#########################################################
## Plots Demo                                          ## 
## Author: Han-Ming Wu (hmwu@stat.sinica.edu.tw)       ## 
## Institute of Statistical Science, Academia Sinica   ## 
## http://www.sinica.edu.tw/~hmwu/                     ## 
## 2006/07/25                                          ## 
#########################################################


## Read Data
setwd("C:\\Program Files\\R\\rw2001\\WorkingData")
library(stats)

## in this test data, rows are samples, and columns are genes
## use command t(testdata) to transpose data if necessary

test.matrix  <- read.table("testdata.txt", header=TRUE)
n <- dim(test.matrix)[1]
p <- dim(test.matrix)[2]-2

test.data <- test.matrix[,3:(p+2)]
name <- test.matrix[,1]

## group can be obtained by kmeans, hierarchical clustering 
## and any other clustering algorithms
group <- test.matrix[,2]
groupID <- unique(group)
no.group <- length(groupID)
groupID.name <- c("group1", "group2", "group3", "group4", "group5", "group6")


## standardized data
test.sdata <- (test.data-apply(test.data, 1, mean))/sqrt(apply(test.data, 1, var)) 

#Histogram for one selected gene
hist(test.sdata[,1], br=12, col="lightblue", border="pink", labels = TRUE, main="Histogram", xlab="log ratio")

#help Histogram
?hist

#Boxplot
boxplot(test.sdata, main="Boxplot for each gene")
boxplot(test.sdata[,1]~group, names=groupID.name, main="Boxplot for groups of first gene")

#Scatterplot matrix
pairs(test.sdata, col=group, main="scatterplot matrix")
pairs(test.sdata[,1:3], col=group, main="scatterplot matrix")

## Data Image
test.image <- as.matrix(t(test.sdata[n:1,]))
RGcol <- maPalette(low = "green", high = "red", k = 50)
image(test.image, xlab="Genes", ylab="Samples", col = RGcol)

## Time Series Plots
number <- 1:n
par(mfrow=c(3,2))
for(i in 0:(no.group-1)){
    ts.plot(t(test.sdata[number[group==i],]), xlab=groupID.name[i+1])
}


### Name: maPalette
### Title: Microarray color palette
### Aliases: maPalette
### Keywords: color
### Author: Sandrine Dudoit <sandrine@stat.berkeley.edu>, Yee Hwa (Jean)
        Yang <yeehwa@stat.berkeley.edu>

maPalette <- function(low = "white",
                      high = c("green", "red"),
                      mid=NULL,
                      k =50)
{
    low <- col2rgb(low)/255
    high <- col2rgb(high)/255

    if(is.null(mid)){
        r <- seq(low[1], high[1], len = k)
        g <- seq(low[2], high[2], len = k)
        b <- seq(low[3], high[3], len = k)
      }
    if(!is.null(mid)){
      k2 <- round(k/2)
      mid <- col2rgb(mid)/255
      r <- c(seq(low[1], mid[1], len = k2),
             seq(mid[1], high[1], len = k2))
      g <- c(seq(low[2], mid[2], len = k2),
             seq(mid[2], high[2], len = k2))
      b <- c(seq(low[3], mid[3], len = k2),
             seq(mid[3], high[3], len = k2))
    }
    rgb(r, g, b)
}