#    Author: Benjamin Tovar
#    Date: 13/April/2012
#
#    Example of a Markov Chain of zero order (the current nucleotide is
#    totally independent of the previous nucleotide).

#    The multinomial model is:
#    p(A)+p(C)+p(G)+p(T) = 1.0
#    0.4 +0.1 +0.1 +0.4  = 1.0

# Define the DNA alphabet that will be used to put names to the objects
alp <- c("A","C","G","T")
# Create the vector that represents the probability distribution of the model
zeroOrder <- c(0.4,0.1,0.1,0.4)
# Put the name of reference of each base 
names(zeroOrder) <- alp 
# Create a sequence of 1000 bases using this model.
zeroOrderSeq <- sample(alp,1000,rep=T,prob=zeroOrder)

# ***** Study the composition bias of the sequence *****
# We wil use the "seqinr" package.
# For the installation of the package, type:
# install.packages("seqinr")
# Then, load the package:
require("seqinr")

# Count the frequency of each base 
# in the sequence using the "count" function
zeroOrderFreq <- count(zeroOrderSeq,1,alphabet=alp,freq=TRUE)

# Count the frequency of dinucleotides 
# in the sequence using the "count" function
zeroOrderFreqDin <- count(zeroOrderSeq,2,alphabet=alp,freq=TRUE) 
 
# Now, plot the results in the same plot:
layout(1:2)
barplot(zeroOrderFreq,col=1:4,main="Compositional bias of each nucleotide",
        xlab="Base",
        ylab="Base proportion")
barplot(zeroOrderFreqDin,col=rainbow(16),
        main="Compositional bias of each dinucleotide",
        xlab="Base",
        ylab="Base proportion")

# &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&

#    Example of a Markov Chain of first order (the current nucleotide only
#    depends on the previous nucleotide).

#    The multinomial model per base is:
#    p(A|A)+p(C|A)+p(G|A)+p(T|A) = 1.0
#    p(A|C)+p(C|C)+p(G|C)+p(T|C) = 1.0
#    p(A|G)+p(C|G)+p(G|G)+p(T|G) = 1.0
#    p(A|T)+p(C|T)+p(G|T)+p(T|T) = 1.0        

#    So:
#    0.6 + 0.1 + 0.1 + 0.2  = 1.0
#    0.1 + 0.5 + 0.3 + 0.1  = 1.0
#    0.05+ 0.2 + 0.7 + 0.05 = 1.0
#    0.4 + 0.05+0.05 + 0.5  = 1.0
    
    
# Create the matrix that will store the probability distribution given 
# a certain nucleotide:
firstOrderMat <- matrix(NA,nr=4,nc=4)
# Put names to the 2 dimensions of the matrix 
colnames(firstOrderMat) <- rownames(firstOrderMat) <- alp 
# Add the probability distribution per base:
firstOrderMat[1,] <- c(0.6,0.1,0.1,0.2)  # Given an A in the 1st position
firstOrderMat[2,] <- c(0.1,0.5,0.3,0.1)  # Given a  C in the 1st position
firstOrderMat[3,] <- c(0.05,0.2,0.7,0.05)# Given a  G in the 1st position
firstOrderMat[4,] <- c(0.4,0.05,0.05,0.5)# Given a  T in the 1st position

# Now we got a matrix
#    > firstOrderMat
#         A    C    G    T
#    A 0.60 0.10 0.10 0.20
#    C 0.10 0.50 0.30 0.10
#    G 0.05 0.20 0.70 0.05
#    T 0.40 0.05 0.05 0.50

# In order to continue, we need an initial probability distribution to know
# which base is the most probable to start up the sequence.
inProb <- c(0.4,0.1,0.1,0.4); names(inProb) <- alp
# So, the sequence will have a 40% to start with an A or a T and 10% with C or G

# Create a function to generate the sequence.
# NOTE: To load the function to the current environment, just copy
# the entire function and paste it inside the R prompt.

generateFirstOrderSeq <- function(lengthSeq,
                                  alphabet,  
                                  initialProb,
                                  firstOrderMatrix){
#    lengthSeq = length of the sequence
#    alphabet = alphabet that compounds the sequence 
#    initialProb   = initial probability distribution
#    firstOrderMatrix = matrix that stores the probability distribution of a 
#                       first order Markov Chain

    # Construct the object that stores the sequence
    outputSeq <- rep(NA,lengthSeq)
    # Which is the first base:
    outputSeq[1]  <- sample(alphabet,1,prob=initialProb) 
    # Let the computer decide:
    for(i in 2:length(outputSeq)){
        prevNuc <- outputSeq[i-1]    
        currentProb <- firstOrderMatrix[prevNuc,]
        outputSeq[i] <- sample(alp,1,prob=currentProb)
    } 
    cat("** DONE: Sequence computation is complete **\n")
    return(outputSeq)
}

# Use the generateFirstOrderSeq function to generate a sequence of 1000 bases 
# long
firstOrderSeq <- generateFirstOrderSeq(1000,alp,inProb,firstOrderMat)

# ***** Study the composition bias of the sequence *****
# We wil use the "seqinr" package.
# For the installation of the package, type:
# install.packages("seqinr")
# Then, load the package:
require("seqinr")

# Count the frequency of each base 
# in the sequence using the "count" function
firstOrderFreq <- count(firstOrderSeq,1,alphabet=alp,freq=TRUE)

# Count the frequency of dinucleotides 
# in the sequence using the "count" function
firstOrderFreqDin <- count(firstOrderSeq,2,alphabet=alp,freq=TRUE) 
 
# Now, plot the results in the same plot:
layout(1:2)
barplot(firstOrderFreq,col=1:4,main="Compositional bias of each nucleotide",
        xlab="Base",
        ylab="Base proportion")
barplot(firstOrderFreqDin,col=rainbow(16),
        main="Compositional bias of each dinucleotide",
        xlab="Base",
        ylab="Base proportion")
        
## Lets plot the 4 plots in one window
    layout(matrix(1:4,nr=2,nc=2))
    # Results from the zero order 
    barplot(zeroOrderFreq,col=1:4,
            main="Compositional bias of each nucleotide\nZero Order Markov Chain",
            xlab="Base",
            ylab="Base proportion")
    barplot(zeroOrderFreqDin,col=rainbow(16),
            main="Compositional bias of each dinucleotide\nZero Order Markov Chain",
            xlab="Base",
            ylab="Base proportion")
    # Results from the first order 
    barplot(firstOrderFreq,col=1:4,
            main="Compositional bias of each nucleotide\nFirst Order Markov Chain",
            xlab="Base",
            ylab="Base proportion")
    barplot(firstOrderFreqDin,col=rainbow(16),
            main="Compositional bias of each dinucleotide\nFirst Order Markov Chain",
            xlab="Base",
            ylab="Base proportion")
        
        
# end.