Finalizing dinucleotide CpG island counts with sliding window

.Rhistory

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+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+#g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.breaks <- g.hist$breaks # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+alpha.LM
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
+plot(yeast)
+hist(yeast)
+hist(g.vec)
+g.pois
+g.mean
+alpha.LM
+alpha.ML
+degree(g)
+sort(degree(g))
+sort(degree(g),decreasing=FALSE)
+sort(degree(g),decreasing=F)
+sort(degree(g),decreasing=false)
+sort(degree(g), decreasing = TRUE)
+head(sort(degree(g), decreasing = TRUE))
+stddev(degree(g))
+sd(degree(g))
+tail(sort(degree(g), decreasing = TRUE))
+plot(log(g.breaks.clean), log(g.probs.clean))
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+#### Part B: Loop Recursion Warmup
+m <- 3 # row edges
+n <- 6 # col edges
+path_matrix <- matrix(1, nrow=m+1, ncol=n+1)
+for (i in seq(2,m+1)){
+for (j in seq(2, n+1)){
+path_matrix[i,j] <- path_matrix[i-1,j] + path_matrix[i,j-1]
+}
+}
+path_matrix
+path_matrix[m][n]
+path_matrix[m]
+path_matrix[n]
+path_matrix[m,n]
+path_matrix[m+1,n+1]
+calc.num.paths <- function(n,m){
+path_matrix <- matrix(1, nrow=m+1, ncol=n+1)
+for (i in seq(2,m+1)){
+for (j in seq(2, n+1)){
+path_matrix[i,j] <- path_matrix[i-1,j] + path_matrix[i,j-1]
+}
+}
+path_matrix[m+1,n+1]
+}
+#### Part B: Loop Recursion Warmup
+calc.num.paths <- function(n,m){
+path_matrix <- matrix(1, nrow=m+1, ncol=n+1)
+for (i in seq(2,m+1)){
+for (j in seq(2, n+1)){
+path_matrix[i,j] <- path_matrix[i-1,j] + path_matrix[i,j-1]
+}
+}
+path_matrix[m+1,n+1]
+}
+m <- 5 # row edges
+n <- 5 # col edges
+calc.num.paths(n,m)
+m <- 5 # row edges
+n <- 6 # col edges
+calc.num.paths(n,m)
+m <- 10 # row edges
+n <- 10 # col edges
+calc.num.paths(n,m)
+factorial(n+m)/(factorial(n)*factorial(m))
+m <- 5 # row edges
+n <- 5 # col edges
+calc.num.paths(n,m)
+factorial(n+m)/(factorial(n)*factorial(m))
+m <- 5 # row edges
+n <- 6 # col edges
+calc.num.paths(n,m)
+factorial(n+m)/(factorial(n)*factorial(m))
+m <- 10 # row edges
+n <- 10 # col edges
+calc.num.paths(n,m)
+factorial(n+m)/(factorial(n)*factorial(m))
+h1n1.Cali
+h1n1.Cali.dna.vec <- fasta2vec("FJ969540.1.fasta")
+#### Part A: EMBOSS pairwise alignment server and influenza
+## Load associated supportive libraries
+if (!require("seqinr")) install.packages("seqinr")
+library(seqinr)
+## Load in the fasta file
+fasta2vec <- function(fasta.file){
+if (!require("seqinr")) install.packages("seqinr")
+library(seqinr)
+fasta <- read.fasta(file=fasta.file, as.string= TRUE)
+fasta.string <- fasta[[1]][1]
+fasta.list <- strsplit(fasta.string,"")
+fasta.vec <- unlist(fasta.list)
+}
+h1n1.Cali.dna.vec <- fasta2vec("FJ969540.1.fasta")
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+h1n1.Cali.dna.vec <- fasta2vec("FJ969540.1.fasta")
+h1n1.Cali.dna.vec[1:5]
+?count
+count(h1n1.Cali.dna.vec[1:5],2)
+count(h1n1.Cali.dna.vec[1:5],2)["aa"]
+count(h1n1.Cali.dna.vec[1:5],11)
+count(h1n1.Cali.dna.vec[1:5],1)
+#### Part C: Dinucleotide Signals
+calc.sliding.cpg <- function(fastaVec, slideWin){
+# allocate memory for odds ratio output
+cg_dinuc_oddsRatio <- double()
+n<-length(fastaVec)
+for (i in 1:(n-slideWin)){
+# array slice the vectpr on sliding window size and obtain dinuc appearances
+dinucs<-count(fastaVec[i:(i+slideWin)],2)
+# retrieve number of times "CG" appeared
+cg_dinuc_count <- dinucs["cg"]
+# get counts of all nucleotides
+nucs<-count(fastaVec[i:(i+slideWin)],1)
+# obtain times dinuc pairing appeared per times the indiv nucs appeared
+cg_dinuc_oddsRatio[i] <-
+cg_dinuc_count/(nucs["c"]*nucs["g"])
+}
+return(cg_dinuc_oddsRatio) # returns vector
+}
+apoe.fasta.vec <- fasta2vec("apoe.fasta")
+cpg_vec <- calc.sliding.cpg(apoe.fasta.vec,150)
+plot(cpg_vec,type="l",main="Observed vs Expected CG",xlab="Base Index", ylab="Obs/Exp")
+cpg_vec

Schrick-Noah_CS-6643_Lab-8.R

@@ -75,4 +75,25 @@ n <- 10 # col edges
 calc.num.paths(n,m)
 factorial(n+m)/(factorial(n)*factorial(m))
 
+#### Part C: Dinucleotide Signals
+calc.sliding.cpg <- function(fastaVec, slideWin){
+  # allocate memory for odds ratio output 
+  cg_dinuc_oddsRatio <- double() 
+  n<-length(fastaVec)
+  for (i in 1:(n-slideWin)){
+    # array slice the vectpr on sliding window size and obtain dinuc appearances
+    dinucs<-count(fastaVec[i:(i+slideWin)],2)
+    # retrieve number of times "CG" appeared
+    cg_dinuc_count <- dinucs["cg"]
+    # get counts of all nucleotides
+    nucs<-count(fastaVec[i:(i+slideWin)],1)
+    # obtain times dinuc pairing appeared per times the indiv nucs appeared
+    cg_dinuc_oddsRatio[i] <- 		
+      cg_dinuc_count/(nucs["c"]*nucs["g"])
+  }
+  return(cg_dinuc_oddsRatio) # returns vector
+}
 
+apoe.fasta.vec <- fasta2vec("apoe.fasta")
+cpg_vec <- calc.sliding.cpg(apoe.fasta.vec,150)
+plot(cpg_vec,type="l",main="Observed vs Expected CG",xlab="Base Index", ylab="Obs/Exp")

apoe.fasta

@@ -0,0 +1,54 @@
+>NC_000019.10:44905796-44909393 Homo sapiens chromosome 19, GRCh38.p14 Primary Assembly
+CTACTCAGCCCCAGCGGAGGTGAAGGACGTCCTTCCCCAGGAGCCGGTGAGAAGCGCAGTCGGGGGCACG
+GGGATGAGCTCAGGGGCCTCTAGAAAGAGCTGGGACCCTGGGAACCCCTGGCCTCCAGGTAGTCTCAGGA
+GAGCTACTCGGGGTCGGGCTTGGGGAGAGGAGGAGCGGGGGTGAGGCAAGCAGCAGGGGACTGGACCTGG
+GAAGGGCTGGGCAGCAGAGACGACCCGACCCGCTAGAAGGTGGGGTGGGGAGAGCAGCTGGACTGGGATG
+TAAGCCATAGCAGGACTCCACGAGTTGTCACTATCATTTATCGAGCACCTACTGGGTGTCCCCAGTGTCC
+TCAGATCTCCATAACTGGGGAGCCAGGGGCAGCGACACGGTAGCTAGCCGTCGATTGGAGAACTTTAAAA
+TGAGGACTGAATTAGCTCATAAATGGAACACGGCGCTTAACTGTGAGGTTGGAGCTTAGAATGTGAAGGG
+AGAATGAGGAATGCGAGACTGGGACTGAGATGGAACCGGCGGTGGGGAGGGGGTGGGGGGATGGAATTTG
+AACCCCGGGAGAGGAAGATGGAATTTTCTATGGAGGCCGACCTGGGGATGGGGAGATAAGAGAAGACCAG
+GAGGGAGTTAAATAGGGAATGGGTTGGGGGCGGCTTGGTAAATGTGCTGGGATTAGGCTGTTGCAGATAA
+TGCAACAAGGCTTGGAAGGCTAACCTGGGGTGAGGCCGGGTTGGGGCCGGGCTGGGGGTGGGAGGAGTCC
+TCACTGGCGGTTGATTGACAGTTTCTCCTTCCCCAGACTGGCCAATCACAGGCAGGAAGATGAAGGTTCT
+GTGGGCTGCGTTGCTGGTCACATTCCTGGCAGGTATGGGGGCGGGGCTTGCTCGGTTCCCCCCGCTCCTC
+CCCCTCTCATCCTCACCTCAACCTCCTGGCCCCATTCAGGCAGACCCTGGGCCCCCTCTTCTGAGGCTTC
+TGTGCTGCTTCCTGGCTCTGAACAGCGATTTGACGCTCTCTGGGCCTCGGTTTCCCCCATCCTTGAGATA
+GGAGTTAGAAGTTGTTTTGTTGTTGTTGTTTGTTGTTGTTGTTTTGTTTTTTTGAGATGAAGTCTCGCTC
+TGTCGCCCAGGCTGGAGTGCAGTGGCGGGATCTCGGCTCACTGCAAGCTCCGCCTCCCAGGTCCACGCCA
+TTCTCCTGCCTCAGCCTCCCAAGTAGCTGGGACTACAGGCACATGCCACCACACCCGACTAACTTTTTTG
+TATTTTCAGTAGAGACGGGGTTTCACCATGTTGGCCAGGCTGGTCTGGAACTCCTGACCTCAGGTGATCT
+GCCCGTTTCGATCTCCCAAAGTGCTGGGATTACAGGCGTGAGCCACCGCACCTGGCTGGGAGTTAGAGGT
+TTCTAATGCATTGCAGGCAGATAGTGAATACCAGACACGGGGCAGCTGTGATCTTTATTCTCCATCACCC
+CCACACAGCCCTGCCTGGGGCACACAAGGACACTCAATACATGCTTTTCCGCTGGGCGCGGTGGCTCACC
+CCTGTAATCCCAGCACTTTGGGAGGCCAAGGTGGGAGGATCACTTGAGCCCAGGAGTTCAACACCAGCCT
+GGGCAACATAGTGAGACCCTGTCTCTACTAAAAATACAAAAATTAGCCAGGCATGGTGCCACACACCTGT
+GCTCTCAGCTACTCAGGAGGCTGAGGCAGGAGGATCGCTTGAGCCCAGAAGGTCAAGGTTGCAGTGAACC
+ATGTTCAGGCCGCTGCACTCCAGCCTGGGTGACAGAGCAAGACCCTGTTTATAAATACATAATGCTTTCC
+AAGTGATTAAACCGACTCCCCCCTCACCCTGCCCACCATGGCTCCAAAGAAGCATTTGTGGAGCACCTTC
+TGTGTGCCCCTAGGTACTAGATGCCTGGACGGGGTCAGAAGGACCCTGACCCACCTTGAACTTGTTCCAC
+ACAGGATGCCAGGCCAAGGTGGAGCAAGCGGTGGAGACAGAGCCGGAGCCCGAGCTGCGCCAGCAGACCG
+AGTGGCAGAGCGGCCAGCGCTGGGAACTGGCACTGGGTCGCTTTTGGGATTACCTGCGCTGGGTGCAGAC
+ACTGTCTGAGCAGGTGCAGGAGGAGCTGCTCAGCTCCCAGGTCACCCAGGAACTGAGGTGAGTGTCCCCA
+TCCTGGCCCTTGACCCTCCTGGTGGGCGGCTATACCTCCCCAGGTCCAGGTTTCATTCTGCCCCTGTCGC
+TAAGTCTTGGGGGGCCTGGGTCTCTGCTGGTTCTAGCTTCCTCTTCCCATTTCTGACTCCTGGCTTTAGC
+TCTCTGGAATTCTCTCTCTCAGCTTTGTCTCTCTCTCTTCCCTTCTGACTCAGTCTCTCACACTCGTCCT
+GGCTCTGTCTCTGTCCTTCCCTAGCTCTTTTATATAGAGACAGAGAGATGGGGTCTCACTGTGTTGCCCA
+GGCTGGTCTTGAACTTCTGGGCTCAAGCGATCCTCCCGCCTCGGCCTCCCAAAGTGCTGGGATTAGAGGC
+ATGAGCCACCTTGCCCGGCCTCCTAGCTCCTTCTTCGTCTCTGCCTCTGCCCTCTGCATCTGCTCTCTGC
+ATCTGTCTCTGTCTCCTTCTCTCGGCCTCTGCCCCGTTCCTTCTCTCCCTCTTGGGTCTCTCTGGCTCAT
+CCCCATCTCGCCCGCCCCATCCCAGCCCTTCTCCCCGCCTCCCACTGTGCGACACCCTCCCGCCCTCTCG
+GCCGCAGGGCGCTGATGGACGAGACCATGAAGGAGTTGAAGGCCTACAAATCGGAACTGGAGGAACAACT
+GACCCCGGTGGCGGAGGAGACGCGGGCACGGCTGTCCAAGGAGCTGCAGGCGGCGCAGGCCCGGCTGGGC
+GCGGACATGGAGGACGTGTGCGGCCGCCTGGTGCAGTACCGCGGCGAGGTGCAGGCCATGCTCGGCCAGA
+GCACCGAGGAGCTGCGGGTGCGCCTCGCCTCCCACCTGCGCAAGCTGCGTAAGCGGCTCCTCCGCGATGC
+CGATGACCTGCAGAAGCGCCTGGCAGTGTACCAGGCCGGGGCCCGCGAGGGCGCCGAGCGCGGCCTCAGC
+GCCATCCGCGAGCGCCTGGGGCCCCTGGTGGAACAGGGCCGCGTGCGGGCCGCCACTGTGGGCTCCCTGG
+CCGGCCAGCCGCTACAGGAGCGGGCCCAGGCCTGGGGCGAGCGGCTGCGCGCGCGGATGGAGGAGATGGG
+CAGCCGGACCCGCGACCGCCTGGACGAGGTGAAGGAGCAGGTGGCGGAGGTGCGCGCCAAGCTGGAGGAG
+CAGGCCCAGCAGATACGCCTGCAGGCCGAGGCCTTCCAGGCCCGCCTCAAGAGCTGGTTCGAGCCCCTGG
+TGGAAGACATGCAGCGCCAGTGGGCCGGGCTGGTGGAGAAGGTGCAGGCTGCCGTGGGCACCAGCGCCGC
+CCCTGTGCCCAGCGACAATCACTGAACGCCGAAGCCTGCAGCCATGCGACCCCACGCCACCCCGTGCCTC
+CTGCCTCCGCGCAGCCTGCAGCGGGAGACCCTGTCCCCGCCCCAGCCGTCCTCCTGGGGTGGACCCTAGT
+TTAATAAAGATTCACCAAGTTTCACGCA
+