Commit e04625e1 authored by mcariou's avatar mcariou
Browse files

add shiny

parent 6455da81
......@@ -34,6 +34,8 @@ 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)
tab$Gene.name<-as.character(tab$Gene.name)
tab$Gene.name[tab$PreyGene=="MTARC1"]<-"MTARC1"
@
\section{Comparison Bats}
......@@ -45,6 +47,7 @@ dim(tab)
plot(tab$cooper.batsAverage_dNdS, as.numeric(as.character(tab$bats_omegaM0codeml)),
xlab="Omega Cooper-bats", ylab="Omega DGINN-bats")
abline(0,1)
abline(lm(as.numeric(as.character(tab$bats_omegaM0codeml))~tab$cooper.batsAverage_dNdS), col="red")
@
\subsection{Cooper-bats VS Hawkins-bats and DGINN-bats VS Hawkins-bats}
......@@ -56,13 +59,13 @@ abline(0,1)
\subsection{Data}
<<subbats>>=
tmp<-na.omit(tab[,c("Gene.name", "bats_codemlM7M8.p.value", "hawkins_Positive.Selection..M8vM8a.p.value", "cooper.batsM7.M8_p_value", "bats_BUSTED", "bats_BppM1M2", "bats_BppM7M8", "bats_codemlM1M2", "bats_codemlM7M8")])
tmp<-na.omit(tab[,c("Gene.name", "bats_codemlM7M8_p.value", "hawkins_Positive.Selection..M8vM8a.p.value", "cooper.batsM7.M8_p_value", "bats_BUSTED", "bats_BppM1M2", "bats_BppM7M8", "bats_codemlM1M2", "bats_codemlM7M8")])
tmp$bats_codemlM7M8.p.value<-as.numeric(as.character(tmp$bats_codemlM7M8.p.value))
tmp$bats_codemlM7M8_p.value<-as.numeric(as.character(tmp$bats_codemlM7M8_p.value))
dim(tmp)
@
174 genes (present in the 3 experiments)
170 genes (present in the 3 experiments)
\subsection{Mondrian}
......@@ -105,9 +108,37 @@ main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
@
<<>>=
source("covid_comp_shiny.R")
df<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
names(df)
dftmp<-tab[,c("bats_File", "bats_Name", "Gene.name",
"bats_GeneSize", "bats_NbSpecies", "bats_omegaM0Bpp",
"bats_omegaM0codeml", "bats_BUSTED", "bats_BUSTED_p.value",
"bats_MEME_NbSites", "bats_MEME_PSS", "bats_BppM1M2",
"bats_BppM1M2_p.value", "bats_BppM1M2_NbSites", "bats_BppM1M2_PSS",
"bats_BppM7M8", "bats_BppM7M8_p.value", "bats_BppM7M8_NbSites",
"bats_BppM7M8_PSS", "bats_codemlM1M2", "bats_codemlM1M2_p.value",
"bats_codemlM1M2_NbSites","bats_codemlM1M2_PSS", "bats_codemlM7M8",
"bats_codemlM7M8_p.value", "bats_codemlM7M8_NbSites" , "bats_codemlM7M8_PSS")]
names(dftmp)<-names(df)
makeFig1(dftmp)
@
\end{document}
No preview for this file type
......@@ -88,8 +88,12 @@ Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
\hlkwd{dim}\hlstd{(tab)}
\end{alltt}
\begin{verbatim}
## [1] 333 161
## [1] 332 139
\end{verbatim}
\begin{alltt}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name}\hlkwb{<-}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstd{Gene.name)}
\hlstd{tab}\hlopt{$}\hlstd{Gene.name[tab}\hlopt{$}\hlstd{PreyGene}\hlopt{==}\hlstr{"MTARC1"}\hlstd{]}\hlkwb{<-}\hlstr{"MTARC1"}
\end{alltt}
\end{kframe}
\end{knitrout}
......@@ -107,8 +111,11 @@ Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
{\ttfamily\noindent\color{warningcolor}{\#\# Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduits lors de la conversion automatique}}\begin{alltt}
\hlkwd{abline}\hlstd{(}\hlnum{0}\hlstd{,}\hlnum{1}\hlstd{)}
\hlkwd{abline}\hlstd{(}\hlkwd{lm}\hlstd{(}\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(tab}\hlopt{$}\hlstd{bats_omegaM0codeml))}\hlopt{~}\hlstd{tab}\hlopt{$}\hlstd{cooper.batsAverage_dNdS),} \hlkwc{col}\hlstd{=}\hlstr{"red"}\hlstd{)}
\end{alltt}
\end{kframe}
{\ttfamily\noindent\color{warningcolor}{\#\# Warning in eval(predvars, data, env): NAs introduits lors de la conversion automatique}}\end{kframe}
\includegraphics[width=\maxwidth]{figure/omegaM7M8bats-1}
\end{knitrout}
......@@ -124,9 +131,9 @@ Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlstd{tmp}\hlkwb{<-}\hlkwd{na.omit}\hlstd{(tab[,}\hlkwd{c}\hlstd{(}\hlstr{"Gene.name"}\hlstd{,} \hlstr{"bats_codemlM7M8.p.value"}\hlstd{,} \hlstr{"hawkins_Positive.Selection..M8vM8a.p.value"}\hlstd{,} \hlstr{"cooper.batsM7.M8_p_value"}\hlstd{,} \hlstr{"bats_BUSTED"}\hlstd{,} \hlstr{"bats_BppM1M2"}\hlstd{,} \hlstr{"bats_BppM7M8"}\hlstd{,} \hlstr{"bats_codemlM1M2"}\hlstd{,} \hlstr{"bats_codemlM7M8"}\hlstd{)])}
\hlstd{tmp}\hlkwb{<-}\hlkwd{na.omit}\hlstd{(tab[,}\hlkwd{c}\hlstd{(}\hlstr{"Gene.name"}\hlstd{,} \hlstr{"bats_codemlM7M8_p.value"}\hlstd{,} \hlstr{"hawkins_Positive.Selection..M8vM8a.p.value"}\hlstd{,} \hlstr{"cooper.batsM7.M8_p_value"}\hlstd{,} \hlstr{"bats_BUSTED"}\hlstd{,} \hlstr{"bats_BppM1M2"}\hlstd{,} \hlstr{"bats_BppM7M8"}\hlstd{,} \hlstr{"bats_codemlM1M2"}\hlstd{,} \hlstr{"bats_codemlM7M8"}\hlstd{)])}
\hlstd{tmp}\hlopt{$}\hlstd{bats_codemlM7M8.p.value}\hlkwb{<-}\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(tmp}\hlopt{$}\hlstd{bats_codemlM7M8.p.value))}
\hlstd{tmp}\hlopt{$}\hlstd{bats_codemlM7M8_p.value}\hlkwb{<-}\hlkwd{as.numeric}\hlstd{(}\hlkwd{as.character}\hlstd{(tmp}\hlopt{$}\hlstd{bats_codemlM7M8_p.value))}
\end{alltt}
......@@ -134,12 +141,12 @@ Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
\hlkwd{dim}\hlstd{(tmp)}
\end{alltt}
\begin{verbatim}
## [1] 174 9
## [1] 170 9
\end{verbatim}
\end{kframe}
\end{knitrout}
174 genes (present in the 3 experiments)
170 genes (present in the 3 experiments)
\subsection{Mondrian}
......@@ -199,9 +206,55 @@ Analysis were formatted by the script covid\_comp\_script0\_table.Rnw.
\end{knitrout}
\begin{knitrout}
\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
\begin{alltt}
\hlkwd{source}\hlstd{(}\hlstr{"covid_comp_shiny.R"}\hlstd{)}
\hlstd{df}\hlkwb{<-}\hlkwd{read.delim}\hlstd{(}\hlkwd{paste0}\hlstd{(workdir,}
\hlstr{"/data/DGINN_202005281649summary_cleaned.csv"}\hlstd{),}
\hlkwc{fill}\hlstd{=T,} \hlkwc{h}\hlstd{=T,} \hlkwc{sep}\hlstd{=}\hlstr{","}\hlstd{)}
\hlkwd{names}\hlstd{(df)}
\end{alltt}
\begin{verbatim}
## [1] "File" "Name" "Gene"
## [4] "GeneSize" "NbSpecies" "omegaM0Bpp"
## [7] "omegaM0codeml" "BUSTED" "BUSTED.p.value"
## [10] "MEME.NbSites" "MEME.PSS" "BppM1M2"
## [13] "BppM1M2.p.value" "BppM1M2.NbSites" "BppM1M2.PSS"
## [16] "BppM7M8" "BppM7M8.p.value" "BppM7M8.NbSites"
## [19] "BppM7M8.PSS" "codemlM1M2" "codemlM1M2.p.value"
## [22] "codemlM1M2.NbSites" "codemlM1M2.PSS" "codemlM7M8"
## [25] "codemlM7M8.p.value" "codemlM7M8.NbSites" "codemlM7M8.PSS"
\end{verbatim}
\begin{alltt}
\hlstd{dftmp}\hlkwb{<-}\hlstd{tab[,}\hlkwd{c}\hlstd{(}\hlstr{"bats_File"}\hlstd{,} \hlstr{"bats_Name"}\hlstd{,} \hlstr{"Gene.name"}\hlstd{,}
\hlstr{"bats_GeneSize"}\hlstd{,} \hlstr{"bats_NbSpecies"}\hlstd{,} \hlstr{"bats_omegaM0Bpp"}\hlstd{,}
\hlstr{"bats_omegaM0codeml"}\hlstd{,} \hlstr{"bats_BUSTED"}\hlstd{,} \hlstr{"bats_BUSTED_p.value"}\hlstd{,}
\hlstr{"bats_MEME_NbSites"}\hlstd{,} \hlstr{"bats_MEME_PSS"}\hlstd{,} \hlstr{"bats_BppM1M2"}\hlstd{,}
\hlstr{"bats_BppM1M2_p.value"}\hlstd{,} \hlstr{"bats_BppM1M2_NbSites"}\hlstd{,} \hlstr{"bats_BppM1M2_PSS"}\hlstd{,}
\hlstr{"bats_BppM7M8"}\hlstd{,} \hlstr{"bats_BppM7M8_p.value"}\hlstd{,} \hlstr{"bats_BppM7M8_NbSites"}\hlstd{,}
\hlstr{"bats_BppM7M8_PSS"}\hlstd{,} \hlstr{"bats_codemlM1M2"}\hlstd{,} \hlstr{"bats_codemlM1M2_p.value"}\hlstd{,}
\hlstr{"bats_codemlM1M2_NbSites"}\hlstd{,}\hlstr{"bats_codemlM1M2_PSS"}\hlstd{,} \hlstr{"bats_codemlM7M8"}\hlstd{,}
\hlstr{"bats_codemlM7M8_p.value"}\hlstd{,} \hlstr{"bats_codemlM7M8_NbSites"} \hlstd{,} \hlstr{"bats_codemlM7M8_PSS"}\hlstd{)]}
\hlkwd{names}\hlstd{(dftmp)}\hlkwb{<-}\hlkwd{names}\hlstd{(df)}
\hlkwd{makeFig1}\hlstd{(dftmp)}
\end{alltt}
\end{kframe}
\includegraphics[width=\maxwidth]{figure/unnamed-chunk-2-1}
\end{knitrout}
\end{document}
\documentclass[11pt, oneside]{article} % use "amsart" instead of "article" for AMSLaTeX format
%\usepackage{geometry} % See geometry.pdf to learn the layout options. There are lots.
%\geometry{letterpaper} % ... or a4paper or a5paper or ...
%\geometry{landscape} % Activate for for rotated page geometry
%\usepackage[parfill]{parskip} % Activate to begin paragraphs with an empty line rather than an indent
%\usepackage{graphicx} % Use pdf, png, jpg, or eps with pdflatex; use eps in DVI mode
% TeX will automatically convert eps --> pdf in pdflatex
%\usepackage{amssymb}
\usepackage[utf8]{inputenc}
%\usepackage[cyr]{aeguill}
%\usepackage[francais]{babel}
%\usepackage{hyperref}
\title{Positive selection on genes interacting with SARS-Cov2, comparison of different analysis}
\author{Marie Cariou}
\date{October 2020} % 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.
<<>>=
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)
tab$Gene.name<-as.character(tab$Gene.name)
tab$Gene.name[tab$PreyGene=="MTARC1"]<-"MTARC1"
@
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Comparisons Primates}
\subsection{Janet Young's results (Young-primate) VS DGINN-full's results}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8_1>>=
plot(tab$whole.gene.dN.dS.model.0, as.numeric(as.character(tab$dginn.primate_omegaM0Bpp)),
xlab="Omega Young-primate", ylab="DGINN-full's")
abline(0,1)
abline(lm(as.numeric(as.character(tab$dginn.primate_omegaM0Bpp))~tab$whole.gene.dN.dS.model.0), col="red")
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.4 & tab$dginn.primate_omegaM0Bpp>0.5,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name)
@
\subsection{Janet Young's results (Young-primate) VS Cooper's result}
Comparaison des Omega: colonne L "whole.gene.dN.dS.model.0" VS colonne "cooper.primates.Average\_dNdS".
<<omegaM7M8_2>>=
plot(tab$whole.gene.dN.dS.model.0, as.numeric(as.character(tab$cooper.primates.Average_dNdS)),
xlab="Omega Young-primate", ylab="Omega Cooper-primate")
abline(0,1)
abline(lm(as.numeric(as.character(tab$cooper.primates.Average_dNdS))~tab$whole.gene.dN.dS.model.0), col="red")
outlier<-tab[tab$whole.gene.dN.dS.model.0<0.4 & tab$cooper.primates.Average_dNdS>0.5,]
text(x=outlier$whole.gene.dN.dS.model.0,
y=outlier$cooper.primates.Average_dNdS,
outlier$Gene.name)
@
\subsection{Cooper's results (Cooper-primate) VS DGINN-full's results}
Comparaison des Omega: colonne "cooper.primates.Average\_dNdS" VS colonne "omega" dans la sortie de dginn.
<<omegaM7M8_3>>=
plot(tab$cooper.primates.Average_dNd, as.numeric(as.character(tab$dginn.primate_omegaM0Bpp)),
xlab="Omega Cooper-primate", ylab="DGINN-full's")
abline(0,1)
abline(lm(as.numeric(as.character(tab$dginn.primate_omegaM0Bpp))~tab$cooper.primates.Average_dNd), col="red")
outlier<-tab[tab$cooper.primates.Average_dNd<0.4 & tab$dginn.primate_omegaM0Bpp>0.5,]
text(x=outlier$cooper.primates.Average_dNd,
y=outlier$dginn.primate_omegaM0Bpp,
outlier$Gene.name)
@
\section{Overlap}
\subsection{Mondrian}
<<mondrianprimates>>=
library(Mondrian)
#######
monddata<-as.data.frame(tab$Gene.name)
dim(monddata)
dginnfulltmp<-rowSums(cbind(tab$dginn.primate_BUSTED=="Y", tab$dginn.primate_BppM1M2=="Y",
tab$dginn.primate_BppM7M8=="Y", tab$dginn.primate_codemlM1M2=="Y", tab$dginn.primate_codemlM7M8=="Y"))
monddata$primates_young<-ifelse(tab$pVal.M8vsM7<0.05, 1, 0)
monddata$primate_cooper<-ifelse(tab$cooper.primates.M7.M8_p_value<0.05, 1, 0)
monddata$primates_dginn_full<-ifelse(dginnfulltmp>=3, 1,0)
mondrian(na.omit(monddata[,2:4]), labels=c("Young", "Cooper", "DGINN-full >=3" ))
#####
monddata$primates_dginn_full<-ifelse(dginnfulltmp>=4, 1,0)
mondrian(na.omit(monddata[,2:4]), labels=c("Young", "Cooper", "DGINN-full >=4"))
@
\subsection{subsetR}
Just another representation of the same result.
<<subsetprimates>>=
library(UpSetR)
upsetdata<-as.data.frame(tab$Gene.name)
upsetdata$primates_young<-ifelse(tab$pVal.M8vsM7<0.05, 1, 0)
upsetdata$primate_cooper<-ifelse(tab$cooper.primates.M7.M8_p_value<0.05, 1, 0)
upsetdata$primates_dginn_full<-ifelse(dginnfulltmp>=3, 1,0)
upset(na.omit(upsetdata), nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
###
upsetdata$primates_dginn_full<-ifelse(dginnfulltmp>=4, 1,0)
upset(na.omit(upsetdata), nsets = 3, matrix.color = "#DC267F",
main.bar.color = "#648FFF", sets.bar.color = "#FE6100")
@
\section{Gene List}
Genes under positive selection for at least 4 methods.
<<>>=
dginnfulltmp<-rowSums(cbind(tab$dginn.primate_BUSTED=="Y",
tab$dginn.primate_BppM1M2=="Y",
tab$dginn.primate_BppM7M8=="Y",
tab$dginn.primate_codemlM1M2=="Y",
tab$dginn.primate_codemlM7M8=="Y"))
tab$Gene.name[dginnfulltmp>=4 & is.na(dginnfulltmp)==F]
tab$Gene.name[dginnfulltmp>=3 & is.na(dginnfulltmp)==F]
tmp<-tab[dginnfulltmp>=4 & is.na(dginnfulltmp)==F,
c("Gene.name","dginn.primate_BUSTED", "dginn.primate_BppM1M2",
"dginn.primate_BppM7M8","dginn.primate_codemlM1M2","dginn.primate_codemlM7M8")]
write.table(tmp, "geneList_DGINN_full_primate_pos4.txt", row.names=F, quote=F)
@
\section{Shiny like}
<<shiny, fig.height=11>>=
makeFig1 <- function(df){
# prepare data for colors etc
colMethods <- c("deepskyblue4", "darkorange" , "deepskyblue3" , "mediumseagreen" , "yellow3" , "black")
nameMethods <- c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8", "MEME")
metColor <- data.frame(Name = nameMethods , Col = colMethods , stringsAsFactors = FALSE)
# subset for this specific figure
#df <- df[df$nbY >= 1, ] # to drop genes found by 0 methods (big datasets)
xt <- df[, c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8")]
xt$Gene <- df$Gene
nbrMeth <- 5
# reverse order of dataframe so that genes with the most Y are at the bottom (to be on top of the barplot)
xt[,1:5] <- ifelse(xt[,1:5] == "Y", 1, 0)
# sort and Filter the 0 lines
xt<-xt[order(rowSums(xt[,1:5])),]
xt<-xt[rowSums(xt[,1:5])>2,]
row.names(xt)<-xt$Gene
xt<-xt[,1:5]
colFig1 <- metColor[which(metColor$Name %in% colnames(xt)) , ]
##### PART 1 : NUMBER OF METHODS
par(xpd = NA , mar=c(2,7,4,0) , oma = c(0,0,0,0) , mgp = c(3,0.3,0))
h = barplot(
t(xt),
border = NA ,
axes = F ,
col = adjustcolor(colFig1$Col, alpha.f = 1),
horiz = T ,
las = 2 ,
main = "Methods detecting positive selection" ,
cex.main = 0.85,
cex.names = min(50/nrow(xt), 1.5)
)
axis(3, line = 0, at = c(0:nbrMeth), label = c("0", rep("", nbrMeth -1), nbrMeth), tck = 0.02)
legend("bottomleft",
horiz = T,
border = colFig1$Col,
legend = colFig1$Name,
fill = colFig1$Col,
cex = 0.8,
bty = "n",
xpd = NA
)
}
@
<<>>=
source("covid_comp_shiny.R")
df<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
names(df)
dftmp<-tab[,c("File", "Name", "Gene.name",
"GeneSize", "dginn.primate_NbSpecies", "dginn.primate_omegaM0Bpp",
"dginn.primate_omegaM0codeml", "dginn.primate_BUSTED", "dginn.primate_BUSTED.p.value",
"dginn.primate_MEME.NbSites", "dginn.primate_MEME.PSS", "dginn.primate_BppM1M2",
"dginn.primate_BppM1M2.p.value", "dginn.primate_BppM1M2.NbSites", "dginn.primate_BppM1M2.PSS",
"dginn.primate_BppM7M8", "dginn.primate_BppM7M8.p.value", "dginn.primate_BppM7M8.NbSites",
"dginn.primate_BppM7M8.PSS", "dginn.primate_codemlM1M2", "dginn.primate_codemlM1M2.p.value",
"dginn.primate_codemlM1M2.NbSites","dginn.primate_codemlM1M2.PSS", "dginn.primate_codemlM7M8",
"dginn.primate_codemlM7M8.p.value", "dginn.primate_codemlM7M8.NbSites" , "dginn.primate_codemlM7M8.PSS")]
names(dftmp)<-names(df)
makeFig1(dftmp)
@
\end{document}
This diff is collapsed.
makeFig1 <- function(df){
# prepare data for colors etc
colMethods <- c("deepskyblue4", "darkorange" , "deepskyblue3" , "mediumseagreen" , "yellow3" , "black")
nameMethods <- c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8", "MEME")
metColor <- data.frame(Name = nameMethods , Col = colMethods , stringsAsFactors = FALSE)
# subset for this specific figure
#df <- df[df$nbY >= 1, ] # to drop genes found by 0 methods (big datasets)
xt <- df[, c("BUSTED", "BppM1M2", "BppM7M8", "codemlM1M2", "codemlM7M8")]
xt$Gene <- df$Gene
nbrMeth <- 5
# reverse order of dataframe so that genes with the most Y are at the bottom (to be on top of the barplot)
xt[,1:5] <- ifelse(xt[,1:5] == "Y", 1, 0)
# sort and Filter the 0 lines
xt<-xt[order(rowSums(xt[,1:5])),]
xt<-na.omit(xt[rowSums(xt[,1:5])>2,])
row.names(xt)<-xt$Gene
xt<-xt[,1:5]
colFig1 <- metColor[which(metColor$Name %in% colnames(xt)) , ]
##### PART 1 : NUMBER OF METHODS
par(xpd = NA , mar=c(2,7,4,0) , oma = c(0,0,0,0) , mgp = c(3,0.3,0))
h = barplot(
t(xt),
border = NA ,
axes = F ,
col = adjustcolor(colFig1$Col, alpha.f = 1),
horiz = T ,
las = 2 ,
main = "Methods detecting positive selection" ,
cex.main = 0.85,
cex.names = min(50/nrow(xt), 1.5)
)
axis(3, line = 0, at = c(0:nbrMeth), label = c("0", rep("", nbrMeth -1), nbrMeth), tck = 0.02)
legend("bottomleft",
horiz = T,
border = colFig1$Col,
legend = colFig1$Name,
fill = colFig1$Col,
cex = 0.8,
bty = "n",
xpd = NA
)
}
df<-read.delim(paste0(workdir,
"/data/DGINN_202005281649summary_cleaned.csv"),
fill=T, h=T, sep=",")
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