geom_text(data = ann_text, mapping = aes(x=group,y=expr,label = label))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(13,12,13,12),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text, mapping = aes(x=group,y=expr,label = label))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
ann_text2
tlfc
rlfc
rlfc <- paste('LFC=',rlfc,sep='')
rlfc
top2 <- topTable(fit2,coef=1,number=Inf,sort.by="P") # ritux
toadd <- subset(top2,row.names(top2)==gene)
rp <- as.numeric(toadd[4])
rp <- signif(rp,3)
rlfc <- as.numeric(toadd[1])
rlfc <- signif(rlfc,3)
rp <- paste('p=',rp,sep='')
rlfc <- paste('LFC=',rlfc,sep='')
top2 <- topTable(fit2,coef=2,number=Inf,sort.by="P") # tocil
toadd <- subset(top2,row.names(top2)==gene)
tp <- as.numeric(toadd[4])
tp <- signif(tp,3)
tlfc <- as.numeric(toadd[1])
tlfc <- signif(tlfc,3)
tp <- paste('p=',tp,sep='')
tlfc <- paste('LFC=',tlfc,sep='')
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(13,12,13,12),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(13,12.7,13,12.7),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(13,12.65,13,12.65),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- fry(v,c2.indices,design,contrast=contrast.matrix[,3])
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- fry(v,c2.indices,design,contrast=cont.matrix[,3])
View(fryresults)
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- fry(v,c2.indices,design,contrast=cont.matrix[,1])
View(fryresults)
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- camera(v,c2.indices,design,contrast=cont.matrix[,1])
View(fryresults)
# do fry analysis
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- camera(v,c2.indices,design,contrast=cont.matrix[,2])
View(fryresults)
?camera
fryresults <- camera(v,c2.indices,design,contrast=2)
View(fryresults)
?camera
fryresults <- camera(v,c2.indices,design,contrast=2,inter.gene.cor = NA)
View(fryresults)
fryresults <- camera(v,c2.indices,design,contrast=1,inter.gene.cor = NA)
fryresults <- camera(v,c2.indices,design,contrast=3,inter.gene.cor = NA)
# do fry analysis
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- fry(v,c2.indices,design,contrast=1)
View(fryresults)
?fry
head(design)
View(fryresults)
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- fry(v,c2.indices,design,contrast=3)
View(fryresults)
head(cont.matrix)
fryresults <- fry(v,c2.indices,design,contrast=contrast.matrix[,1])
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- fry(v,c2.indices,design,contrast=cont.matrix[,1])
View(fryresults)
qs <- qvalue::qvalue(p = fryresults$PValue)
fryresults$q.Val <- qs$qvalues
View(fryresults)
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- fry(v,c2.indices,design,contrast=cont.matrix[,2])
qs <- qvalue::qvalue(p = fryresults$PValue)
fryresults$q.Val <- qs$qvalues
head(cont.matrix)
View(fryresults)
# do fry analysis
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresults <- fry(v,c2.indices,design,contrast=cont.matrix[,3])
qs <- qvalue::qvalue(p = fryresults$PValue)
fryresults$q.Val <- qs$qvalues
View(fryresults)
head(cont.matrix)
# do fry analysis
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('tmod')
fryresults <- fry(v,c2.indices,design,contrast=cont.matrix[,3])
View(fryresults)
# do fry analysis
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('tmod')
fryresults <- fry(v,c2.indices,design,contrast=cont.matrix[,1])
View(fryresults)
fryresultsr <- fry(v,c2.indices,design,contrast=cont.matrix[,1])
fryresultst <- fry(v,c2.indices,design,contrast=cont.matrix[,2])
View(fryresultsr)
View(fryresults)
View(fryresultst)
# do fry analysis
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('AMP')
fryresultsr <- fry(v,c2.indices,design,contrast=cont.matrix[,1])
fryresultst <- fry(v,c2.indices,design,contrast=cont.matrix[,2])
View(fryresultsr)
View(fryresultst)
source('~/Desktop/scripts/parsemodulesforcameraV3.R')
c2.indices <- loadmodules('wgcna')
fryresultsr <- fry(v,c2.indices,design,contrast=cont.matrix[,1])
fryresultst <- fry(v,c2.indices,design,contrast=cont.matrix[,2])
View(fryresultsr)
View(fryresultst)
gene <- 'PAX5'
#subset(top2, row.names(top2) == 'PAX5')
## set up data frame for plot
vstb <- vst[,c(meta$Seq_ID.V2)]
MS4A1 <- vstb[row.names(vstb)==gene,]
# turn into data frame with group
genedf <- data.frame(group=meta$Visit,expr=as.numeric(MS4A1),medication=meta$Randomised.medication,
patient=as.character(meta$Patient_ID))
# get expression value for each group (do we need to plot standard error/ deviation here?)
genedf2 <- aggregate( expr ~ group, genedf, mean )
## set up text to add to plot (p value and LFC)
top2 <- topTable(fit2,coef=1,number=Inf,sort.by="P") # ritux
toadd <- subset(top2,row.names(top2)==gene)
rp <- as.numeric(toadd[4])
rp <- signif(rp,3)
rlfc <- as.numeric(toadd[1])
rlfc <- signif(rlfc,3)
rp <- paste('p=',rp,sep='')
rlfc <- paste('LFC=',rlfc,sep='')
top2 <- topTable(fit2,coef=2,number=Inf,sort.by="P") # tocil
toadd <- subset(top2,row.names(top2)==gene)
tp <- as.numeric(toadd[4])
tp <- signif(tp,3)
tlfc <- as.numeric(toadd[1])
tlfc <- signif(tlfc,3)
tp <- paste('p=',tp,sep='')
tlfc <- paste('LFC=',tlfc,sep='')
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(13,12.6,13,12.6),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
genedf
genedf$expr
range(genedf$expr)
range(genedf$expr)[2]
max <- range(genedf$expr)[2]
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(max,max-0.4,max,max-0.4),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
range(genedf$expr)
range(genedf$expr)[2]
range(genedf$expr)[2]-range(genedf$expr)[1]
(range(genedf$expr)[2]-range(genedf$expr)[1])/100
unit <- (range(genedf$expr)[2]-range(genedf$expr)[1])/100
unit
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(max,max-0.25,max,max-0.25),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
unit <- unit/3
unit
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(max,max-unit,max,max-unit),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
unt
unit
unit <- (range(genedf$expr)[2]-range(genedf$expr)[1])/100
unit
unit*10
unit*5
unit*4
max <- range(genedf$expr)[2]
unit <- (range(genedf$expr)[2]-range(genedf$expr)[1])/100
unit <- unit*4
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(max,max-unit,max,max-unit),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
gene <- 'CD79A'
#subset(top2, row.names(top2) == 'PAX5')
## set up data frame for plot
vstb <- vst[,c(meta$Seq_ID.V2)]
MS4A1 <- vstb[row.names(vstb)==gene,]
# turn into data frame with group
genedf <- data.frame(group=meta$Visit,expr=as.numeric(MS4A1),medication=meta$Randomised.medication,
patient=as.character(meta$Patient_ID))
# get expression value for each group (do we need to plot standard error/ deviation here?)
genedf2 <- aggregate( expr ~ group, genedf, mean )
## set up text to add to plot (p value and LFC)
top2 <- topTable(fit2,coef=1,number=Inf,sort.by="P") # ritux
toadd <- subset(top2,row.names(top2)==gene)
rp <- as.numeric(toadd[4])
rp <- signif(rp,3)
rlfc <- as.numeric(toadd[1])
rlfc <- signif(rlfc,3)
rp <- paste('p=',rp,sep='')
rlfc <- paste('LFC=',rlfc,sep='')
top2 <- topTable(fit2,coef=2,number=Inf,sort.by="P") # tocil
toadd <- subset(top2,row.names(top2)==gene)
tp <- as.numeric(toadd[4])
tp <- signif(tp,3)
tlfc <- as.numeric(toadd[1])
tlfc <- signif(tlfc,3)
tp <- paste('p=',tp,sep='')
tlfc <- paste('LFC=',tlfc,sep='')
max <- range(genedf$expr)[2]
unit <- (range(genedf$expr)[2]-range(genedf$expr)[1])/100
unit <- unit*4
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(max,max-unit,max,max-unit),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
gene <- 'IL6R'
#subset(top2, row.names(top2) == 'PAX5')
## set up data frame for plot
vstb <- vst[,c(meta$Seq_ID.V2)]
MS4A1 <- vstb[row.names(vstb)==gene,]
# turn into data frame with group
genedf <- data.frame(group=meta$Visit,expr=as.numeric(MS4A1),medication=meta$Randomised.medication,
patient=as.character(meta$Patient_ID))
# get expression value for each group (do we need to plot standard error/ deviation here?)
genedf2 <- aggregate( expr ~ group, genedf, mean )
## set up text to add to plot (p value and LFC)
top2 <- topTable(fit2,coef=1,number=Inf,sort.by="P") # ritux
toadd <- subset(top2,row.names(top2)==gene)
rp <- as.numeric(toadd[4])
rp <- signif(rp,3)
rlfc <- as.numeric(toadd[1])
rlfc <- signif(rlfc,3)
rp <- paste('p=',rp,sep='')
rlfc <- paste('LFC=',rlfc,sep='')
top2 <- topTable(fit2,coef=2,number=Inf,sort.by="P") # tocil
toadd <- subset(top2,row.names(top2)==gene)
tp <- as.numeric(toadd[4])
tp <- signif(tp,3)
tlfc <- as.numeric(toadd[1])
tlfc <- signif(tlfc,3)
tp <- paste('p=',tp,sep='')
tlfc <- paste('LFC=',tlfc,sep='')
max <- range(genedf$expr)[2]
unit <- (range(genedf$expr)[2]-range(genedf$expr)[1])/100
unit <- unit*4
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(max,max-unit,max,max-unit),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
gene <- 'IL6ST'
#subset(top2, row.names(top2) == 'PAX5')
## set up data frame for plot
vstb <- vst[,c(meta$Seq_ID.V2)]
MS4A1 <- vstb[row.names(vstb)==gene,]
# turn into data frame with group
genedf <- data.frame(group=meta$Visit,expr=as.numeric(MS4A1),medication=meta$Randomised.medication,
patient=as.character(meta$Patient_ID))
# get expression value for each group (do we need to plot standard error/ deviation here?)
genedf2 <- aggregate( expr ~ group, genedf, mean )
## set up text to add to plot (p value and LFC)
top2 <- topTable(fit2,coef=1,number=Inf,sort.by="P") # ritux
toadd <- subset(top2,row.names(top2)==gene)
rp <- as.numeric(toadd[4])
rp <- signif(rp,3)
rlfc <- as.numeric(toadd[1])
rlfc <- signif(rlfc,3)
rp <- paste('p=',rp,sep='')
rlfc <- paste('LFC=',rlfc,sep='')
top2 <- topTable(fit2,coef=2,number=Inf,sort.by="P") # tocil
toadd <- subset(top2,row.names(top2)==gene)
tp <- as.numeric(toadd[4])
tp <- signif(tp,3)
tlfc <- as.numeric(toadd[1])
tlfc <- signif(tlfc,3)
tp <- paste('p=',tp,sep='')
tlfc <- paste('LFC=',tlfc,sep='')
max <- range(genedf$expr)[2]
unit <- (range(genedf$expr)[2]-range(genedf$expr)[1])/100
unit <- unit*4
ann_text <- data.frame(group=c(6.5,6.5),expr=c(13,13),label=c(rp,tp),
medication=factor(c('Rituximab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
ann_text2 <- data.frame(group=c(6.5,6.5,6.5,6.5),expr=c(max,max-unit,max,max-unit),label=c(rp,rlfc,tp,tlfc),
medication=factor(c('Rituximab','Rituximab','Tocilizumab','Tocilizumab')),
patient=factor(NA,levels=levels(genedf$patient)))
## do plot
fsize <- 12
ggplot(data=genedf, aes(x=group, y=expr, group=patient)) +
geom_point() + geom_line() + facet_wrap(~medication) + theme_bw() +
theme(strip.background = element_blank(),
strip.text = element_text(size=12)) + scale_x_continuous(breaks=c(3,7)) +
theme(plot.title = element_text(size = fsize, hjust = 0.5),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_rect(colour = "white", fill = "white"),
axis.text.y = element_text(size = fsize, colour = 'black'),
axis.text.x =  element_text(size = fsize, colour = 'black'),
axis.title.x = element_text(size = fsize, colour = 'black'),
axis.title.y = element_text(size = fsize, colour = 'black')) +
ylab('Normalised expression') + xlab('Visit') +
geom_text(data = ann_text2, mapping = aes(x=group,y=expr,label = label))
