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%\usepackage{amssymb}

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\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{Janvier 2021}							% Activate to display a given date or no date

\begin{document}
\maketitle

\tableofcontents

\newpage

\section{Data}

Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.


<<eval=FALSE>>=
workdir<-"/home/adminmarie/Documents/CIRI_BIBS_projects/2020_05_Etienne_covid/"

tab<-read.delim(paste0(workdir, 
  "covid_comp/covid_comp_complete.txt"), h=T, sep="\t")
dim(tab)
@



<<>>=
workdir<-"/home/adminmarie/Documents/CIRI_BIBS_projects/2020_05_Etienne_covid/"

tab<-read.delim(paste0(workdir, 
  "covid_comp/covid_comp_alldginn.txt"), h=T, sep="\t")
dim(tab)
@

\section{Comparison of dataset}

\subsection{Data}

<<data>>=
tmp<-na.omit(tab[,c("Gene.name", "bats_BUSTED", "bats_BppM1M2", "bats_BppM7M8", 
    "bats_codemlM1M2", "bats_codemlM7M8", "dginn.primate_codemlM1M2", 
    "dginn.primate_codemlM7M8", "dginn.primate_BppM1M2", 
    "dginn.primate_BppM7M8", "dginn.primate_BUSTED")])
col<-c("Gene.name", "bats_BUSTED", "bats_BppM1M2", "bats_BppM7M8", 
    "bats_codemlM1M2", "bats_codemlM7M8", "dginn.primate_codemlM1M2", 
    "dginn.primate_codemlM7M8", "dginn.primate_BppM1M2", 
    "dginn.primate_BppM7M8", "dginn.primate_BUSTED")
dim(tmp)
@

\subsection{Omega plot}

<<>>=
x=as.numeric(as.character(tab$dginn.primate_omegaM0Bpp[tab$status=="shared"]))
y=as.numeric(as.character(tab$bats_omegaM0Bpp[tab$status=="shared"]))
names(x)<-tab$Gene.name[tab$status=="shared"]
  
plot(x,y, xlab="bpp omega primate", ylab="bpp omega bats", cex=0.5)
abline(0,1)
abline(lm(y~x), col="red")

text(x[x>0.5 &y<0.4], (y[x>0.5 &y<0.4]+0.01), names(x)[x>0.5 &y<0.4], cex=0.7)
text(x[x<0.45 &y>0.45], (y[x<0.45 &y>0.45]+0.01), names(x)[x<0.45 &y>0.45], cex=0.7)
text(x[x>0.45 &y>0.4], (y[x>0.45 &y>0.4]+0.01), names(x)[x>0.45 &y>0.4], cex=0.7)

@

\subsection{Mondrian}

<<mondrianbats>>=
library(Mondrian)

monddata<-as.data.frame(tmp$Gene.name)

batstmp<-rowSums(cbind(tmp$bats_codemlM1M2=="Y", tmp$bats_codemlM7M8=="Y", 
tmp$bats_BppM1M2=="Y", tmp$bats_BppM7M8=="Y", tmp$bats_BUSTED=="Y")) 

primatetmp<-rowSums(cbind(tmp$"dginn.primate_codemlM1M2"=="Y", 
  tmp$"dginn.primate_codemlM7M8"=="Y", tmp$"dginn.primate_BppM1M2"=="Y", 
  tmp$"dginn.primate_BppM7M8"=="Y", tmp$"dginn.primate_BUSTED"=="Y")) 
                                       
monddata$bats_dginn3<-ifelse(batstmp>=3, 1,0)
monddata$primate_dginn3<-ifelse(primatetmp>=3, 1,0)
monddata$bats_dginn4<-ifelse(batstmp>=4, 1,0)
monddata$primate_dginn4<-ifelse(primatetmp>=4, 1,0)

mondrian(monddata[,2:3], labels=c("DGINN bats >3", "DGINN primate >3"))

mondrian(monddata[,4:5], labels=c("DGINN bats >4", "DGINN primate >4"))

@

\subsection{subsetR}

<<subsetbats>>=
library(UpSetR)

upset(monddata, nsets = 4, matrix.color = "#DC267F", 
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")

upset(monddata[,1:3], nsets = 2, matrix.color = "#DC267F", 
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")

upset(monddata[,c(1,4,5)], nsets = 2, matrix.color = "#DC267F", 
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
@

\section{Which are these genes?}

\subsection{Gene under positive selection in both bats and primates}

4 methods:

<<>>=
monddata[monddata$bats_dginn4==1 & monddata$primate_dginn4==1,]
@

3 methods:

<<>>=
monddata[monddata$bats_dginn3==1 & monddata$primate_dginn3==1,]
@


\subsection{Gene under positive selection only in primates}

4 methods:

<<>>=
monddata[monddata$bats_dginn4==0 & monddata$primate_dginn4==1,]
@

3 methods:

<<t>>=
monddata[monddata$bats_dginn3==0 & monddata$primate_dginn3==1,]
@


\subsection{Gene under positive selection only in bats}

4 methods:

<<>>=
monddata[monddata$bats_dginn4==1 & monddata$primate_dginn4==0,]
@

3 methods:

<<>>=
monddata[monddata$bats_dginn3==1 & monddata$primate_dginn3==0,]
@

\subsection{Figure tableau}

<<tablo>>=
tablo<-as.data.frame(tmp$Gene.name)
tablo$nbats<-batstmp
tablo$nprimates<-primatetmp

plot(NULL, xlim=c(-0.5,5.5), ylim=c(-3,5.5), xlab="bats", ylab="primates", main="Genes supported by x,y methods in bats and primates", bty="n", xaxt="n", yaxt="n")

text(x=rep(-0.6, 6), y=0:5, 0:5)
text(y=rep(-0.65, 6), x=0:5, 0:5)
sapply(seq(from=-0.5, to=5.5, by=1), function(x){
  segments(x0=x, x1=x, y0=-0.5, y1=5.5)
})

sapply(seq(from=-0.5, to=5.5, by=1), function(x){
  segments(x0=-0.5, x1=5.5, y0=x, y1=x)
})

for (p in 0:5){
  for (b in 0:5){
    tmp<-tablo$`tmp$Gene.name`[tablo$nbats==b & tablo$nprimates==p]
    if(length(tmp)>0 & length(tmp)<=8){
      text(b,seq(from=(p-0.4), to=(p+0.4), length.out = length(tmp)), tmp, cex=0.4)
    }else if (length(tmp)>8 & length(tmp)<=16){
      print(c(p, b))
      text((b-0.3),seq(from=(p-0.4), to=(p+0.4), length.out = 8), tmp[1:8], cex=0.4)      
      text((b+0.3),seq(from=(p-0.4), to=(p+0.4), length.out = (length(tmp)-8)), tmp[9:length(tmp)], cex=0.4)
    }else if (length(tmp)>16){
      text(b,p, paste0(length(tmp), " values"))
      }  
  }
}


tmp<-tablo$`tmp$Gene.name`[tablo$nbats==0 & tablo$nprimates==1]
text(-0.4,-1.2, "p=1/n=0", cex=0.6)
text(seq(from=0.1, to=5.5, length.out = 18),-1.1, tmp[1:18], cex=0.4)
text(seq(from=0.1, to=5.5, length.out = length(tmp)-18),-1.3, tmp[19:length(tmp)], cex=0.4)

tmp<-tablo$`tmp$Gene.name`[tablo$nbats==1 & tablo$nprimates==1]
text(-0.4,-1.7, "p=1/n=1", cex=0.6)
text(seq(from=0.1, to=5.5, length.out = 18),-1.6, tmp[1:18], cex=0.4)
text(seq(from=0.1, to=4.5, length.out = length(tmp)-18),-1.8, tmp[19:length(tmp)], cex=0.4)


tmp<-tablo$`tmp$Gene.name`[tablo$nbats==0 & tablo$nprimates==0]
text(-0.4,-2.3, "p=0/n=0", cex=0.6)
text(seq(from=0.1, to=5.5, length.out = 17),-2.1, tmp[1:17], cex=0.4)
text(seq(from=0.1, to=5.5, length.out = 17),-2.3, tmp[18:34], cex=0.4)
text(seq(from=0.1, to=5.5, length.out = length(tmp)-34),-2.5, tmp[35:length(tmp)], cex=0.4)


tmp<-tablo$`tmp$Gene.name`[tablo$nbats==2 & tablo$nprimates==0]
text(-0.4,-2.9, "p=0/n=2", cex=0.6)
text(seq(from=0.1, to=5.5, length.out = 18),-2.8, tmp[1:18], cex=0.4)
text(seq(from=0.1, to=1, length.out = length(tmp)-18),-3.0, tmp[19:length(tmp)], cex=0.4)

@

<<>>=
write.csv(tablo[tablo$nbats>=3,"tmp$Gene.name"], "batssup3.csv", row.names=FALSE, quote=FALSE)

write.csv(tablo[tablo$nprimates>=3,"tmp$Gene.name"], "primatessup3.csv", row.names=FALSE, quote=FALSE)
write.csv(tablo, "primatesVbats.csv", row.names=FALSE, quote=FALSE)
@

Restreindre ce tableau aux gènes présent dans l'analyse de Krogan.
<<setup, include=FALSE, cache=FALSE, tidy=TRUE>>=
options(tidy=TRUE, width=70)
@
<<>>=
# Reading the Krogan table
tab<-read.delim(paste0(workdir, 
  "data/COVID_PAMLresults_332hits_plusBatScreens_2020_Apr14.csv"),
	fill=T, h=T, dec=",")
dim(tab)

#Formating the column Gene.name and changing one wierd name
tab$Gene.name<-as.character(tab$Gene.name)
tab$Gene.name[tab$PreyGene=="MTARC1"]<-"MTARC1"

#Adding ACE2 and TMPRSS2
krogan<-c(tab$Gene.name,  "ACE2", "TMPRSS2")

# The list
length(krogan)
krogan

#In the table, I select line that match the krogan gene name liste
tabloK<-tablo[tablo$`tmp$Gene.name` %in% krogan,]

# How many gene lost?
dim(tablo)
dim(tabloK)

# Les gènes perdus (dans le tableau mais pas dans la liste de Krogan)
sort(tablo$`tmp$Gene.name`[tablo$`tmp$Gene.name` %in% krogan==F])

# Les gènes de Krogan non présent dans cette liste
sort(krogan[krogan %in% tablo$`tmp$Gene.name`==F])


write.csv(tabloK, "primatesVbats_onlykrogan.csv", row.names=FALSE, quote=FALSE)
@


\end{document}