%\VignetteIndexEntry{OmicCircos vignette}
%\VignetteKeywords{visualization, circular plot} 
%\VignettePackage{OmicCircos}

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\begin{document}
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\title{The OmicCircos usages by examples}
\author{Ying Hu and Chunhua Yan}

\date{\today}

\maketitle

\tableofcontents

\clearpage

\section{Introduction}
The OmicCircos package generates high-quality circular plots for visualizing variations 
in omics data. The data can be gene or chromosome position-based values from mutation, 
copy number, expression, and methylation analyses. This package is capable of displaying 
variations in scatterplots, lines, and text labels. The relationships between genomic 
features can be presented in the forms of polygons and curves. By utilizing the statistical and graphic 
functions in R/Bioconductor environment, OmicCircos is also able to draw boxplots, histograms, 
and heatmaps from multiple sample data. Each track is drawn independently, which allows the 
use to optimize the track quickly and easily.

In this vignette, we will introduce the package plotting functions using simulation data 
and TCGA gene expression and copy number variation (cnv) data (http://www.cancergenome.nih.gov/). 

A quick way to load the vignette examples is:
\begin{lstlisting} 
vignette("OmicCircos")
\end{lstlisting} 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\clearpage
\section{Input file formats}
Four input data files are used in the package: segment data, mapping data, link data and 
link polygon data. Segment data are required to draw the anchor circular track. The remaining three data sets are used to draw additional tracks or connections.   

\subsection{segment data}
The \texttt{segment data} lay out the foundation of a circular graph and typically are 
used to draw the outmost anchor track. In the segment data, column 1 should be the segment or chromosome names. 
Columns 2 and 3 are the start and end positions of the segment. Columns 4 and 5 are optional 
which can contain additional description of the segment. The package comes with the segment 
data for human (hg18 and hg19) and mouse (mm9 and mm10). Let’s start by loading the package

\begin{lstlisting} 
options(stringsAsFactors = FALSE);
library(OmicCircos); 
## input hg19 cytogenetic band data
data(UCSC.hg19.chr);
head(UCSC.hg19.chr);
\end{lstlisting} 

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos); 
## input hg19 cytogenetic band data
data(UCSC.hg19.chr);
head(UCSC.hg19.chr);
@

\subsection{mapping data}
The \texttt{mapping data} are an R data frame which includes values to be drawn in 
the graph. In the mapping data, columns 1 and 2 are segment name and position respectively. Column 3 
and beyond is optional which can be the value or name. In the following example, 
the third column is the gene symbol. Column 4 and 5 are the gene expression values 
for each sample. 

\begin{lstlisting} 
options(stringsAsFactors = FALSE);
# load the OmicCircos-package
library(OmicCircos); 
## TCGA gene expression data
data(TCGA.BC.gene.exp.2k.60);
head(TCGA.BC.gene.exp.2k.60[,c(1:5)]);
\end{lstlisting} 

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos); 
## TCGA gene expression data
data(TCGA.BC.gene.exp.2k.60);
head(TCGA.BC.gene.exp.2k.60[,c(1:5)]);
@

\subsection{link data}
The \texttt{link data} are for drawing curves between two anchor points. In the link data, 
columns 1, 2, 3 are the segment name, position, label of the first anchor point; 
columns 4, 5, 6 are segment name, position, label of the second anchor point   
Column 7 is optional and could be used for the link type description.

\begin{lstlisting} 
options(stringsAsFactors = FALSE);
# load the OmicCircos-package
library(OmicCircos); 
## TCGA fusion gene data
data(TCGA.BC.fus);
head(TCGA.BC.fus[,c(1:6)]);
\end{lstlisting} 

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos); 
## TCGA fusion gene data
data(TCGA.BC.fus);
head(TCGA.BC.fus[,c(1:6)]);
@

\subsection{link polygon data}
The \texttt{link polygon data} are for connecting two segments with a polygon graph. 
In the link polygon data, columns 1, 2 and 3 are the name, start and end points for the first segment and columns 4, 5 
and 6 are the name, start and end points for the second segment.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\clearpage
\section{The package functions}
There are three main functions in the package: \texttt{sim.circos}, \texttt{segAnglePo} 
and \texttt{circos}. \texttt{sim.circos} generates simulation data for drawing circular 
plots. \texttt{segAnglePo} converts the genomic (linear) coordinates (chromosome base pair positions) 
to the angle based coordinates along circumference. circos enables users to superimpose graphics on the circle track.

\subsection{sim.circos}
The \texttt{sim.circos} function generates four simutatedinput data files, which allows 
users to preview the graph quickly with different parameters and design an optimal 
presentation with desired features. In the following example, there are 10 segments, 
10 individuals, 10 links, and 10 link polygons. Each segment has the value ranging 
from 20 to 50. The values will be generated by rnorm(1) + i. The i is the ordinal 
number of the segments. The values are increased by the segment order.

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 10;
sim.out     <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, link.pg=link.pg.num);
@

\begin{lstlisting} 
options(stringsAsFactors = FALSE);
# load the OmicCircos-package
library(OmicCircos);
# set up the initial parameters
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 10;
# run sim.circos function
sim.out     <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, link.pg=link.pg.num);
# display the data set names
names(sim.out)
# display the segment data
head(sim.out$seg.frame[,c(1:3)])
\end{lstlisting} 

<<echo=FALSE>>=
head(sim.out$seg.frame[,c(1:3)])
@

\begin{lstlisting} 
# display the mapping data
head(sim.out$seg.mapping[,c(1:5)])
\end{lstlisting} 

<<echo=FALSE>>=
head(sim.out$seg.mapping[,c(1:5)])
@

\begin{lstlisting} 
# display the linking data
head(sim.out$seg.link)
\end{lstlisting} 

<<echo=FALSE>>=
head(sim.out$seg.link)
@

\begin{lstlisting} 
# display the linking polygon data
head(sim.out$seg.link.pg)
\end{lstlisting} 

<<echo=FALSE>>=
head(sim.out$seg.link.pg)
@

\subsection{segAnglePo}
The \texttt{segAnglePo} function converts the segment pointer positions (linear coordinates) 
into angle values (the angle based coordinates along circumference) and returns a data frame. 
It specifies the circle size, number of segments, and segment length.

\begin{lstlisting} 
library(OmicCircos);
options(stringsAsFactors = FALSE);
set.seed(1234);
## initial values for simulation data 
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 4;
## output simulation data
sim.out <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, 
  link.pg=link.pg.num);
seg.f     <- sim.out$seg.frame;
seg.v     <- sim.out$seg.mapping;
link.v    <- sim.out$seg.link
link.pg.v <- sim.out$seg.link.pg
seg.num   <- length(unique(seg.f[,1]));
## select segments
seg.name <- paste("chr", 1:seg.num, sep="");
db       <- segAnglePo(seg.f, seg=seg.name);
\end{lstlisting} 

<<echo=FALSE>>=
library(OmicCircos);
options(stringsAsFactors = FALSE);
set.seed(1234);
## initial values for simulation data 
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 4;
## output simulation data
sim.out <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, 
  link.pg=link.pg.num);
seg.f     <- sim.out$seg.frame;
seg.v     <- sim.out$seg.mapping;
link.v    <- sim.out$seg.link
link.pg.v <- sim.out$seg.link.pg
seg.num   <- length(unique(seg.f[,1]));
## select segments
seg.name <- paste("chr", 1:seg.num, sep="");
db       <- segAnglePo(seg.f, seg=seg.name);
db[,2]   <- round(as.numeric(db[,2]), 3);
db[,3]   <- round(as.numeric(db[,3]), 3);
db
@

In the above example, there are 10 segments in a circle. Column 1 is segment name. 
Columns 2, 3 are the start and end angles of the segment. Column 4 and 5 are the 
accumulative start and end positions. Column 6 and 7 are the start and end position 
for the segment. The plotting is clockwise starting at 12 o’clock (270 degree).

\subsection{circos}
The \texttt{circos} is the main function to draw different shapes of the circle. For 
example, expression and CNV data can be viewed using basic shapes like scatterplots 
and lines while structural variations such as translocations and fusion proteins can 
be viewed using curves and polygons to connect different segments. Additionally, multiple 
sample expression and CNV data sets can be displayed as boxplots, histograms, or heatmaps 
using standard R functions such as apply. The usage of this function is illustrated in 
the next section.  

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\clearpage
\section{Plotting parameters}
\subsection{basic plotting}

The input data sets were generated by texttt{sim.circos} function.

\begin{lstlisting} 
options(stringsAsFactors = FALSE);
library(OmicCircos);
options(stringsAsFactors = FALSE);
set.seed(1234);

# initial
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 4;

sim.out <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, 
  link.pg=link.pg.num);

seg.f     <- sim.out$seg.frame;
seg.v     <- sim.out$seg.mapping;
link.v    <- sim.out$seg.link
link.pg.v <- sim.out$seg.link.pg
seg.num   <- length(unique(seg.f[,1]));

# name segment (option)
seg.name <- paste("chr", 1:seg.num, sep="");
db       <- segAnglePo(seg.f, seg=seg.name);
# set transparent colors
colors   <- rainbow(seg.num, alpha=0.5);
\end{lstlisting} 

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);
set.seed(1234);
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 4;
sim.out <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, 
  link.pg=link.pg.num);
seg.f     <- sim.out$seg.frame;
seg.v     <- sim.out$seg.mapping;
link.v    <- sim.out$seg.link
link.pg.v <- sim.out$seg.link.pg
seg.num   <- length(unique(seg.f[,1]));
seg.name <- paste("chr", 1:seg.num, sep="");
db       <- segAnglePo(seg.f, seg=seg.name);
colors   <- rainbow(seg.num, alpha=0.5);
@

To get perfect circle, the output figure should be in square. The output file is 
the same width and height. The same line values are in the margin of the graphical 
parameters. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 1
\begin{lstlisting} 
par(mar=c(2, 2, 2, 2));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

circos(R=400, cir=db, type="chr",  col=colors, print.chr.lab=TRUE, W=4, scale=TRUE);
circos(R=360, cir=db, W=40, mapping=seg.v, col.v=3, type="l",   B=TRUE, col=colors[1], lwd=2, scale=TRUE);
circos(R=320, cir=db, W=40, mapping=seg.v, col.v=3, type="ls",  B=FALSE, col=colors[9], lwd=2, scale=TRUE);
circos(R=280, cir=db, W=40, mapping=seg.v, col.v=3, type="lh",  B=TRUE, col=colors[7], lwd=2, scale=TRUE);
circos(R=240, cir=db, W=40, mapping=seg.v, col.v=19, type="ml",  B=FALSE, col=colors, lwd=2, scale=TRUE);
circos(R=200, cir=db, W=40, mapping=seg.v, col.v=19, type="ml2", B=TRUE, col=colors, lwd=2);
circos(R=160, cir=db, W=40, mapping=seg.v, col.v=19, type="ml3", B=FALSE, cutoff=5, lwd=2);
circos(R=150, cir=db, W=40, mapping=link.v, type="link", lwd=2, col=colors[c(1,7)]);
circos(R=150, cir=db, W=40, mapping=link.pg.v, type="link.pg", lwd=2, col=sample(colors,link.pg.num));
\end{lstlisting} 

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette1, fig=TRUE, echo=FALSE, width=8, height=8>>=
par(mar=c(0, 0, 0, 0));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");
circos(R=400, cir=db, type="chr",  col=colors, print.chr.lab=TRUE, W=4, scale=TRUE);
circos(R=360, cir=db, W=40, mapping=seg.v, col.v=3, type="l",   B=TRUE, col=colors[1], lwd=2, scale=TRUE);
circos(R=320, cir=db, W=40, mapping=seg.v, col.v=3, type="ls",  B=FALSE, col=colors[9], lwd=2, scale=TRUE);
circos(R=280, cir=db, W=40, mapping=seg.v, col.v=3, type="lh",  B=TRUE, col=colors[7], lwd=2, scale=TRUE);
circos(R=240, cir=db, W=40, mapping=seg.v, col.v=19, type="ml",  B=FALSE, col=colors, lwd=2, scale=TRUE);
circos(R=200, cir=db, W=40, mapping=seg.v, col.v=19, type="ml2", B=TRUE, col=colors, lwd=2);
circos(R=160, cir=db, W=40, mapping=seg.v, col.v=19, type="ml3", B=FALSE, cutoff=5, lwd=2);
circos(R=150, cir=db, W=40, mapping=link.v, type="link", lwd=2, col=colors[c(1,7)]);
circos(R=150, cir=db, W=40, mapping=link.pg.v, type="link.pg", lwd=2, col=sample(colors,link.pg.num));
@

\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette1}
\end{figure}

Figure 1 from outside to inside: Track is lines; Track 2 is the stair steps;
Track 3 is the horizontal lines; Tracks 4, 5 and 6 are the multiple lines, stair steps and 
horizontal lines for multiple the samples.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 2

\begin{lstlisting} 
options(stringsAsFactors = FALSE);
library(OmicCircos);
set.seed(1234);

## initial values for simulation data 
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 4;
## output simulation data
sim.out <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, 
  link.pg=link.pg.num);

seg.f     <- sim.out$seg.frame;
seg.v     <- sim.out$seg.mapping;
link.v    <- sim.out$seg.link
link.pg.v <- sim.out$seg.link.pg
seg.num   <- length(unique(seg.f[,1]));

## select segments
seg.name <- paste("chr", 1:seg.num, sep="");
db       <- segAnglePo(seg.f, seg=seg.name);

colors   <- rainbow(seg.num, alpha=0.5);
\end{lstlisting} 

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);
set.seed(1234);

## initial values for simulation data 
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 4;
## output simulation data
sim.out <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, 
  link.pg=link.pg.num);

seg.f     <- sim.out$seg.frame;
seg.v     <- sim.out$seg.mapping;
link.v    <- sim.out$seg.link
link.pg.v <- sim.out$seg.link.pg
seg.num   <- length(unique(seg.f[,1]));

## select segments
seg.name <- paste("chr", 1:seg.num, sep="");
db       <- segAnglePo(seg.f, seg=seg.name);

colors   <- rainbow(seg.num, alpha=0.5);
@

\begin{lstlisting} 
par(mar=c(2, 2, 2, 2));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

circos(R=400, type="chr", cir=db, col=colors, print.chr.lab=TRUE, W=4, scale=TRUE);
circos(R=360, cir=db, W=40, mapping=seg.v, col.v=8, type="box",   B=TRUE, col=colors[1], lwd=0.1, scale=TRUE);
circos(R=320, cir=db, W=40, mapping=seg.v, col.v=8, type="hist",  B=TRUE, col=colors[3], lwd=0.1, scale=TRUE);
circos(R=280, cir=db, W=40, mapping=seg.v, col.v=8, type="ms",  B=TRUE, col=colors[7], lwd=0.1, scale=TRUE);
circos(R=240, cir=db, W=40, mapping=seg.v, col.v=3, type="h",  B=FALSE,  col=colors[2], lwd=0.1);
circos(R=200, cir=db, W=40, mapping=seg.v, col.v=3, type="s", B=TRUE, col=colors, lwd=0.1);
circos(R=160, cir=db, W=40, mapping=seg.v, col.v=3, type="b", B=FALSE, col=colors, lwd=0.1);
circos(R=150, cir=db, W=40, mapping=link.v, type="link", lwd=2, col=colors[c(1,7)]);
circos(R=150, cir=db, W=40, mapping=link.pg.v, type="link.pg", lwd=2, col=sample(colors,link.pg.num));
\end{lstlisting} 


\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette2, echo=FALSE, fig=TRUE, width=8, height=8>>=
par(mar=c(0, 0, 0, 0));

plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

circos(R=400, type="chr", cir=db, col=colors, print.chr.lab=TRUE, W=4, scale=TRUE);
circos(R=360, cir=db, W=40, mapping=seg.v, col.v=8, type="box",   B=TRUE, col=colors[1], lwd=0.1, scale=TRUE);
circos(R=320, cir=db, W=40, mapping=seg.v, col.v=8, type="hist",  B=TRUE, col=colors[3], lwd=0.1, scale=TRUE);
circos(R=280, cir=db, W=40, mapping=seg.v, col.v=8, type="ms",  B=TRUE, col=colors[7], lwd=0.1, scale=TRUE);
circos(R=240, cir=db, W=40, mapping=seg.v, col.v=3, type="h",  B=FALSE,  col=colors[2], lwd=0.1);
circos(R=200, cir=db, W=40, mapping=seg.v, col.v=3, type="s", B=TRUE, col=colors, lwd=0.1);
circos(R=160, cir=db, W=40, mapping=seg.v, col.v=3, type="b", B=FALSE, col=colors, lwd=0.1);
circos(R=150, cir=db, W=40, mapping=link.v, type="link", lwd=2, col=colors[c(1,7)]);
circos(R=150, cir=db, W=40, mapping=link.pg.v, type="link.pg", lwd=2, col=sample(colors,link.pg.num));
@
\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette2}
\end{figure}

Figure 2  from outside to inside: Track 1 is the boxplot for the samples from column 8 (col.v=8) to the last column in the data frame seg.v with the scale; Track 2 and track 3 are the histograms (in horizontal) and the scatter plots for multiple samples as track 1. Tracks 4, 5 and 6 are the histogram (in vertical), scatter plot and vertical line for just one sample (column 3 in the data frame seg.v).


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 3

\begin{lstlisting}
options(stringsAsFactors = FALSE);
library(OmicCircos);
set.seed(1234);

## initial values for simulation data 
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 4;
## output simulation data
sim.out <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, 
  link.pg=link.pg.num);

seg.f     <- sim.out$seg.frame;
seg.v     <- sim.out$seg.mapping;
link.v    <- sim.out$seg.link
link.pg.v <- sim.out$seg.link.pg
seg.num   <- length(unique(seg.f[,1]));

## 
seg.name <- paste("chr", 1:seg.num, sep="");
db       <- segAnglePo(seg.f, seg=seg.name);

colors   <- rainbow(seg.num, alpha=0.5);
\end{lstlisting}

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);
set.seed(1234);
## initial values for simulation data 
seg.num     <- 10;
ind.num     <- 20;
seg.po      <- c(20:50);
link.num    <- 10;
link.pg.num <- 4;
## output simulation data
sim.out <- sim.circos(seg=seg.num, po=seg.po, ind=ind.num, link=link.num, 
  link.pg=link.pg.num);
seg.f     <- sim.out$seg.frame;
seg.v     <- sim.out$seg.mapping;
link.v    <- sim.out$seg.link
link.pg.v <- sim.out$seg.link.pg
seg.num   <- length(unique(seg.f[,1]));
seg.name <- paste("chr", 1:seg.num, sep="");
db       <- segAnglePo(seg.f, seg=seg.name);
colors   <- rainbow(seg.num, alpha=0.5);
@

\begin{lstlisting}
par(mar=c(2, 2, 2, 2));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

circos(R=400, type="chr", cir=db, col=colors, print.chr.lab=TRUE, W=4, scale=TRUE);
circos(R=360, cir=db, W=40, mapping=seg.v, col.v=8, type="quant90", B=FALSE, col=colors, lwd=2, scale=TRUE);
circos(R=320, cir=db, W=40, mapping=seg.v, col.v=3, type="sv", B=TRUE, col=colors[7],  scale=TRUE);
circos(R=280, cir=db, W=40, mapping=seg.v, col.v=3, type="ss", B=FALSE, col=colors[3],  scale=TRUE);
circos(R=240, cir=db, W=40, mapping=seg.v, col.v=8, type="heatmap", lwd=3);
circos(R=200, cir=db, W=40, mapping=seg.v, col.v=3, type="s.sd", B=FALSE, col=colors[4]);
circos(R=160, cir=db, W=40, mapping=seg.v, col.v=3, type="ci95", B=TRUE, col=colors[4], lwd=2);
circos(R=150, cir=db, W=40, mapping=link.v, type="link", lwd=2, col=colors[c(1,7)]);
circos(R=150, cir=db, W=40, mapping=link.pg.v, type="link.pg", lwd=2, col=sample(colors,link.pg.num));

the.col1=rainbow(10, alpha=0.5)[3];
highlight <- c(160, 410, 6, 2, 6, 10, the.col1, the.col1);
circos(R=110, cir=db, W=40, mapping=highlight, type="hl", lwd=1);

the.col1=rainbow(10, alpha=0.1)[3];
the.col2=rainbow(10, alpha=0.5)[1];
highlight <- c(160, 410, 3, 12, 3, 20, the.col1, the.col2);
circos(R=110, cir=db, W=40, mapping=highlight, type="hl", lwd=2);
\end{lstlisting}

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette3, echo=FALSE, fig=TRUE, width=8, height=8>>=
par(mar=c(0, 0, 0, 0));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

circos(R=400, type="chr", cir=db, col=colors, print.chr.lab=TRUE, W=4, scale=TRUE);
circos(R=360, cir=db, W=40, mapping=seg.v, col.v=8, type="quant90", B=FALSE, col=colors, lwd=2, scale=TRUE);
circos(R=320, cir=db, W=40, mapping=seg.v, col.v=3, type="sv", B=TRUE, col=colors[7],  scale=TRUE);
circos(R=280, cir=db, W=40, mapping=seg.v, col.v=3, type="ss", B=FALSE, col=colors[3],  scale=TRUE);
circos(R=240, cir=db, W=40, mapping=seg.v, col.v=20, type="heatmap", lwd=3);
circos(R=200, cir=db, W=40, mapping=seg.v, col.v=3, type="s.sd", B=FALSE, col=colors[4]);
circos(R=160, cir=db, W=40, mapping=seg.v, col.v=3, type="ci95", B=TRUE, col=colors[4], lwd=2);
circos(R=150, cir=db, W=40, mapping=link.v, type="link", lwd=2, col=colors[c(1,7)]);
circos(R=150, cir=db, W=40, mapping=link.pg.v, type="link.pg", lwd=2, col=sample(colors,link.pg.num));

the.col1=rainbow(10, alpha=0.5)[3];
highlight <- c(160, 410, 6, 2, 6, 10, the.col1, the.col1);
circos(R=110, cir=db, W=40, mapping=highlight, type="hl", lwd=1);

the.col1=rainbow(10, alpha=0.1)[3];
the.col2=rainbow(10, alpha=0.5)[1];
highlight <- c(160, 410, 3, 12, 3, 20, the.col1, the.col2);
circos(R=110, cir=db, W=40, mapping=highlight, type="hl", lwd=2);

@
\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette3}
\end{figure}

Figure 3 from outside to inside: Track 1 is the three lines for quantile values for the 
samples from column 8 (col.v=8) to the last column in the data frame seg.v with the scale. The middle line is for 
the median, the outside line and the inside line are for 90\% and the 10\%, respectively; Track 
2 is the circle points with the center=median and radium=variance; Track 3 is the circle plot 
with the center equal to the mean and scaled value (for example, the range from 0 to 3);  
Tracks 4 is the heatmap for the samples from column 8 (col.v=8) to the last column in the data frame seg.v; Track 5 is the circle plot with the center=median and radius=standard deviation; Track 6 is the 95\% confidence interval of the samples.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 4
\subsection{annotation}

\begin{lstlisting}
options(stringsAsFactors = FALSE);
library(OmicCircos);
set.seed(1234);

## load mm cytogenetic band data
data("UCSC.mm10.chr", package="OmicCircos");
ref     <- UCSC.mm10.chr;
ref[,1] <- gsub("chr", "", ref[,1]);
## initial values for simulation data 
colors <- rainbow(10, alpha=0.8);
lab.n  <- 50;
cnv.n  <- 200;
arc.n  <- 30;
fus.n  <- 10;

## make arc data
arc.d <- c();
for (i in 1:arc.n){
  chr     <- sample(1:19, 1);
  chr.i   <- which(ref[,1]==chr);
  chr.arc <- ref[chr.i,];
  arc.i   <- sample(1:nrow(chr.arc), 2);
  arc.d   <- rbind(arc.d, c(chr.arc[arc.i[1],c(1,2)], chr.arc[arc.i[2],c(2,4)]));
}
colnames(arc.d) <- c("chr", "start", "end", "value");

## make fusion
fus.d <- c();
for (i in 1:fus.n){
  chr1    <- sample(1:19, 1);
  chr2    <- sample(1:19, 1);
  chr1.i  <- which(ref[,1]==chr1);
  chr2.i  <- which(ref[,1]==chr2);
  chr1.f  <- ref[chr1.i,];
  chr2.f  <- ref[chr2.i,];
  fus1.i  <- sample(1:nrow(chr1.f), 1);
  fus2.i  <- sample(1:nrow(chr2.f), 1);
  n1      <- paste0("geneA", i);
  n2      <- paste0("geneB", i);
  fus.d   <- rbind(fus.d, c(chr1.f[fus1.i,c(1,2)], n1, chr2.f[fus2.i,c(1,2)], n2));
}
colnames(fus.d) <- c("chr1", "po1", "gene1", "chr2", "po2", "gene2");

cnv.i <- sample(1:nrow(ref), cnv.n);
vale  <- rnorm(cnv.n);
cnv.d <- data.frame(ref[cnv.i,c(1,2)], value=vale);

\end{lstlisting}

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);
set.seed(1234);

data("UCSC.mm10.chr", package="OmicCircos");
ref     <- UCSC.mm10.chr;
ref[,1] <- gsub("chr", "", ref[,1]);

colors <- rainbow(10, alpha=0.8);
lab.n  <- 50;
cnv.n  <- 200;
arc.n  <- 30;
fus.n  <- 10;

## make arc data
arc.d <- c();
for (i in 1:arc.n){
  chr     <- sample(1:19, 1);
  chr.i   <- which(ref[,1]==chr);
  chr.arc <- ref[chr.i,];
  arc.i   <- sample(1:nrow(chr.arc), 2);
  arc.d   <- rbind(arc.d, c(chr.arc[arc.i[1],c(1,2)], chr.arc[arc.i[2],c(2,4)]));
}
colnames(arc.d) <- c("chr", "start", "end", "value");

## make fusion
fus.d <- c();
for (i in 1:fus.n){
  chr1    <- sample(1:19, 1);
  chr2    <- sample(1:19, 1);
  chr1.i  <- which(ref[,1]==chr1);
  chr2.i  <- which(ref[,1]==chr2);
  chr1.f  <- ref[chr1.i,];
  chr2.f  <- ref[chr2.i,];
  fus1.i  <- sample(1:nrow(chr1.f), 1);
  fus2.i  <- sample(1:nrow(chr2.f), 1);
  n1      <- paste0("geneA", i);
  n2      <- paste0("geneB", i);
  fus.d   <- rbind(fus.d, c(chr1.f[fus1.i,c(1,2)], n1, chr2.f[fus2.i,c(1,2)], n2));
}
colnames(fus.d) <- c("chr1", "po1", "gene1", "chr2", "po2", "gene2");

cnv.i <- sample(1:nrow(ref), cnv.n);
vale  <- rnorm(cnv.n);
cnv.d <- data.frame(ref[cnv.i,c(1,2)], value=vale);

@

\begin{lstlisting}
par(mar=c(2, 2, 2, 2));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="");

circos(R=400, type="chr", cir="mm10", print.chr.lab=TRUE, W=4, scale=TRUE);
circos(R=340, cir="mm10", W=60, mapping=cnv.d, type="b3", B=TRUE, col=colors[7]);
circos(R=340, cir="mm10", W=60, mapping=cnv.d, type="s2", B=FALSE, col=colors[1], cex=0.5);
circos(R=280, cir="mm10", W=60, mapping=arc.d, type="arc2", B=FALSE, col=colors, lwd=10, cutoff=0);
circos(R=220, cir="mm10", W=60, mapping=cnv.d, col.v=3, type="b2", B=TRUE, cutoff=-0.2, col=colors[c(7,9)], lwd=2);
circos(R=160, cir="mm10", W=60, mapping=arc.d, col.v=4, type="arc",  B=FALSE, col=colors[c(1,7)], lwd=4, scale=TRUE);
circos(R=150, cir="mm10", W=10, mapping=fus.d, type="link",  lwd=2, col=colors[c(1,7,9)]);

\end{lstlisting}

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette4, echo=FALSE, fig=TRUE, width=8, height=8>>=
par(mar=c(0, 0, 0, 0));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="");

circos(R=400, type="chr", cir="mm10", print.chr.lab=TRUE, W=4, scale=TRUE);
circos(R=340, cir="mm10", W=60, mapping=cnv.d, type="b3", B=TRUE, col=colors[7]);
circos(R=340, cir="mm10", W=60, mapping=cnv.d, type="s2", B=FALSE, col=colors[1], cex=0.5);
circos(R=280, cir="mm10", W=60, mapping=arc.d, type="arc2", B=FALSE, col=colors, lwd=10, cutoff=0);
circos(R=220, cir="mm10", W=60, mapping=cnv.d, col.v=3, type="b2", B=TRUE, cutoff=-0.2, col=colors[c(7,9)], lwd=2);
circos(R=160, cir="mm10", W=60, mapping=arc.d, col.v=4, type="arc",  B=FALSE, col=colors[c(1,7)], lwd=4, scale=TRUE);
circos(R=150, cir="mm10", W=10, mapping=fus.d, type="link",  lwd=2, col=colors[c(1,7,9)]);

@
\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette4}
\end{figure}

%\clearpage
\subsection{label}

Figure 4 from outside to inside: Track 1 is the vertical lines with the same length and radius which can be used for the annotation of SNP positions; Track 2 is
the arcs with the same radius which can be used for the segment annotation, e.g. cnv (
copy number variation); Track 3 is the barplot with positive and negative values; 
Track 4 is the arcs in the different radius. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 5 (vignette5.1)

\begin{lstlisting}
options(stringsAsFactors = FALSE);
library(OmicCircos);

data("TCGA.PAM50_genefu_hg18");
data("TCGA.BC.fus");
data("TCGA.BC.cnv.2k.60");
data("TCGA.BC.gene.exp.2k.60");
data("TCGA.BC.sample60");
data("TCGA.BC_Her2_cnv_exp");

pvalue <- -1 * log10(TCGA.BC_Her2_cnv_exp[,5]);
pvalue <- cbind(TCGA.BC_Her2_cnv_exp[,c(1:3)], pvalue);

Her2.i <- which(TCGA.BC.sample60[,2] == "Her2");
Her2.n <- TCGA.BC.sample60[Her2.i,1];

Her2.j <- which(colnames(TCGA.BC.cnv.2k.60) %in% Her2.n);
cnv    <- TCGA.BC.cnv.2k.60[,c(1:3,Her2.j)]; 
cnv.m  <- cnv[,c(4:ncol(cnv))];
cnv.m[cnv.m >  2] <- 2;
cnv.m[cnv.m < -2] <- -2;
cnv <- cbind(cnv[,1:3], cnv.m);

Her2.j   <- which(colnames(TCGA.BC.gene.exp.2k.60) %in% Her2.n);
gene.exp <- TCGA.BC.gene.exp.2k.60[,c(1:3,Her2.j)]; 
colors <- rainbow(10, alpha=0.5);
\end{lstlisting}

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);

data("TCGA.PAM50_genefu_hg18");
data("TCGA.BC.fus");
data("TCGA.BC.cnv.2k.60");
data("TCGA.BC.gene.exp.2k.60");
data("TCGA.BC.sample60");
data("TCGA.BC_Her2_cnv_exp");

pvalue <- -1 * log10(TCGA.BC_Her2_cnv_exp[,5]);
pvalue <- cbind(TCGA.BC_Her2_cnv_exp[,c(1:3)], pvalue);

Her2.i <- which(TCGA.BC.sample60[,2] == "Her2");
Her2.n <- TCGA.BC.sample60[Her2.i,1];

Her2.j <- which(colnames(TCGA.BC.cnv.2k.60) %in% Her2.n);
cnv    <- TCGA.BC.cnv.2k.60[,c(1:3,Her2.j)]; 
cnv.m  <- cnv[,c(4:ncol(cnv))];
cnv.m[cnv.m >  2] <- 2;
cnv.m[cnv.m < -2] <- -2;
cnv <- cbind(cnv[,1:3], cnv.m);

Her2.j   <- which(colnames(TCGA.BC.gene.exp.2k.60) %in% Her2.n);
gene.exp <- TCGA.BC.gene.exp.2k.60[,c(1:3,Her2.j)]; 
colors <- rainbow(10, alpha=0.5);
@

\begin{lstlisting}
par(mar=c(2, 2, 2, 2));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="");

circos(R=300, type="chr", cir="hg18", print.chr.lab=FALSE, W=4);
circos(R=310, cir="hg18", W=20, mapping=TCGA.PAM50_genefu_hg18, type="label", 
       side="out", col=c("black", "blue","red"), cex=0.4);
circos(R=250, cir="hg18", W=50, mapping=cnv, col.v=4, type="ml3", B=FALSE, col=colors[7], cutoff=0, scale=TRUE);
circos(R=200, cir="hg18", W=50, mapping=gene.exp, col.v=4, type="ml3", B=TRUE, col=colors[3], cutoff=0, scale=TRUE);
circos(R=140, cir="hg18", W=50, mapping=pvalue, col.v=4, type="l", B=FALSE, col=colors[1], scale=TRUE);
## set fusion gene colors
cols  <- rep(colors[7], nrow(TCGA.BC.fus));
col.i <- which(TCGA.BC.fus[,1]==TCGA.BC.fus[,4]);
cols[col.i] <- colors[1];
circos(R=132, cir="hg18", W=50, mapping=TCGA.BC.fus, type="link", col=cols, lwd=2);
@
\end{lstlisting}

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette5, echo=FALSE, fig=TRUE, width=8, height=8>>=
par(mar=c(0, 0, 0, 0));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="");

circos(R=300, type="chr", cir="hg18", print.chr.lab=FALSE, W=4);
circos(R=310, cir="hg18", W=20, mapping=TCGA.PAM50_genefu_hg18, type="label", 
       side="out", col=c("black", "blue","red"), cex=0.4);
circos(R=250, cir="hg18", W=50, mapping=cnv, col.v=11, type="ml3", B=FALSE, col=colors[7], cutoff=0, scale=TRUE);
circos(R=200, cir="hg18", W=50, mapping=gene.exp, col.v=11, type="ml3", B=TRUE, col=colors[3], cutoff=0, scale=TRUE);
circos(R=140, cir="hg18", W=50, mapping=pvalue, col.v=4, type="l", B=FALSE, col=colors[1], scale=TRUE);
## set fusion gene colors
cols  <- rep(colors[7], nrow(TCGA.BC.fus));
col.i <- which(TCGA.BC.fus[,1]==TCGA.BC.fus[,4]);
cols[col.i] <- colors[1];
circos(R=132, cir="hg18", W=50, mapping=TCGA.BC.fus, type="link", col=cols, lwd=2);
@
\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette5}
\end{figure}

Figure 5 is an example of adding outside labels.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 6  (vignette5.2)

\begin{lstlisting}
par(mar=c(2, 2, 2, 2));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");
circos(R=300, type="chr", cir="hg18", col=TRUE, print.chr.lab=FALSE, W=4);
circos(R=290, cir="hg18", W=20, mapping=TCGA.PAM50_genefu_hg18, type="label", side="in", col=c("black", "blue"), cex=0.4);
circos(R=310, cir="hg18", W=50, mapping=cnv, col.v=4, type="ml3", B=TRUE, col=colors[7], cutoff=0, scale=TRUE);
circos(R=150, cir="hg18", W=50, mapping=gene.exp, col.v=4, type="ml3", B=TRUE, col=colors[3], cutoff=0, scale=TRUE);
circos(R=90,  cir="hg18", W=50, mapping=pvalue, col.v=4, type="l", B=FALSE, col=colors[1], scale=TRUE);
circos(R=82, cir="hg18", W=50, mapping=TCGA.BC.fus, type="link", col=cols, lwd=2);
@
\end{lstlisting}

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette6, echo=FALSE, fig=TRUE, width=8, height=8>>=
par(mar=c(0, 0, 0, 0));
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="");
circos(R=300, type="chr", cir="hg18", col=TRUE, print.chr.lab=FALSE, W=4);
circos(R=290, cir="hg18", W=20, mapping=TCGA.PAM50_genefu_hg18, type="label", side="in", col=c("black", "blue"), cex=0.4);
circos(R=310, cir="hg18", W=50, mapping=cnv, col.v=11, type="ml3", B=TRUE, col=colors[7], cutoff=0, scale=TRUE);
circos(R=150, cir="hg18", W=50, mapping=gene.exp, col.v=11, type="ml3", B=TRUE, col=colors[3], cutoff=0, scale=TRUE);
circos(R=90,  cir="hg18", W=50, mapping=pvalue, col.v=4, type="l", B=FALSE, col=colors[1], scale=TRUE);
circos(R=82, cir="hg18", W=50, mapping=TCGA.BC.fus, type="link", col=cols, lwd=2);
@
\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette6}
\end{figure}

Figure 6 is an example of the inside labels.
%\clearpage
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 7  (vignette6)
\subsection{heatmap}

\begin{lstlisting}
options(stringsAsFactors = FALSE);
library(OmicCircos);

data("TCGA.PAM50_genefu_hg18");
data("TCGA.BC.fus");
data("TCGA.BC.cnv.2k.60");
data("TCGA.BC.gene.exp.2k.60");
data("TCGA.BC.sample60");
data("TCGA.BC_Her2_cnv_exp");

pvalue <- -1 * log10(TCGA.BC_Her2_cnv_exp[,5]);
pvalue <- cbind(TCGA.BC_Her2_cnv_exp[,c(1:3)], pvalue);

Her2.i <- which(TCGA.BC.sample60[,2] == "Her2");
Her2.n <- TCGA.BC.sample60[Her2.i,1];

Her2.j <- which(colnames(TCGA.BC.cnv.2k.60) %in% Her2.n);
cnv    <- TCGA.BC.cnv.2k.60[,c(1:3,Her2.j)]; 
cnv.m  <- cnv[,c(4:ncol(cnv))];
cnv.m[cnv.m >  2] <- 2;
cnv.m[cnv.m < -2] <- -2;
cnv <- cbind(cnv[,1:3], cnv.m);

Her2.j   <- which(colnames(TCGA.BC.gene.exp.2k.60) %in% Her2.n);
gene.exp <- TCGA.BC.gene.exp.2k.60[,c(1:3,Her2.j)]; 

colors <- rainbow(10, alpha=0.5);
\end{lstlisting}

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);

data("TCGA.PAM50_genefu_hg18");
data("TCGA.BC.fus");
data("TCGA.BC.cnv.2k.60");
data("TCGA.BC.gene.exp.2k.60");
data("TCGA.BC.sample60");
data("TCGA.BC_Her2_cnv_exp");

pvalue <- -1 * log10(TCGA.BC_Her2_cnv_exp[,5]);
pvalue <- cbind(TCGA.BC_Her2_cnv_exp[,c(1:3)], pvalue);

Her2.i <- which(TCGA.BC.sample60[,2] == "Her2");
Her2.n <- TCGA.BC.sample60[Her2.i,1];

Her2.j <- which(colnames(TCGA.BC.cnv.2k.60) %in% Her2.n);
cnv    <- TCGA.BC.cnv.2k.60[,c(1:3,Her2.j)]; 
cnv.m  <- cnv[,c(4:ncol(cnv))];
cnv.m[cnv.m >  2] <- 2;
cnv.m[cnv.m < -2] <- -2;
cnv <- cbind(cnv[,1:3], cnv.m);

Her2.j   <- which(colnames(TCGA.BC.gene.exp.2k.60) %in% Her2.n);
gene.exp <- TCGA.BC.gene.exp.2k.60[,c(1:3,Her2.j)]; 

colors <- rainbow(10, alpha=0.5);
@

\begin{lstlisting}
par(mar=c(2, 2, 2, 2));

plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

circos(R=400, cir="hg18", W=4,   type="chr", print.chr.lab=TRUE, scale=TRUE);
circos(R=300, cir="hg18", W=100, mapping=gene.exp,  col.v=4,  type="heatmap2", 
       cluster=TRUE, col.bar=TRUE, lwd=0.1, col="blue");
circos(R=220, cir="hg18", W=80,  mapping=cnv,   col.v=4,   type="ml3", B=FALSE, lwd=1, cutoff=0);
circos(R=140, cir="hg18", W=80,  mapping=pvalue,  col.v=4,    type="l",   B=TRUE, lwd=1, col=colors[1]);

cols        <- rep(colors[7], nrow(TCGA.BC.fus));
col.i       <- which(TCGA.BC.fus[,1]==TCGA.BC.fus[,4]);
cols[col.i] <- colors[1];
circos(R=130, cir="hg18", W=10,  mapping=TCGA.BC.fus, type="link2", lwd=2, col=cols);

@
\end{lstlisting}

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette7, echo=FALSE, fig=TRUE, width=8, height=8>>=
par(mar=c(0, 0, 0, 0));

plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

circos(R=400, cir="hg18", W=4,   type="chr", print.chr.lab=TRUE, scale=TRUE);
circos(R=300, cir="hg18", W=100, mapping=gene.exp,  col.v=8,  type="heatmap2", 
       cluster=TRUE, col.bar=TRUE, lwd=0.1, col="blue");
circos(R=220, cir="hg18", W=80,  mapping=cnv,   col.v=4,   type="ml3", B=FALSE, lwd=1, cutoff=0);
circos(R=140, cir="hg18", W=80,  mapping=pvalue,  col.v=4,    type="l",   B=TRUE, lwd=1, col=colors[1]);

cols        <- rep(colors[7], nrow(TCGA.BC.fus));
col.i       <- which(TCGA.BC.fus[,1]==TCGA.BC.fus[,4]);
cols[col.i] <- colors[1];
circos(R=130, cir="hg18", W=10,  mapping=TCGA.BC.fus, type="link2", lwd=2, col=cols);

@
\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette7}
\end{figure}
 
Figure 7: An example of a circular plots generated by OmicCircos showing the expression, CNV and fusion protein in 15 Her2 subtype samples from TCGA Breast Cancer data.
Circular tracks from outside to inside: genome positions by chromosomes (black lines are cytobands); expression heatmap (red: up-regulated; blue: down-regulated); CNVs (red: gain; blue: loss); 
correlation p values between expression and CNVs; fusion genes.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 8  (vignette7)
%\clearpage
\subsection{traditional plotting and OmicCircos}

\begin{lstlisting}
options(stringsAsFactors = FALSE);
library(OmicCircos);

data("TCGA.BC.fus");
data("TCGA.BC.cnv.2k.60");
data("TCGA.BC.gene.exp.2k.60");
data("TCGA.BC.sample60");

## gene expression data for PCA
exp.m   <- TCGA.BC.gene.exp.2k.60[,c(4:ncol(TCGA.BC.gene.exp.2k.60))];
cnv     <- TCGA.BC.cnv.2k.60;
type.n  <- unique(TCGA.BC.sample60[,2]);
colors  <- rainbow(length(type.n), alpha=0.5);

## sub-type colors
pca.col <- rep(NA, nrow(TCGA.BC.sample60));
for (i in 1:length(type.n)){
  n   <- type.n[i];
  n.i <- which(TCGA.BC.sample60[,2] == n);
  n.n <- TCGA.BC.sample60[n.i,1];
  g.i <- which(colnames(exp.m) %in% n.n);
  pca.col[g.i] <- colors[i];
}

## run PCA
exp.m   <- na.omit(exp.m);
pca.out <- prcomp(t(exp.m), scale = TRUE);

## subtype cnv
cnv.i <- c();
for (i in 1:length(type.n)){
  n     <- type.n[i];
  n.i   <- which(TCGA.BC.sample60[,2] == n);
  n.n   <- TCGA.BC.sample60[n.i,1];
  cnv.i <- which(colnames(cnv) %in% n.n);
}
\end{lstlisting}

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);

data("TCGA.BC.fus");
data("TCGA.BC.cnv.2k.60");
data("TCGA.BC.gene.exp.2k.60");
data("TCGA.BC.sample60");

## gene expression data for PCA
exp.m   <- TCGA.BC.gene.exp.2k.60[,c(4:ncol(TCGA.BC.gene.exp.2k.60))];
cnv     <- TCGA.BC.cnv.2k.60;
type.n  <- unique(TCGA.BC.sample60[,2]);
colors  <- rainbow(length(type.n), alpha=0.5);

## sub-type colors
pca.col <- rep(NA, nrow(TCGA.BC.sample60));
for (i in 1:length(type.n)){
  n   <- type.n[i];
  n.i <- which(TCGA.BC.sample60[,2] == n);
  n.n <- TCGA.BC.sample60[n.i,1];
  g.i <- which(colnames(exp.m) %in% n.n);
  pca.col[g.i] <- colors[i];
}

## run PCA
exp.m   <- na.omit(exp.m);
pca.out <- prcomp(t(exp.m), scale = TRUE);

## subtype cnv
cnv.i <- c();
for (i in 1:length(type.n)){
  n     <- type.n[i];
  n.i   <- which(TCGA.BC.sample60[,2] == n);
  n.n   <- TCGA.BC.sample60[n.i,1];
  cnv.i <- which(colnames(cnv) %in% n.n);
}
@

\begin{lstlisting}
## PCA is plotting. 
plot(pca.out$x[,1]*5, pca.out$x[,2]*5, pch=19, col=pca.col, main="",  
     cex=2, xlab="PC1", ylab="PC2", ylim=c(-200, 460), xlim=c(-200,460));
legend(200,0, c("Basal","Her2","LumA","LumB"), pch=19, col=colors[c(2,4,1,3)], cex=1, 
     title ="Gene Expression (PCA)", box.col="white");

## It is going to plot the circos.
circos(xc=280, yc=280, R=168, cir="hg18", W=4,     type="chr", print.chr.lab=TRUE);
R.v <- 135;
for (i in 1:length(type.n)){
  n     <- type.n[i];
  n.i   <- which(TCGA.BC.sample60[,2] == n);
  n.n   <- TCGA.BC.sample60[n.i,1];
  cnv.i <- which(colnames(cnv) %in% n.n);
  cnv.v <- cnv[,cnv.i];
  cnv.v[cnv.v > 2]  <- 2;
  cnv.v[cnv.v < -2] <- -2;
  cnv.m <- cbind(cnv[,c(1:3)], cnv.v);
  circos(xc=280, yc=280, R=R.v, cir="hg18", W=34, mapping=cnv.m, col.v=4,  type="ml3", B=FALSE, lwd=0.5, cutoff=0);
  R.v <- R.v - 25;
}

legend(-80,460, c("1 Basal", "2 Her2", "3 LumA", "4 LumB", "(center)"), cex=1, 
     title ="CNV (OmicCircos)", box.col="white");
\end{lstlisting}

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette8, echo=FALSE, fig=TRUE, width=8, height=8>>=

## PCA is plotting. 
plot(pca.out$x[,1]*5, pca.out$x[,2]*5, pch=19, col=pca.col, main="",  
     cex=2, xlab="PC1", ylab="PC2", ylim=c(-200, 460), xlim=c(-200,460));
legend(200,0, c("Basal","Her2","LumA","LumB"), pch=19, col=colors[c(2,4,1,3)], cex=1, 
     title ="Gene Expression (PCA)", box.col="white");

## It is going to plot the circos.
circos(xc=280, yc=280, R=168, cir="hg18", W=4,     type="chr", print.chr.lab=TRUE);
R.v <- 135;
for (i in 1:length(type.n)){
  n     <- type.n[i];
  n.i   <- which(TCGA.BC.sample60[,2] == n);
  n.n   <- TCGA.BC.sample60[n.i,1];
  cnv.i <- which(colnames(cnv) %in% n.n);
  cnv.v <- cnv[,cnv.i];
  cnv.v[cnv.v > 2]  <- 2;
  cnv.v[cnv.v < -2] <- -2;
  cnv.m <- cbind(cnv[,c(1:3)], cnv.v);
  the.v <- ncol(cnv.m) - 8;
  if (the.v < 4){
    the.v <- 4;
  }
  circos(xc=280, yc=280, R=R.v, cir="hg18", W=34, mapping=cnv.m, col.v=the.v,  type="ml3", B=FALSE, lwd=0.5, cutoff=0);
  R.v <- R.v - 25;
}

legend(-80,460, c("1 Basal", "2 Her2", "3 LumA", "4 LumB", "(center)"), cex=1, 
     title ="CNV (OmicCircos)", box.col="white");
@
\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette8}
\end{figure}

Figure 8: Integration of circular plots by OmicCircos and PCA from R for 
TCGA breast cancer data. Circular tracks from outside to inside: genome positions 
by chromosomes (black lines are cytobands); CNVs in Basal, Her2, Luminal A (LumA) 
and Luminal B (LumB) subtypes with 15 samples in each track (red: gain; blue: loss); 
At the bottom left corner, four clusters were generated by the from principle component analysis (PCA) of 
the gene expression data using R packages in the bioconductor.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 9  (vignette8)

\begin{lstlisting}
plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

legend(680,800, c("Basal","Her2","LumA","LumB"), pch=19, col=colors[c(2,4,1,3)], cex=0.5, 
     title ="Gene Expression (PCA)", box.col="white");

legend(5,800, c("1 Basal", "2 Her2", "3 LumA", "4 LumB", "(center)"), cex=0.5, 
     title ="CNV (OmicCircos)", box.col="white");

circos(xc=400, yc=400, R=390, cir="hg18", W=4,  type="chr", print.chr.lab=TRUE, scale=TRUE);
R.v <- 330;
for (i in 1:length(type.n)){
  n     <- type.n[i];
  n.i   <- which(TCGA.BC.sample60[,2] == n);
  n.n   <- TCGA.BC.sample60[n.i,1];
  cnv.i <- which(colnames(cnv) %in% n.n);
  cnv.v <- cnv[,cnv.i];
  cnv.v[cnv.v > 2]  <- 2;
  cnv.v[cnv.v < -2] <- -2;
  cnv.m <- cbind(cnv[,c(1:3)], cnv.v);
  circos(xc=400, yc=400, R=R.v, cir="hg18", W=60, mapping=cnv.m, col.v=4,  type="ml3", B=FALSE, lwd=1, cutoff=0, scale=TRUE);
  R.v <- R.v - 60;
}

points(pca.out$x[,1]*3.6+400, pca.out$x[,2]*3.6+400, pch=19, col=pca.col, cex=2);

\end{lstlisting}

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette9, echo=FALSE, fig=TRUE, width=8, height=8>>=

plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

legend(680,800, c("Basal","Her2","LumA","LumB"), pch=19, col=colors[c(2,4,1,3)], cex=0.5, 
     title ="Gene Expression (PCA)", box.col="white");

legend(5,800, c("1 Basal", "2 Her2", "3 LumA", "4 LumB", "(center)"), cex=0.5, 
     title ="CNV (OmicCircos)", box.col="white");

circos(xc=400, yc=400, R=390, cir="hg18", W=4,  type="chr", print.chr.lab=TRUE, scale=TRUE);
R.v <- 330;
for (i in 1:length(type.n)){
  n     <- type.n[i];
  n.i   <- which(TCGA.BC.sample60[,2] == n);
  n.n   <- TCGA.BC.sample60[n.i,1];
  cnv.i <- which(colnames(cnv) %in% n.n);
  cnv.v <- cnv[,cnv.i];
  cnv.v[cnv.v > 2]  <- 2;
  cnv.v[cnv.v < -2] <- -2;
  cnv.m <- cbind(cnv[,c(1:3)], cnv.v);
  the.v <- ncol(cnv.m) - 8;
  if (the.v < 4){
    the.v <- 4;
  }
  circos(xc=400, yc=400, R=R.v, cir="hg18", W=60, mapping=cnv.m, col.v=the.v,  type="ml3", B=FALSE, lwd=1, cutoff=0, scale=TRUE);
  R.v <- R.v - 60;
}

points(pca.out$x[,1]*3.6+400, pca.out$x[,2]*3.6+400, pch=19, col=pca.col, cex=2);

@
\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette9}
\end{figure}

Figure 9 is an example showing the PCA plot is localed at the center of the circos plot. Integration of circular plots by OmicCircos and PCA from R of 
TCGA breast cancer data. Circular tracks from outside to inside: genome positions 
by chromosomes (black lines are cytobands); CNVs in Basal, Her2, Luminal A (LumA) 
and Luminal B (LumB) subtypes with 15 samples in each track (red: gain; blue: loss); 
In the center, four clusters from principle component analysis (PCA) of 
the gene expression data with R packages in the bioconductor.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%fig 10
%\clearpage
\subsection{zoom}

\begin{lstlisting}
options(stringsAsFactors = FALSE);
library(OmicCircos);

data("TCGA.PAM50_genefu_hg18");
data("TCGA.BC.fus");
data("TCGA.BC.cnv.2k.60");
data("TCGA.BC.gene.exp.2k.60");
data("TCGA.BC.sample60");
data("TCGA.BC_Her2_cnv_exp");
data("TCGA.PAM50_genefu_hg18");

pvalue <- -1 * log10(TCGA.BC_Her2_cnv_exp[,5]);
pvalue <- cbind(TCGA.BC_Her2_cnv_exp[,c(1:3)], pvalue);

Her2.i <- which(TCGA.BC.sample60[,2] == "Her2");
Her2.n <- TCGA.BC.sample60[Her2.i,1];

Her2.j <- which(colnames(TCGA.BC.cnv.2k.60) %in% Her2.n);
cnv    <- TCGA.BC.cnv.2k.60[,c(1:3,Her2.j)]; 
cnv.m  <- cnv[,c(4:ncol(cnv))];
cnv.m[cnv.m >  2] <- 2;
cnv.m[cnv.m < -2] <- -2;
cnv <- cbind(cnv[,1:3], cnv.m);

gene.exp <- TCGA.BC.gene.exp.2k.60[,c(1:3,Her2.j)]; 

colors <- rainbow(10, alpha=0.5);
\end{lstlisting}

<<echo=FALSE>>=
options(stringsAsFactors = FALSE);
library(OmicCircos);

data("TCGA.PAM50_genefu_hg18");
data("TCGA.BC.fus");
data("TCGA.BC.cnv.2k.60");
data("TCGA.BC.gene.exp.2k.60");
data("TCGA.BC.sample60");
data("TCGA.BC_Her2_cnv_exp");
data("TCGA.PAM50_genefu_hg18");

pvalue <- -1 * log10(TCGA.BC_Her2_cnv_exp[,5]);
pvalue <- cbind(TCGA.BC_Her2_cnv_exp[,c(1:3)], pvalue);

Her2.i <- which(TCGA.BC.sample60[,2] == "Her2");
Her2.n <- TCGA.BC.sample60[Her2.i,1];

Her2.j <- which(colnames(TCGA.BC.cnv.2k.60) %in% Her2.n);
cnv    <- TCGA.BC.cnv.2k.60[,c(1:3,Her2.j)]; 
cnv.m  <- cnv[,c(4:ncol(cnv))];
cnv.m[cnv.m >  2] <- 2;
cnv.m[cnv.m < -2] <- -2;
cnv <- cbind(cnv[,1:3], cnv.m);

gene.exp <- TCGA.BC.gene.exp.2k.60[,c(1:3,Her2.j)]; 

colors <- rainbow(10, alpha=0.5);
@

\begin{lstlisting}
par(mar=c(2, 2, 2, 2));

plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");
# In figure 7, the chromosome 1 to chromosome 22 are going to be plotted from the angle 0 (12 Oclock) 
# to 180 degree (6 Oclock). 
zoom <- c(1, 22, 939245.5, 154143883, 0, 180);
circos(R=400, cir="hg18", W=4,   type="chr", print.chr.lab=TRUE, scale=TRUE, zoom=zoom);
circos(R=300, cir="hg18", W=100, mapping=gene.exp, col.v=4,  type="heatmap2", cluster=TRUE, col.bar=TRUE, col.bar.po = "bottomright", lwd=0.01, zoom=zoom);
circos(R=220, cir="hg18", W=80,  mapping=cnv,      col.v=4,  type="ml3", B=FALSE, lwd=1, cutoff=0, zoom=zoom);
circos(R=140, cir="hg18", W=80,  mapping=pvalue,   col.v=4,  type="l",   B=TRUE, lwd=1, col=colors[1], zoom=zoom);
circos(R=130, cir="hg18", W=10,  mapping=TCGA.BC.fus, type="link", lwd=2, zoom=zoom);

# zoom in links by using the hightlight functions 
# highlight
the.col1=rainbow(10, alpha=0.5)[1];

# The highline region is radium from 140 to 400 and from position 282412.5 to 133770314.5 in chromosome 11.
highlight <- c(140, 400, 11, 282412.5, 11, 133770314.5, the.col1, the.col1);
circos(R=110, cir="hg18", W=40, mapping=highlight, type="hl", lwd=2, zoom=zoom);
the.col2=rainbow(10, alpha=0.5)[6];
highlight <- c(140, 400, 17, 739525, 17, 78385909, the.col2, the.col2);
circos(R=110, cir="hg18", W=40, mapping=highlight, type="hl", lwd=2, zoom=zoom);
## highlight link
highlight.link1 <- c(400, 400, 140, 376.8544, 384.0021, 450, 540.5);
circos(cir="hg18", mapping=highlight.link1, type="highlight.link", col=the.col1, lwd=1);
highlight.link2 <- c(400, 400, 140, 419.1154, 423.3032, 543, 627);
circos(cir="hg18", mapping=highlight.link2, type="highlight.link", col=the.col2, lwd=1);

# The chromosome 11 region is going plotting from 180 (6 Oclock) to 270 degree (9 Oclock).
zoom <- c(11, 11, 282412.5, 133770314.5, 180, 270);
circos(R=400, cir="hg18", W=4,   type="chr", print.chr.lab=TRUE, scale=FALSE, zoom=zoom);
circos(R=300, cir="hg18", W=100, mapping=gene.exp, col.v=4,  type="heatmap2", cluster=TRUE, lwd=0.01, zoom=zoom);
circos(R=220, cir="hg18", W=80,  mapping=cnv,      col.v=4,  type="ml3", B=FALSE, lwd=1, cutoff=0, zoom=zoom);
circos(R=140, cir="hg18", W=80,  mapping=pvalue,   col.v=4,  type="l",   B=TRUE, lwd=1, col=colors[1], zoom=zoom);

# The chromosome 17 region is going plotting from 180 (6 Oclock) to 270 degree (9 Oclock).

gene.names <- c("ERBB2","CDC6");
PAM50.17   <- which(TCGA.PAM50_genefu_hg18[,3]==gene.names);
TCGA.PAM50 <- TCGA.PAM50_genefu_hg18[PAM50.17,];

# zoom in chromosome 17
zoom <- c(17, 17, 739525, 78385909, 274, 356);
circos(R=400, cir="hg18", W=4,   type="chr", print.chr.lab=TRUE, scale=TRUE, zoom=zoom);
circos(R=300, cir="hg18", W=100, mapping=gene.exp, col.v=4,  type="heatmap2", cluster=TRUE, lwd=0.01, zoom=zoom);
circos(R=220, cir="hg18", W=80,  mapping=cnv,      col.v=4,  type="ml3", B=FALSE, lwd=1, cutoff=0, zoom=zoom);
circos(R=140, cir="hg18", W=80,  mapping=pvalue,   col.v=4,  type="l",   B=TRUE, lwd=1, col=colors[1], zoom=zoom);
circos(R=410, cir="hg18", W=40,  mapping=TCGA.PAM50, type="label", side="out", col="blue", zoom=zoom);

\end{lstlisting}

\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htbp]
\begin{center}
<<OmicCircos4vignette10, echo=FALSE, fig=TRUE, width=8, height=8>>=
par(mar=c(0, 0, 0, 0));

plot(c(1,800), c(1,800), type="n", axes=FALSE, xlab="", ylab="", main="");

zoom <- c(1, 22, 939245.5, 154143883, 0, 180);
circos(R=400, cir="hg18", W=4,   type="chr", print.chr.lab=TRUE, scale=TRUE, zoom=zoom);
circos(R=300, cir="hg18", W=100, mapping=gene.exp, col.v=8,  type="heatmap2", cluster=TRUE, col.bar=TRUE, col.bar.po = "bottomright", lwd=0.01, zoom=zoom);
circos(R=220, cir="hg18", W=80,  mapping=cnv,      col.v=4,  type="ml3", B=FALSE, lwd=1, cutoff=0, zoom=zoom);
circos(R=140, cir="hg18", W=80,  mapping=pvalue,   col.v=4,  type="l",   B=TRUE, lwd=1, col=colors[1], zoom=zoom);
circos(R=130, cir="hg18", W=10,  mapping=TCGA.BC.fus, type="link", lwd=2, zoom=zoom);

the.col1=rainbow(10, alpha=0.5)[1];

highlight <- c(140, 400, 11, 282412.5, 11, 133770314.5, the.col1, the.col1);
circos(R=110, cir="hg18", W=40, mapping=highlight, type="hl", lwd=2, zoom=zoom);
the.col2=rainbow(10, alpha=0.5)[6];
highlight <- c(140, 400, 17, 739525, 17, 78385909, the.col2, the.col2);
circos(R=110, cir="hg18", W=40, mapping=highlight, type="hl", lwd=2, zoom=zoom);

highlight.link1 <- c(400, 400, 140, 376.8544, 384.0021, 450, 540.5);
circos(cir="hg18", mapping=highlight.link1, type="highlight.link", col=the.col1, lwd=1);
highlight.link2 <- c(400, 400, 140, 419.1154, 423.3032, 543, 627);
circos(cir="hg18", mapping=highlight.link2, type="highlight.link", col=the.col2, lwd=1);

zoom <- c(11, 11, 282412.5, 133770314.5, 180, 270);
circos(R=400, cir="hg18", W=4,   type="chr", print.chr.lab=TRUE, scale=TRUE, zoom=zoom);
circos(R=300, cir="hg18", W=100, mapping=gene.exp, col.v=8,  type="heatmap2", cluster=TRUE, lwd=0.01, zoom=zoom);
circos(R=220, cir="hg18", W=80,  mapping=cnv,      col.v=4,  type="ml3", B=FALSE, lwd=1, cutoff=0, zoom=zoom);
circos(R=140, cir="hg18", W=80,  mapping=pvalue,   col.v=4,  type="l",   B=TRUE, lwd=1, col=colors[1], zoom=zoom);

gene.names <- c("ERBB2","CDC6");
PAM50.17   <- which(TCGA.PAM50_genefu_hg18[,3]==gene.names);
TCGA.PAM50 <- TCGA.PAM50_genefu_hg18[PAM50.17,];

zoom <- c(17, 17, 739525, 78385909, 274, 356);
circos(R=400, cir="hg18", W=4,   type="chr", print.chr.lab=TRUE, scale=TRUE, zoom=zoom);
circos(R=300, cir="hg18", W=100, mapping=gene.exp, col.v=8,  type="heatmap2", cluster=TRUE, lwd=0.01, zoom=zoom);
circos(R=220, cir="hg18", W=80,  mapping=cnv,      col.v=4,  type="ml3", B=FALSE, lwd=1, cutoff=0, zoom=zoom);
circos(R=140, cir="hg18", W=80,  mapping=pvalue,   col.v=4,  type="l",   B=TRUE, lwd=1, col=colors[1], zoom=zoom);
circos(R=410, cir="hg18", W=40,  mapping=TCGA.PAM50, type="label", side="out", col="blue", zoom=zoom);
@

\end{center}
\captionsetup{width=1\textwidth}
\caption{}
\label{fig:OmicCircos4vignette10}
\end{figure}

Figure 10: Circular plots by OmicCircos of expression, CNV, and fusion proteins in 15 Her2 subtype samples from TCGA breast cancer data. Circular tracks from outside to inside: genome positions by chromosomes (black lines are cytobands), expression heatmap of 500 most variable genes whose genomic
coordinates are indicated by the blue connectors, CNVs (red: gain, blue: loss), correlation p-values between expression and CNV, fusion genes (intra/inter chromosomes: red/blue). Chromosomes 1–22 are shown in the right half of the circle. Zoomed chromosomes 11 and 17 are displayed in the left half. The circle arc was drawn using trigonometric functions. The fusion protein links were plotted with the Bézier curve algorithm.

\end{document}