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Neuropeptides.r
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Neuropeptides.r
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# Figures - Smith et al
library(pheatmap)
library(RColorBrewer)
library(colorspace)
library(R.utils)
library(stringr)
library(superheat)
library(sm)
library(vioplot)
# Read in processed data table for CPM and FPKM values from Tasic et al, Nature, 2018 Nov;563(7729):72-78
# This table contains expression profiles for 23,823 cells from VISp and ALM cortical regions together with laminar location and cluster id.
# All figures and analyses can be generated from this code.
np_gpcr_cpm=as.data.frame(read.csv("/volumes/PHILIPS UFD/Analysis/Neuropeptides/Submission/np_gpcr_cpm.csv"))
np_gpcr_cpm=as.data.frame(read.csv("F:\\Analysis\\Neuropeptides\\Submission\\np_gpcr_cpm.csv"))
# membership of cell types in Tasic and membership in major cell types
celltype=read.csv("/volumes/PHILIPS UFD/Analysis/Neuropeptides/Submission/CellTypeAnnotation.csv",header=F)
celltype=read.csv("F:\\Analysis\\Neuropeptides\\Submission\\CellTypeAnnotation.csv",header=F)
# Gene classes of interest: NPP
nppm=c("Npy","Sst","Vip","Tac2", "Cck","Penk","Crh","Cort", "Tac1", "Pdyn", "Pthlh", "Pnoc","Trh","Grp","Rln1","Adcyap1","Nts","Nmb")
npph=toupper(nppm)
# NP-GPCR
npgpcrm=c("Sstr2", "Npy2r", "Npy1r", "Grpr", "Cckbr", "Ntsr2", "Npy5r", "Nmbr", "Rxfp1", "Sstr4",
"Trhr","Sstr1","Adcyap1r1","Crhr1","Rxfp3","Oprl1","Crhr2","Tacr3","Oprk1","Tacr1",
"Pth1r","Vipr1","Oprm1","Trhr2","Vipr2","Rxfp2","Oprd1","Ntsr1","Sstr3")
# twelve basic cell types for pooling results to coarser resolution
ctpool=c("IT","PT","NP","CT","L6b","Lamp5","Sncg","Serpinf1","VIP","Sst Chodl","Sst","Pvalb")
ctcolors=c("#A1D5BA","#7AB7AB","#BEDFCB","#9ECAE8","#91ACBA","#F2ABB7","#C387E2","#E6B8FF","#D5A8DF","#FEFF99","#E0B889","#EE98A4")
names(ctcolors)=ctpool
# totalnumber of types in taxonomy
cellind=1:133
# cell type membership data, types and colors for VISp and ALM regional analysis
allclust=read.csv("/volumes/PHILIPS UFD/Analysis/Neuropeptides/Submission/All_Types.csv")
allclust=read.csv("F:\\Analysis\\Neuropeptides\\Submission\\All_Types.csv")
allclid=as.integer(allclust[,2])
alldend=as.integer(allclust[,1])
almclust=read.csv("/volumes/PHILIPS UFD/Analysis/Neuropeptides/Submission/AlM_Types.csv")
almclust=read.csv("F:\\Analysis\\Neuropeptides\\Submission\\ALM_Types.csv")
almclid=as.integer(almclust[,2])
almdend=as.integer(almclust[,1])
visclust=read.csv("/volumes/PHILIPS UFD/Analysis/Neuropeptides/Submission/VISp_Types.csv")
visclust=read.csv("F:\\Analysis\\Neuropeptides\\Submission\\VISp_Types.csv")
visclid=as.integer(visclust[,2])
visdend=as.integer(visclust[,1])
# Cell type lists for pooling to 12 major types, these give the index map for the 12 major cell types for each region
vislist=NULL
icount=0
for (i in 1:12) {
itemp=intersect(which(celltype[,4]==i),visdend)
vislist[[i]]=c((icount+1):(icount+length(itemp)))
icount=icount+length(itemp)
}
almlist=NULL
icount=0
for (i in 1:12) {
itemp=intersect(which(celltype[,4]==i),almdend)
almlist[[i]]=c((icount+1):(icount+length(itemp)))
icount=icount+length(itemp)
}
alllist=NULL
icount=0
for (i in 1:12) {
itemp=intersect(which(celltype[,4]==i),alldend)
alllist[[i]]=c((icount+1):(icount+length(itemp)))
icount=icount+length(itemp)
}
# table of 37 interaction pairs and
inpairs=read.csv("F:\\Analysis\\Neuropeptides\\Submission\\CognatePairs.csv",header=FALSE)
inpairs=read.csv("/volumes/PHILIPS UFD/Analysis/Neuropeptides/Submission/CognatePairs.csv",header=FALSE)
######################################################
# Figure 1B. Fraction of Cells Expressing NPP
######################################################
# Note: run all plotting subroutines below
# ALM - NPP
d1=subset(np_gpcr_cpm,brain_region_label=="ALM",nppm)
cvec=sum(apply(d1,1,sum)>0)
totcell=dim(d1)[1]
bimat=d1>0
almcnt=c(1,cvec/totcell,apply(bimat,2,sum)/totcell)
b1=almcnt[-1]
names(almcnt)[1]="ALM"
names(almcnt)[2]="Any NPP"
names(b1)[1]="Any"
pervec_alm=almcnt[c(-1,-2)]
# VISp - NPP
d1=subset(np_gpcr_cpm,brain_region_label=="VISp",nppm)
cvec=sum(apply(d1,1,sum)>0)
totcell=dim(d1)[1]
bimat=d1>0
almcnt=c(1,cvec/totcell,apply(bimat,2,sum)/totcell)
b2=almcnt[-1]
names(almcnt)[1]="VISp"
names(almcnt)[2]="Any NPP"
pervec_visp=almcnt[c(-1,-2)]
# dual plot
c1=rbind(b1,b2)
row.names(c1)=c("ALM","VISp")
cr1=terrain_hcl(19)[5]
cr2=terrain_hcl(19)[11]
par(las=2)
par(fig = c(0,1,0,1))
barplot(c1,col=rbind(rep(cr1,14),rep(cr2,14)),ylim=c(0,1),beside=TRUE,main="NPP Expressing Cells - ALM and VISp")
legend(40,0.8,legend=c("ALM","VISp"),col=c(cr1,cr2),pch=c(15,15),box.lty=0,pt.cex=1.5,cex=1.25)
##########################################################
# Figure 1C. Histogram for NPP Expressing Cells
##########################################################
d1=subset(np_gpcr_cpm,brain_region_label=="ALM",nppm)
totcell=dim(d1)[1]
dpos=d1>0
fvec=apply(dpos,1,sum)
tvec=table(fvec)
tvecn=tvec/sum(tvec[2:length(tvec)])
tvec1=tvecn
d1=subset(np_gpcr_cpm,brain_region_label=="VISp",nppm)
totcell=dim(d1)[1]
dpos=d1>0
fvec=apply(dpos,1,sum)
tvec=table(fvec)
tvecn=tvec/sum(tvec[2:length(tvec)])
tvec2=tvecn
# dual plot
c1=rbind(b1,b2)
row.names(c1)=c("ALM","VISp")
cr1=terrain_hcl(19)[5]
cr2=terrain_hcl(19)[11]
# in case one array is longer pad with terminal zeros
difl=length(tvec1)-length(tvec2)
if (difl > 0) {
tvec2=c(tvec2,rep(0,difl))
names(tvec2)=names(tvec1)
}
if (difl < 0) {
tvec1=c(tvec1,rep(0,abs(difl)))
names(tvec1)=names(tvec2)
}
outr=rbind(tvec1,tvec2)
par(las=1)
barplot(outr,col=rbind(rep(cr1,12),rep(cr2,12)),ylim=c(0,0.2),beside=TRUE,
ylab="Histogram Frac. NPP expressing",cex.names=1.2)
title(main="Histogram of NPP expressing cells in ALM and VISp")
par(mar=c(5, 4, 4, 2.7) + 0.1)
legend(30,0.15,legend=c("ALM","VISp"),col=c(cr1,cr2),pch=c(15,15),box.lty=0,pt.cex=1.5,cex=1.25)
#########################################################
# Figure 2B. Fraction of Cells Expressing NP-GPCR
#########################################################
d1=subset(np_gpcr_cpm,brain_region_label=="ALM",npgpcrm)
cvec=sum(apply(d1,1,sum)>0)
totcell=dim(d1)[1]
bimat=d1>0
almcnt=c(1,cvec/totcell,apply(bimat,2,sum)/totcell)
b1=almcnt[-1]
names(almcnt)[1]="ALM"
names(b1)[1]="Any NP-GPCR"
pervec_alm=almcnt[c(-1,-2)]
d1=subset(np_gpcr_cpm,brain_region_label=="VISp",npgpcrm)
cvec=sum(apply(d1,1,sum)>0)
totcell=dim(d1)[1]
bimat=d1>0
almcnt=c(1,cvec/totcell,apply(bimat,2,sum)/totcell)
b2=almcnt[-1]
names(almcnt)[1]="ALM"
names(almcnt)[2]="Any NP-GPCR"
names(b1)[1]="Any NP-GPCR"
pervec_alm=almcnt[c(-1,-2)]
# dual plot
c1=rbind(b1,b2)
row.names(c1)=c("ALM","VISp")
cr1=rainbow_hcl(19)[5]
cr2=rainbow_hcl(19)[11]
par(las=2)
barplot(c1,col=rbind(rep(cr1,14),rep(cr2,14)),ylim=c(0,1),beside=TRUE,main="NP-GPCR Expressing Cells - ALM and VISp")
legend(70,0.8,legend=c("ALM","VISp"),col=c(cr1,cr2),pch=c(15,15),box.lty=0,pt.cex=1.5,cex=1.25)
###################################################################
# Fraction of cell types expressing NP-GPCR and cognate pairs
###################################################################
commat=matrix(0,dim(inpairs)[1],118)
for (i in 1:dim(inpairs)[1]) {
np1=as.character(inpairs[i,2])
np2=as.character(inpairs[i,3])
ipos=which(((np_gpcr_cpm[,np1]>0)*(np_gpcr_cpm[,np2]>0))==1)
lipos=length(ipos)
itab=table((np_gpcr_cpm[,"cluster_id"])[ipos])[alldend]
commat[i,as.integer(names(itab))]=as.integer(itab)
}
##########################################################
# Figure 2C. Histogram for NP-GPCR Expressing Cells
##########################################################
d1=subset(np_gpcr_cpm,brain_region_label=="ALM",npgpcrm)
totcell=dim(d1)[1]
dpos=d1>0
fvec=apply(dpos,1,sum)
tvec=table(fvec)
tvecn=tvec/sum(tvec[2:length(tvec)])
tvec1=tvecn
d1=subset(np_gpcr_cpm,brain_region_label=="VISp",npgpcrm)
totcell=dim(d1)[1]
dpos=d1>0
fvec=apply(dpos,1,sum)
tvec=table(fvec)
tvecn=tvec/sum(tvec[2:length(tvec)])
tvec2=tvecn
# dual plot
c1=rbind(tvec1,tvec2)
row.names(c1)=c("ALM","VISp")
cr1=rainbow_hcl(19)[5]
cr2=rainbow_hcl(19)[11]
# in case one array is longer pad with terminal zeros
difl=length(tvec1)-length(tvec2)
if (difl > 0) {
tvec2=c(tvec2,rep(0,difl))
names(tvec2)=names(tvec1)
}
if (difl < 0) {
tvec1=c(tvec1,rep(0,abs(difl)))
names(tvec1)=names(tvec2)
}
outr=rbind(tvec1,tvec2)
par(las=1)
barplot(outr,col=rbind(rep(cr1,12),rep(cr2,12)),ylim=c(0,0.2),beside=TRUE,
ylab="Histogram Frac. NP-GPCR Expressing")
title(main="Histogram of NP-GPCR expressing cells in ALM and VISp")
par(mar=c(5, 4, 4, 2.7) + 0.1)
legend(40,0.15,legend=c("ALM","VISp"),col=c(cr1,cr2),pch=c(15,15),box.lty=0,pt.cex=1.5,cex=1.25)
#####################################################################################
# Figure 3A. Basic expression plots and variability for NPP - Combined VISp and ALM
#####################################################################################
alltyp=133
clabel=paste("CT",c(1:alltyp),sep="")
nppmat=matrix (0,length(nppm),alltyp,dimnames=list(nppm,clabel))
for (i in 1:length(nppm)) {
ip1=which(names(np_gpcr_cpm)==nppm[i])
for (j in 1:alltyp) {
ip2=which(np_gpcr_cpm[,6]==j)
nppmat[i,j]=mean(np_gpcr_cpm[ip2,ip1],trim=0.05)
}
}
vmat=nppmat[,allclid]
# log before normalization
vmat=log10(vmat+1)
# before normalization and plotting, compute mean variation within major cell types compared to total variation
varmat=matrix(0,dim(vmat)[1],length(ctpool)+1,dimnames=list(rownames(vmat),c("All",ctpool)))
for (i in 1:dim(vmat)[1]) {
varmat[i,1]=var(vmat[i,])/mean(vmat[i,])
}
for (i in 1:dim(vmat)[1]) {
for (j in 1:length(ctpool)) {
if (length(vmat[i,alllist[[j]]]) > 1)
if (mean(vmat[i,alllist[[j]]])>0) {
varmat[i,j+1]=var(vmat[i,alllist[[j]]])/mean(vmat[i,alllist[[j]]])
}
}
}
varmat_npp=varmat
#####################################################################
# Figure 3C. Violin plots NPP variability over major types
#####################################################################
# create data frame for variability plots
varmatm=as.data.frame(varmat[,-11])
labn=colnames(varmatm)
longd=NULL
for (j in 1:dim(varmatm)[2]) {
longd=rbind(longd,cbind(varmatm[,j],rep(labn[j],dim(varmatm)[1])))
}
longdf=as.data.frame(longd)
colnames(longdf)=c("dat","reg")
longdfp=longdf[as.numeric(as.character(longdf[,1]))>0,]
cvec=c(rgb(0.75,0.75,0.75),ctcolors)
names(cvec)[1]="All"
lcvec=NULL
for (i in 1:12) {
lcvec=c(lcvec,rep(cvec[i],18))
}
longdf2=cbind(longdf,lcvec)
par(fig = c(0,1,0,1))
par(las=1)
vioplot(as.numeric(as.character(longdfp[longdfp$reg=="All",1])),as.numeric(as.character(longdfp[longdfp$reg=="IT",1])),
as.numeric(as.character(longdfp[longdfp$reg=="PT",1])),as.numeric(as.character(longdfp[longdfp$reg=="NP",1])),
as.numeric(as.character(longdfp[longdfp$reg=="CT",1])),as.numeric(as.character(longdfp[longdfp$reg=="L6b",1])),
as.numeric(as.character(longdfp[longdfp$reg=="Lamp5",1])),as.numeric(as.character(longdfp[longdfp$reg=="Sncg",1])),
as.numeric(as.character(longdfp[longdfp$reg=="Serpinf1",1])),as.numeric(as.character(longdfp[longdfp$reg=="VIP",1])),
as.numeric(as.character(longdfp[longdfp$reg=="Sst",1])),as.numeric(as.character(longdfp[longdfp$reg=="Pvalb",1])),col=cvec[-11],
names=names(cvec)[-11],cex.axis=1.75,main="NPP expression variability",cex.main=1.5,axes=F,ylim=c(0,2.5),
font.axis=1)
points(2,max(varmatm[,2]),cex=1.25,pch=16)
points(3,max(varmatm[,3]),cex=1.25,pch=16)
points(4,max(varmatm[,4]),cex=1.25,pch=16)
points(5,max(varmatm[,5]),cex=1.25,pch=16)
points(6,max(varmatm[,6]),cex=1.25,pch=16)
points(7,max(varmatm[,7]),cex=1.25,pch=16)
points(8,max(varmatm[,8]),cex=1.25,pch=16)
points(9,max(varmatm[,9]),cex=1.25,pch=16)
points(10,max(varmatm[,10]),cex=1.25,pch=16)
points(11,max(varmatm[,11]),cex=1.25,pch=16)
points(11,max(varmatm[,11]),cex=1.25,pch=16)
points(12,max(varmatm[,12]),cex=1.25,pch=16)
points(12,max(varmatm[,12]),cex=1.25,pch=16)
# barplot for ratios on same plot
mbarv=vector("numeric",11)
mall=mean(varmatm[,1])
for (j in 2:dim(varmatm)[2]) {
mbarv[j-1]=mean(varmatm[varmatm[,j]>0,j])/mall
}
par(fig = c(0.75,0.95,0.65,1.0),new=TRUE,las=2)
barplot(mbarv,names.arg=colnames(varmatm)[-1],col=ctcolors[-10],cex.names=0.75,cex.axis=0.75,ylab="Relative CV")
abline(h=mean(mbarv),lty=2)
# Supplementary data for Figure 3 variability plots
varmatn=varmat[,-c(1,11)]
sumvec=apply(varmatn,2,mean)
ov=order(sumvec,decreasing=T)
pmat=varmatn[,ov]
covo=names(sumvec[ov])
pmatn=pmat
mypal=colorRampPalette(c(rgb(1,1,1),rgb(0.25,0.25,0.25)),bias=3)(10)
mypal=rev(colorRampPalette(brewer.pal(8,"Spectral"),bias=1)(8))
c1=brewer.pal(8,"Spectral")[1]
c8=brewer.pal(8,"Spectral")[8]
mypal=rev(colorRampPalette(c(rgb(0.75,0.4,1,0.75), rgb(0.75,0.4,1,0)), alpha = TRUE)(8))
imname="/volumes/PHILIPS UFD/Analysis/Neuropeptides/Submission/cell_type_variability_npp.tiff"
imname="F:\\Analysis\\Neuropeptides\\Submission\\cell_type_variability_npp.tiff"
tiff(imname,width=1500,height=1100)
par(cex.axis=1.5)
superheat(X=pmatn,left.label.col=c("ivory","ivory2"),bottom.label.col=ctcolors[covo],legend.text.size=14,
left.label.text.alignment="center",X.text=round(pmatn,2),X.text.size=8,heat.pal=c("white","tan1"),yt=sumvec[covo],
yt.axis.name="Mean cell type CV / Global CV",yt.plot.type="bar",yt.axis.size=18,yt.axis.name.size=16,yt.obs.col=ctcolors[covo],
left.label.text.size=8,bottom.label.text.size=9)
dev.off()
# normalalize to row maximum
for (i in 1:dim(vmat)[1]) {
print(paste(rownames(nppmat)[i],round(max(vmat[i,]),3)))
vmat[i,]=vmat[i,]/max(vmat[i,])
}
# run plotting subroutines first
plot_All(vmat,"NPP expression in ALM and VISp",1)
# expression pooled to 12 major types
plot_All_pooled(vmat,"NPP expression in ALM and VISp",1)
############################################################
# Figure 3B. Basic expression plots for NP-GPCR - Combined
############################################################
alltyp=133
clabel=paste("CT",c(1:alltyp),sep="")
nppmat=matrix(0,length(npgpcrm),alltyp,dimnames=list(npgpcrm,clabel))
for (i in 1:length(npgpcrm)) {
ip1=which(names(np_gpcr_cpm)==npgpcrm[i])
for (j in 1:alltyp) {
ip2=which(np_gpcr_cpm[,6]==j)
nppmat[i,j]=mean(np_gpcr_cpm[ip2,ip1],trim=0.05)
}
}
vmat=nppmat[,allclid]
# log before normalization
vmat=log10(vmat+1)
# before normalization and plotting, compute mean variation within major cell types compared to total variation
varmat=matrix(0,dim(vmat)[1],length(ctpool)+1,dimnames=list(rownames(vmat),c("All",ctpool)))
for (i in 1:dim(vmat)[1]) {
varmat[i,1]=var(vmat[i,])/mean(vmat[i,])
}
for (i in 1:dim(vmat)[1]) {
for (j in 1:length(ctpool)) {
if (length(vmat[i,alllist[[j]]]) > 1)
if (mean(vmat[i,alllist[[j]]])>0) {
varmat[i,j+1]=var(vmat[i,alllist[[j]]])/mean(vmat[i,alllist[[j]]])
}
}
}
varmat_npgpcr=varmat
#####################################################################
# Figure 3D. Violin plots for NP-GPCR variability over major types
#####################################################################
varmatm=as.data.frame(varmat[,-11])
labn=colnames(varmatm)
longd=NULL
for (j in 1:dim(varmatm)[2]) {
longd=rbind(longd,cbind(varmatm[,j],rep(labn[j],dim(varmatm)[1])))
}
longdf=as.data.frame(longd)
colnames(longdf)=c("dat","reg")
longdfp=longdf[as.numeric(as.character(longdf[,1]))>0,]
# violin plot for cv variability
cvec=c(rgb(0.75,0.75,0.75),ctcolors)
names(cvec)[1]="All"
lcvec=NULL
for (i in 1:12) {
lcvec=c(lcvec,rep(cvec[i],29))
}
longdf2=cbind(longdf,lcvec)
par(fig = c(0,1,0,1))
par(las=1)
vioplot(as.numeric(as.character(longdfp[longdfp$reg=="All",1])),as.numeric(as.character(longdfp[longdfp$reg=="IT",1])),
as.numeric(as.character(longdfp[longdfp$reg=="PT",1])),as.numeric(as.character(longdfp[longdfp$reg=="NP",1])),
as.numeric(as.character(longdfp[longdfp$reg=="CT",1])),as.numeric(as.character(longdfp[longdfp$reg=="L6b",1])),
as.numeric(as.character(longdfp[longdfp$reg=="Lamp5",1])),as.numeric(as.character(longdfp[longdfp$reg=="Sncg",1])),
as.numeric(as.character(longdfp[longdfp$reg=="Serpinf1",1])),as.numeric(as.character(longdfp[longdfp$reg=="VIP",1])),
as.numeric(as.character(longdfp[longdfp$reg=="Sst",1])),as.numeric(as.character(longdfp[longdfp$reg=="Pvalb",1])),col=cvec[-11],
names=names(cvec)[-11],cex.axis=1.75,main="NP-GPCR expression variability",cex.main=1.5,axes=F,ylim=c(0,2),
font.axis=1)
points(2,1.7113,cex=1.25,pch=16)
points(3,1.027,cex=1.55,pch=16)
points(4,0.821417,cex=1.55,pch=16)
points(5,0.7069,cex=1.25,pch=16)
points(6,0.95626,cex=1.25,pch=16)
points(7,1.4198,cex=1.25,pch=16)
points(7,1.1718,cex=1.25,pch=16)
points(8,0.9220,cex=1.25,pch=16)
points(9,1.2237,cex=1.25,pch=16)
points(10,1.127,cex=1.25,pch=16)
points(11,1.38148,cex=1.25,pch=16)
points(11,1.228,cex=1.25,pch=16)
points(12,1.446,cex=1.25,pch=16)
points(12,1.338,cex=1.25,pch=16)
mbarv=vector("numeric",11)
mall=mean(varmatm[,1])
for (j in 2:dim(varmatm)[2]) {
mbarv[j-1]=mean(varmatm[varmatm[,j]>0,j])/mall
}
par(fig = c(0.75,0.95,0.65,1.0),new=TRUE,las=2)
barplot(mbarv,names.arg=colnames(varmatm)[-1],col=ctcolors[-10],cex.names=0.75,cex.axis=0.75,ylab="Relative CV")
abline(h=mean(mbarv),lty=2)
# Supplementary data for Figure 3 variability plots
varmatn=varmat[,-c(1,11)]
sumvec=apply(varmatn,2,mean)
ov=order(sumvec,decreasing=T)
pmat=varmatn[,ov]
covo=names(sumvec[ov])
pmatn=pmat
mypal=colorRampPalette(c(rgb(1,1,1),rgb(0.25,0.25,0.25)),bias=3)(10)
mypal=rev(colorRampPalette(brewer.pal(8,"Spectral"),bias=1)(8))
c1=brewer.pal(8,"Spectral")[1]
c8=brewer.pal(8,"Spectral")[8]
mypal=rev(colorRampPalette(c(rgb(0.75,0.4,1,0.75), rgb(0.75,0.4,1,0)), alpha = TRUE)(8))
imname="/volumes/PHILIPS UFD/Analysis/Neuropeptides/cell_type_variability_np-gpcr.tiff"
imname="F:\\Analysis\\Neuropeptides\\Submission\\cell_type_variability_np-gpcr.tiff"
tiff(imname,width=1500,height=1500)
par(cex.axis=1.5)
superheat(X=pmatn,left.label.col=c("ivory","ivory2"),bottom.label.col=ctcolors[covo],legend.text.size=14,
left.label.text.alignment="center",X.text=round(pmatn,2),X.text.size=8,heat.pal=c("white","tan1"),yt=sumvec[covo],
yt.axis.name="Mean cell type CV / Global CV",yt.plot.type="bar",yt.axis.size=18,yt.axis.name.size=16,yt.obs.col=ctcolors[covo],
left.label.text.size=8,bottom.label.text.size=9)
dev.off()
for (i in 1:dim(vmat)[1]) {
print(paste(rownames(nppmat)[i],round(max(vmat[i,]),3)))
vmat[i,]=vmat[i,]/max(vmat[i,])
}
plot_All(vmat,"NP-GPCR expression in ALM and VISp",1)
plot_All_pooled(vmat,"NP-GPCR expression in ALM and VISp",1)
colnames(vmat)=paste("CT",alldend,sep="")
write.csv(vmat,"F:\\Analysis\\Neuropeptides\\FigureData\\Figure3B.csv")
# try plotting one large
combmat=rbind(varmat_npp,varmat_npgpcr)
combn=combmat[,-c(1,11)]
pheatmap(combn)
##################################
# 5A. NPP: VISp Expression
##################################
d1=subset(np_gpcr_cpm,brain_region_label=="VISp")
alltyp=133
clabel=paste("CT",c(1:alltyp),sep="")
nppmat=matrix(0,length(nppm),alltyp,dimnames=list(nppm,clabel))
for (i in 1:length(nppm)) {
ip1=which(names(d1)==nppm[i])
for (j in 1:alltyp) {
ip2=which(d1[,6]==j)
nppmat[i,j]=mean(d1[ip2,ip1],trim=0.05)
}
}
vmat=nppmat[,visclid]
vmat_npp_visp=vmat
# log before normalization
vmat=log10(vmat+1)
for (i in 1:dim(vmat)[1]) {
print(paste(rownames(nppmat)[i],round(max(vmat[i,]),3)))
vmat[i,]=vmat[i,]/max(vmat[i,])
}
plot_VISp(vmat,"NPP expression in VISp",1)
colnames(vmat)=paste("CT",visdend,sep="")
write.csv(vmat,"F:\\Analysis\\Neuropeptides\\FigureData\\Figure5A.csv")
######################################
# 5B. NP-GPCR: VISp Expression
######################################
d1=subset(np_gpcr_cpm,brain_region_label=="VISp")
alltyp=133
clabel=paste("CT",c(1:alltyp),sep="")
nppmat=matrix(0,length(npgpcrm),alltyp,dimnames=list(npgpcrm,clabel))
for (i in 1:length(npgpcrm)) {
ip1=which(names(d1)==npgpcrm[i])
for (j in 1:alltyp) {
ip2=which(d1[,6]==j)
nppmat[i,j]=mean(d1[ip2,ip1],trim=0.05)
}
}
vmat=nppmat[,visclid]
vmat_npgpcr_visp=vmat
vmat_npgpcr_visp=vmat
# log before normalization
vmat=log10(vmat+1)
for (i in 1:dim(vmat)[1]) {
print(paste(rownames(nppmat)[i],round(max(vmat[i,]),3)))
vmat[i,]=vmat[i,]/max(vmat[i,])
}
plot_VISp(vmat,"NP-GPCR expression in VISp",1)
colnames(vmat)=paste("CT",visdend,sep="")
write.csv(vmat,"F:\\Analysis\\Neuropeptides\\FigureData\\Figure5A.csv")
##################################
# 5C. NPP: ALM Expression
##################################
d1=subset(np_gpcr_cpm,brain_region_label=="ALM")
alltyp=133
clabel=paste("CT",c(1:alltyp),sep="")
nppmat=matrix(0,length(nppm),alltyp,dimnames=list(nppm,clabel))
for (i in 1:length(nppm)) {
ip1=which(names(d1)==nppm[i])
for (j in 1:alltyp) {
ip2=which(d1[,6]==j)
nppmat[i,j]=mean(d1[ip2,ip1],trim=0.05)
}
}
vmat=nppmat[,almclid]
vmat_npp_alm=vmat
# log before normalization
vmat=log10(vmat+1)
for (i in 1:dim(vmat)[1]) {
print(paste(rownames(nppmat)[i],round(max(vmat[i,]),3)))
vmat[i,]=vmat[i,]/max(vmat[i,])
}
plot_ALM(vmat,"NPP expression in ALM",1)
colnames(vmat)=paste("CT",almdend,sep="")
write.csv(vmat,"F:\\Analysis\\Neuropeptides\\FigureData\\Figure5C.csv")
######################################
# 5D. NP-GPCR: ALM Expression
######################################
alltyp=133
clabel=paste("CT",c(1:alltyp),sep="")
nppmat=matrix(0,length(npgpcrm),alltyp,dimnames=list(npgpcrm,clabel))
for (i in 1:length(npgpcrm)) {
ip1=which(names(d1)==npgpcrm[i])
for (j in 1:alltyp) {
ip2=which(d1[,6]==j)
nppmat[i,j]=mean(d1[ip2,ip1],trim=0.05)
}
}
vmat=nppmat[,almclid]
vmat_npgpcr_alm=vmat
vmat_npgpcr_alm=vmat
# log before normalization
vmat=log10(vmat+1)
for (i in 1:dim(vmat)[1]) {
print(paste(rownames(nppmat)[i],round(max(vmat[i,]),3)))
vmat[i,]=vmat[i,]/max(vmat[i,])
}
plot_ALM(vmat,"NP-GPCR expression in ALM",1)
colnames(vmat)=paste("CT",almdend,sep="")
write.csv(vmat,"F:\\Analysis\\Neuropeptides\\FigureData\\Figure5D.csv")
# Calculate intersection of cell types correlation - NPP
commont=intersect(colnames(vmat_npp_alm),colnames(vmat_npp_visp))
amat=as.vector(vmat_npp_alm[,commont])
bmat=as.vector(vmat_npp_visp[,commont])
plot(amat,bmat,pch=16,cex=0.5)
cor(amat,bmat)
# Calculate intersection of cell types correlation - NP-GPCR
commont=intersect(colnames(vmat_npgpcr_alm),colnames(vmat_npgpcr_visp))
amat=as.vector(vmat_npgpcr_alm[,commont])
bmat=as.vector(vmat_npgpcr_visp[,commont])
par(fig = c(0,1,0,1))
plot(log(amat+1),log(bmat+1),pch=16,cex=0.5,axes=F,xlab="ALM",ylab="VISp",main="Expression in common ALM and VISp cell types")
axis(1)
axis(2)
cor.test(amat,bmat)
amod=lm(log(bmat+1)~log(amat+1))
abline(amod$coefficients,col=2,lw=2)
rsq=summary(amod)$adj.r.squared
pval="p<2e-16"
text(2,5.75,paste("Adjusted R-squared = ",round(rsq,3)))
text(1.5,5.5,"p<2e-16")
#############################################################################################################
# # Figure 6. Expression profiles of conjugate NPP/NP-GPCR pairs predict neuropeptidergic signaling networks
#############################################################################################################
maxvec_alm=vector("numeric",dim(inpairs)[1])
maxvec_visp=vector("numeric",dim(inpairs)[1])
# assumes vat_npp_visp etc. matrices above
regionvec=c("VISp","ALM")
for (region in regionvec) {
for (ival in 1:dim(inpairs)[1]) {
print (paste("Printing",region,ival))
jval = as.character(inpairs[ival,2])
kval = as.character(inpairs[ival,3])
if (region == "VISp") {
outerprod=outer(vmat_npp_visp[jval,],vmat_npgpcr_visp[kval,])
outerprod=log10(outerprod+1)
maxvec_visp[ival]=max(outerprod)
}
if (region == "ALM") {
outerprod=outer(vmat_npp_alm[jval,],vmat_npgpcr_alm[kval,])
outerprod=log10(outerprod+1)
maxvec_alm[ival]=max(outerprod)
}
# make title
normv=round(max(outerprod),2)
icode=paste("(",region,", ",as.character(inpairs[ival,4]),", ",normv,")",sep="")
inum=paste(ival,":",sep="")
ititle=paste(inum,jval,"->",kval, " ", icode,sep=" ")
fdir="F:\\Analysis\\Neuropeptides\\Submission\\Interaction Plots\\"
# fdir="/volumes/PHILIPS UFD/Analysis/Neuropeptides/Interaction Plots/"
fname=paste(fdir,region,"_",ival,"_",as.character(inpairs[ival,1]),".tiff",sep="")
tiff(fname,width=1000,height=1000)
if (region == "VISp") {plot_Interaction_VISp(outerprod,ititle,gamma=1)}
if (region == "ALM") {plot_Interaction_ALM(outerprod,ititle,gamma=1)}
dev.off()
f2dir="F:\\Analysis\\Neuropeptides\\Submission\\Interaction Matrices\\"
#f2dir="/volumes/PHILIPS UFD/Analysis/Neuropeptides/Interaction Matrices/"
f2name=paste(f2dir,ival,"_",region,"_",inpairs[ival,1],".csv",sep="")
write.table(outerprod,file=f2name,col.names=F,row.names=F,quote=F,sep=",")
}
}
# write out maximum scale normalization
df=data.frame(cbind(as.character(inpairs[,1]),round(maxvec_alm,3),round(maxvec_visp,3)))
write.csv(df,"F:\\Analysis\\Neuropeptides\\Submission\\CognatePair_interaction_normalization.csv")
#write.csv(df,"/volumes/PHILIPS UFD/Analysis/Neuropeptides/Normalization.csv")
############################################
# Figure 6e: Pooled cell type representation
############################################
indir = "F:\\Analysis\\Neuropeptides\\Submission\\Interaction Matrices\\"
infiles=list.files(indir)
poolmat=matrix(0,12,12,dimnames=list(ctpool,ctpool))
plist_ALM=NULL
plist_VISp=NULL
ialm=0
ivisp=0
for (i in 1:length(infiles)) {
print (paste("Printing Pooled",region,i))
idf=read.csv(paste(indir,infiles[i],sep=""),header=F)
imat=as.matrix(idf)
fn1=unlist(strsplit(infiles[i],"[.]"))[1]
ft=unlist(strsplit(fn1,"_"))
fnum=as.integer(ft[1])
region=as.character(ft[2])
ipair=unlist(strsplit(as.character(ft[3]),"-")) # construct interaction pair and read off type
iloc=which(as.character(inpairs[,1])==as.character(ft[3]))
itype=as.character(inpairs[iloc,4])
for (j in 1:length(ctpool)) {
for (k in 1:length(ctpool)) {
if (region == "ALM") {suml=almlist}
if (region == "VISp") {suml=vislist}
poolmat[j,k] = mean(imat[suml[[j]],suml[[k]]])
}
}
if (region == "ALM") {
ialm=ialm+1
plist_ALM[[ialm]]=poolmat
}
if (region == "VISp") {
ivisp=ivisp+1
plist_VISp[[ivisp]]=poolmat
}
maxv=round(max(poolmat),2) # normalization factor
# Generate annotations for rows and columns
annotation_col = data.frame(
CellType = factor(ctpool)
)
rownames(annotation_col)= ctpool
ann_colors = list(CellType = ctcolors)
mpal = colorRampPalette(brewer.pal(8,"PuBuGn"),bias=bval)(6)
# construct title
titp = paste(fnum,": ",ipair[1],"->",ipair[2], " (",region,", ",itype,", ",maxv, ")",sep="")
fdir ="F:\\Analysis\\Neuropeptides\\Submission\\Interaction Plots Pooled\\"
fname=paste(fdir,region,"_",fnum,"_",ipair[1],"-",ipair[2],"_Pooled.tiff",sep="")
tiff(fname,width=800,height=800)
pheatmap(poolmat,cluster_cols=FALSE,cluster_rows=FALSE,fontsize_row=20,fontsize_col=20,fontsize=20,border_color=9,color=rev(mpal),
main=titp,annotation_col=annotation_col,annotation_row=annotation_col,annotation_colors = ann_colors,annotation_legend=F,
show_rownames=TRUE,show_colnames=TRUE,annotation_names_row=F,annotation_names_col=F,legend=F)
dev.off()
}
##############################################################
# Figure 6 (Supplmental) Clustering of interactions matrices
##############################################################
# make distance matrix between interaction matrices
fpath="/volumes/PHILIPS UFD/Analysis/Neuropeptides/Interaction Matrices/"
fpath="F:\\Analysis\\Neuropeptides\\Interaction Matrices\\"
files=list.files(fpath)
matlistALM=NULL
matlistVISp=NULL
macount=0
mvcount=0
nameALM=vector("character",37)
nameVISp=vector("character",37)
corALM=matrix(0,37,37,dimnames=list(inpairs[,1],inpairs[,1]))
corVISp=matrix(0,37,37,dimnames=list(inpairs[,1],inpairs[,1]))
for (i in 1:length(files)) {
ireg=unlist(strsplit(unlist(strsplit(files[i],"-"))[1],"_"))[2]
inpp=unlist(strsplit(unlist(strsplit(files[i],"-"))[1],"_"))[3]
igpcr=unlist(strsplit(unlist(strsplit(files[i],"-"))[2],"[.]"))[1]
ipos=which ((as.character(inpairs[,2])==inpp) & (as.character(inpairs[,3])==igpcr))
icode=as.character(inpairs[ipos,4])
ival=paste(inpp,igpcr,sep="-")
if (ireg=="ALM") {
macount=macount+1
matlistALM[[macount]]=as.matrix(read.csv(paste(fpath,files[i],sep=""),header=F))
nameALM[macount]=ival
}
if (ireg=="VISp") {
mvcount=mvcount+1
matlistVISp[[mvcount]]=as.matrix(read.csv(paste(fpath,files[i],sep=""),header=F))
nameVISp[mvcount]=ival
}
}
for (i in 1:macount) {
for (j in i:macount) {
corALM[nameALM[i],nameALM[j]]=cor(as.vector(matlistALM[[i]]),as.vector(matlistALM[[j]]))
}
}
for (i in 1:macount) {
for (j in i:macount) {
corALM[j,i]=corALM[i,j]
corALM[i,i]=0.0
}
}