTags(qlft)
is.de <- decideTests(qlft, p.value=0.01)
summary(is.de)
sp <- diffSpliceDGE(qlft, geneid="GeneID")
exactTst <- exactTest(qlf)
ex.sig <- subset(exactTst$table, (PValue < 0.05))
z <- estimateGLMTrendedDisp(g.cnt.ma,design)
fite <- glmFit(g.cnt.ma,design,dispersion=z)
lrt <- glmLRT(fite, contrast=contr.matrix)
dt <- decideTests(lrt)
lrt.sig <- subset(lrt$table, (PValue < 0.05))
sum(p.adjust(lrt$table$PValue,method="BH")<0.05)
topTags(lrt)
d<- DGEList(counts=g.cnt.ma, group=grp.fac,
remove.zeros=TRUE,genes = row.names(g.cnt.ma))
d <- calcNormFactors(d)
d = estimateCommonDisp(d)
d = estimateTagwiseDisp(d)
exactTst <- exactTest(d)
exactTest.top <- subset(exactTst$table, (PValue < 0.05))
edgeR.sigGenes<-rownames(exactTest.top)
# length(edgeR.sigGenes)
## Identify genes with at least 1 cpm in at least all of the samples of a group
keep.exprs <- filterByExpr(d, group=grp.fac)
counts = d[keep.exprs,]
# Classic Approach after filtering
DGEobj.f=DGEList(counts,group=grp.fac, remove.zeros=TRUE,genes = row.names(counts))
DGEobj.f = calcNormFactors(DGEobj.f)
DGEobj.f = estimateCommonDisp(DGEobj.f)
DGEobj.f = estimateTagwiseDisp(DGEobj.f)
DGEobj.d <- estimateDisp(DGEobj.f,design)
d2 <- estimateGLMCommonDisp(d,design)
d2 <- estimateGLMTrendedDisp(d2,design, method="auto")
# You can change method to "auto", "bin.spline", "power", "spline", "bin.loess".