diff --git a/.Rhistory b/.Rhistory
index d931055..d5f7a57 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,444 +1,3 @@
-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$breaks[-c(1,2)] # remove 0
-g.probs <- g.hist$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$breaks[-c(1,2,3)] # remove 0
-g.probs <- g.hist$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
@@ -489,24 +48,465 @@ S[i,j]<- mismatch_score
 }
 }
 }
-len(S)
-size(S)
-nrows(S)
-nrow(S)
-col(S)
-S
-S[A][T]
-S[A,T]
-S
-S[A]
-S.A
-S.at(A)
-S[1.1]
+#### Part B: Alignment Score Matrix (F) and Traceback Matrix (T)
+x <- unlist(strsplit(x_str, ""))
+y <- unlist(strsplit(y_str, ""))
+x.len <- length(x)
+y.len <- length(y)
+Fmat<-matrix(0,nrow=x.len+1,ncol=y.len+1)
+Tmat<-Fmat # 0's to start
+rownames(Fmat)<-c("-",x); colnames(Fmat)<-c("-",y)
+rownames(Tmat)<-c("-",x); colnames(Tmat)<-c("-",y)
+# create first row and column
+Fmat[,1]<- seq(from=0,len=x.len+1,by=-abs(gap_penalty))
+Fmat[1,]<- seq(from=0,len=y.len+1,by=-abs(gap_penalty))
+Tmat[,1]<- rep(2,x.len+1)  # 2 means align with a gap in the upper seq
+Tmat[1,]<- rep(3,y.len+1)  # 3 means align with a gap in the side seq
+x
+T
+Tmat
+Fmat
+#### Part C: Building Fmat and Tmat
+my.numbers <- c(7,9,-4)
+max(my.numbers)
+which.max(my.numbers)
+#### Part C: Building Fmat and Tmat
+my.numbers <- c(9,9,-4)
+which.max(my.numbers)
 S[1,1]
-S["A", "T"]
-dna.letters("A")
-dna.letters
-?index()
-match("A", dna.letters)
-match("T", dna.letters)
-S[1,4]
+S
+S[1,2]
+rowname(Fmat[i])
+(Fmat[1])
+(Fmat[2])
+(Fmat[2,])
+rownames(Fmat)
+rownames(Fmat[1])
+rownames(Fmat[2])
+rownames(Fmat[2,1])
+rownames(Fmat)[1]
+rownames(Fmat)[2]
+S[rownames(Fmat)[2], colnames(Fmat)[2])
+S[rownames(Fmat)[2], colnames(Fmat)[2]]
+#### Part C: Building Fmat and Tmat
+for (i in 2:nrow(Fmat)){
+for (j in 2:ncol(Fmat)){    # use F recursive rules
+test_three_cases <- c(Fmat[i-1, j-1] + S[rownames(Fmat)[i], colnames(Fmat)[j]],   # 1 mis/match
+# Fmat[i-1, j] + gap_penalty,    # 2 up-gap
+Fmat[i, j-1] + gap_penalty)  # 3 left-gap
+Fmat[i,j]=max(test_three_cases)
+Tmat[i,j]=which.max(test_three_cases)
+}
+}
+final_score <- Fmat[nrow(Fmat),ncol(Fmat)]
+Fmat
+final_score
+#### Part C: Building Fmat and Tmat
+for (i in 2:nrow(Fmat)){
+for (j in 2:ncol(Fmat)){    # use F recursive rules
+test_three_cases <- c(Fmat[i-1, j-1] + S[rownames(Fmat)[i], colnames(Fmat)[j]],   # 1 mis/match
+Fmat[i-1, j] + gap_penalty,    # 2 up-gap
+Fmat[i, j-1] + gap_penalty)  # 3 left-gap
+Fmat[i,j]=max(test_three_cases)
+Tmat[i,j]=which.max(test_three_cases)
+}
+}
+final_score <- Fmat[nrow(Fmat),ncol(Fmat)]
+Fmat
+gap_penalty
+Fmat[1,5]
+Fmat[1,6]
+Tmat
+final_score
+## Aligning from Tmat
+m <- nrow(Fmat)
+n <- ncol(Fmat)
+top_seq <- list()
+side_seq <- list()
+while (m>0 && n>0){
+if (Tmat[m,n] == 3){
+top_seq.append(rownames(Tmat)[m])
+side_seq.append("-")
+m--
+}  else if (Tmat[m,n] == 2){
+while (m>0 && n>0){
+if (Tmat[m,n] == 3){
+top_seq.append(rownames(Tmat)[m])
+side_seq.append("-")
+m--
+}
+else if (Tmat[m,n] == 2){
+while (m>0 && n>0){
+if (Tmat[m,n] == 3){
+top_seq.append(rownames(Tmat)[m])
+side_seq.append("-")
+m--
+}
+while (m>0 && n>0){
+if (Tmat[m,n] == 3){
+top_seq.append(rownames(Tmat)[m])
+side_seq.append("-")
+m--
+}  else if (Tmat[m,n] == 2){
+n <- n-1
+while (m>0 && n>0){
+if (Tmat[m,n] == 3){
+top_seq.append(rownames(Tmat)[m])
+side_seq.append("-")
+m <- m-1
+}  else if (Tmat[m,n] == 2){
+top_seq.append("-")
+side_seq.append(colnames(Tmat)[n])
+n <- n-1
+}  else{
+top_seq.append(rownames(Tmat)[m])
+side_seq.append(colnames(Tmat)[n])
+m <- m-1
+n <- n-1
+}
+}
+while (m>0 && n>0){
+if (Tmat[m,n] == 3){
+top_seq <- append(top_seq, rownames(Tmat)[m])
+side_seq <- append(side_seq, "-")
+m <- m-1
+}  else if (Tmat[m,n] == 2){
+top_seq <- append(top_seq, "-")
+side_seq <- append(side_seq, colnames(Tmat)[n])
+n <- n-1
+}  else{
+top_seq <- append(top_seq, rownames(Tmat)[m])
+side_seq <- append(side_seq, colnames(Tmat)[n])
+m <- m-1
+n <- n-1
+}
+}
+top_seq
+side_seq
+top_seq[1]
+paste(top_seq, collapse=',')
+paste(top_seq, collapse=',')
+paste(side_seq, collapse=',')
+paste(rev(top_seq), collapse=',')
+paste(rev(side_seq), collapse=',')
+## Aligning from Tmat
+m <- nrow(Fmat)
+n <- ncol(Fmat)
+m
+Tmat[5,5]
+rownames(Tmat)[5]
+colnames(Tmat)[5]
+n
+rownames(Tmat)[5,7]
+rownames(Tmat)[7]
+colnames(Tmat)[7]
+paste(rev(top_seq), collapse=',')
+paste(rev(side_seq), collapse=',')
+side_seq <- list()
+top_seq <- list()
+top_seq <- append(top_seq, rownames(Tmat)[m])
+side_seq <- append(side_seq, colnames(Tmat)[n])
+paste(rev(top_seq), collapse=',')
+paste(rev(side_seq), collapse=',')
+m <- m-1
+n <- n-1
+top_seq <- append(top_seq, rownames(Tmat)[m])
+side_seq <- append(side_seq, colnames(Tmat)[n])
+m <- m-1
+n <- n-1
+paste(rev(top_seq), collapse=',')
+paste(rev(side_seq), collapse=',')
+top_seq <- append(top_seq, rownames(Tmat)[m])
+side_seq <- append(side_seq, colnames(Tmat)[n])
+m <- m-1
+n <- n-1
+paste(rev(top_seq), collapse=',')
+paste(rev(side_seq), collapse=',')
+?rbind
+curr_align_col <- rbind(x[n-1],y[m-1])
+curr_align_col
+## Aligning from Tmat
+m <- nrow(Tmat)
+n <- ncol(Tmat)
+seq_align <- character()
+while ((n+m) != 2){
+if (Tmat[m,n] == 3){
+curr_align_col <- rbind("-",y[m-1])
+alignment <- cbind(curr_align_col,alignment)
+m <- m-1
+}  else if (Tmat[m,n] == 2){
+curr_align_col <- rbind(x[n-1],"-")
+alignment <- cbind(curr_align_col,alignment)
+n <- n-1
+}  else{
+curr_align_col <- rbind(x[n-1], y[m-1])
+alignment <- cbind(curr_align_col, alignment)
+m <- m-1
+n <- n-1
+}
+}
+seq_align <- character()
+while ((n+m) != 2){
+if (Tmat[m,n] == 3){
+curr_align_col <- rbind("-",y[m-1])
+seq_align <- cbind(curr_align_col,seq_align)
+m <- m-1
+}  else if (Tmat[m,n] == 2){
+curr_align_col <- rbind(x[n-1],"-")
+seq_align <- cbind(curr_align_col,seq_align)
+n <- n-1
+}  else{
+curr_align_col <- rbind(x[n-1], y[m-1])
+seq_align <- cbind(curr_align_col, seq_align)
+m <- m-1
+n <- n-1
+}
+}
+?cbind
+## Aligning from Tmat
+n <- nrow(Tmat)
+m <- ncol(Tmat)
+seq_align <- character()
+while ((n+m) != 2){
+if (Tmat[m,n] == 3){
+curr_align_col <- rbind("-",y[m-1])
+seq_align <- cbind(curr_align_col,seq_align)
+m <- m-1
+}  else if (Tmat[m,n] == 2){
+curr_align_col <- rbind(x[n-1],"-")
+seq_align <- cbind(curr_align_col,seq_align)
+n <- n-1
+}  else{
+curr_align_col <- rbind(x[n-1], y[m-1])
+seq_align <- cbind(curr_align_col, seq_align)
+m <- m-1
+n <- n-1
+}
+}
+## Aligning from Tmat
+m <- nrow(Tmat)
+n <- ncol(Tmat)
+seq_align <- character()
+while ((n+m) != 2){
+if (Tmat[m,n] == 3){
+curr_align_col <- rbind("-",y[m-1])
+seq_align <- cbind(curr_align_col,seq_align)
+m <- m-1
+}  else if (Tmat[m,n] == 2){
+curr_align_col <- rbind(x[n-1],"-")
+seq_align <- cbind(curr_align_col,seq_align)
+n <- n-1
+}  else{
+curr_align_col <- rbind(x[n-1], y[m-1])
+seq_align <- cbind(curr_align_col, seq_align)
+m <- m-1
+n <- n-1
+}
+}
+seq_align
+curr_align_col
+x
+y
+n
+m
+## Aligning from Tmat
+n<-nrow(Tmat) # start at bottom right of Tmat
+m<-ncol(Tmat)
+alignment<-character()
+while( (n+m)!=2 ){
+if (Tmat[n,m]==1){
+# subtract 1 from x and y indices because they are
+# one row/col smaller than Tmat
+curr_align_col <- rbind(x[n-1],y[m-1])
+alignment <- cbind(curr_align_col,alignment)
+n=n-1; m=m-1; # move back diagonally
+}else if(Tmat[n,m]==2){
+curr_align_col <- rbind(x[n-1],"-") # put gap in top seq
+alignment <- cbind(curr_align_col,alignment)
+n=n-1 # move up
+}else{
+curr_align_col <- rbind("-",y[m-1]) # put gap in side seq
+alignment <- cbind(curr_align_col,alignment)
+m=m-1 # move left
+}
+} # end while
+alignment
+## Aligning from Tmat
+n <- nrow(Tmat)
+m <- ncol(Tmat)
+seq_align <- character()
+while( (n+m)!=2 ){
+if (Tmat[n,m]==1){
+curr_align_col <- rbind(x[n-1],y[m-1])
+seq_align <- cbind(curr_align_col,seq_align)
+n <- n-1
+m <- m-1
+}else if(Tmat[n,m]==2){
+curr_align_col <- rbind(x[n-1],"-") # put gap in top seq
+seq_align <- cbind(curr_align_col,seq_align)
+n=n-1 # move up
+}else{
+curr_align_col <- rbind("-",y[m-1]) # put gap in side seq
+seq_align <- cbind(curr_align_col,seq_align)
+m=m-1 # move left
+}
+} # end while
+alignment
+## Aligning from Tmat
+n <- nrow(Tmat)
+m <- ncol(Tmat)
+seq_align <- character()
+while( (n+m)!=2 ){
+if (Tmat[n,m]==1){
+curr_align_col <- rbind(x[n-1],y[m-1])
+seq_align <- cbind(curr_align_col,seq_align)
+n <- n-1
+m <- m-1
+}else if(Tmat[n,m]==2){
+curr_align_col <- rbind(x[n-1],"-")
+seq_align <- cbind(curr_align_col,seq_align)
+n <- n-1
+}else{
+curr_align_col <- rbind("-",y[m-1])
+seq_align <- cbind(curr_align_col,seq_align)
+m <- m-1
+}
+} # end while
+alignment
+seq_align
+#### Part D: Convert to functions
+make.alignment.matrices <- function(x_str, y_str, match_score, mismatch_score,
+gap_penalty){
+## Substitution Matrix
+dna.letters<-c("A","C","G","T")
+num.letters <- length(dna.letters)
+S<-data.frame(matrix(0,nrow=num.letters,ncol=num.letters))  # data frame
+rownames(S)<-dna.letters; colnames(S)<-dna.letters
+for (i in 1:4){
+for (j in 1:4){
+if(dna.letters[i]==dna.letters[j]){
+S[i,j]<- match_score
+}
+else{
+S[i,j]<- mismatch_score
+}
+}
+}
+## F Matrix and T Matrix
+x <- unlist(strsplit(x_str, ""))
+y <- unlist(strsplit(y_str, ""))
+x.len <- length(x)
+y.len <- length(y)
+Fmat<-matrix(0,nrow=x.len+1,ncol=y.len+1)
+Tmat<-Fmat # 0's to start
+rownames(Fmat)<-c("-",x); colnames(Fmat)<-c("-",y)
+rownames(Tmat)<-c("-",x); colnames(Tmat)<-c("-",y)
+# create first row and column
+Fmat[,1]<- seq(from=0,len=x.len+1,by=-abs(gap_penalty))
+Fmat[1,]<- seq(from=0,len=y.len+1,by=-abs(gap_penalty))
+Tmat[,1]<- rep(2,x.len+1)  # 2 means align with a gap in the upper seq
+Tmat[1,]<- rep(3,y.len+1)  # 3 means align with a gap in the side seq
+## Building Fmat and Tmat
+for (i in 2:nrow(Fmat)){
+for (j in 2:ncol(Fmat)){    # use F recursive rules
+test_three_cases <- c(Fmat[i-1, j-1] + S[rownames(Fmat)[i], colnames(Fmat)[j]],   # 1 mis/match
+Fmat[i-1, j] + gap_penalty,    # 2 up-gap
+Fmat[i, j-1] + gap_penalty)  # 3 left-gap
+Fmat[i,j]=max(test_three_cases)
+Tmat[i,j]=which.max(test_three_cases)
+}
+}
+final_score <- Fmat[nrow(Fmat),ncol(Fmat)]
+return(list(Fmat=Fmat, Tmat=Tmat, score_out=final_score))
+}
+# load new input
+x_str2 <- "GATTA"  # side sequence
+y_str2 <- "GAATTC" # top sequence
+match_score <- 2
+mismatch_score <- -1
+gap_penalty <- -2
+align.list2 <- make.alignment.matrices(x_str2, y_str2, match_score,
+mismatch_score, gap_penalty)
+align.list2$Fmat
+align.list2$Tmat
+align.list2$score_out
+if (!require("gplots")) install.packages("gplots")
+library(gplots)
+Fmat2 <- align.list2$Fmat
+col = c("black","blue","red","yellow","green")
+breaks = seq(min(Fmat2),max(Fmat2),len=length(col)+1)
+heatmap.2(Fmat2[-1,-1], dendrogram='none', density.info="none",
+Rowv=FALSE, Colv=FALSE, trace='none',
+breaks = breaks, col = col,
+sepwidth=c(0.01,0.01),
+sepcolor="black",
+colsep=1:ncol(Fmat2),
+rowsep=1:nrow(Fmat2))
+#### Part E: Traceback Matrix
+show.alignment <- function(x_str,y_str,Tmat){
+################ create the alignment
+# input Tmat and the two sequences: x side seq and y is top seq
+# make character vectors out of the strings
+x<-unlist(strsplit(x_str,""))
+y<-unlist(strsplit(y_str,""))
+n<-nrow(Tmat) # start at bottom right of Tmat
+m<-ncol(Tmat)
+alignment<-character()
+while( (n+m)!=2 ){
+if (Tmat[n,m]==1){
+# subtract 1 from x and y indices because they are
+# one row/col smaller than Tmat
+curr_align_col <- rbind(x[n-1],y[m-1])
+alignment <- cbind(curr_align_col,alignment)
+n=n-1; m=m-1; # move back diagonally
+}else if(Tmat[n,m]==2){
+curr_align_col <- rbind(x[n-1],"-") # put gap in top seq
+alignment <- cbind(curr_align_col,alignment)
+n=n-1 # move up
+}else{
+curr_align_col <- rbind("-",y[m-1]) # put gap in side seq
+alignment <- cbind(curr_align_col,alignment)
+m=m-1 # move left
+}
+} # end while
+return(alignment)
+} # end function
+alignment2 <- show.alignment(x_str2,y_str2,align.list2$Tmat)
+alignment2
+write.table(alignment2,row.names=F,col.names=F,quote=F)
+## Input 3
+x_str3 <- "ATCGT"  # side sequence
+y_str3 <- "TGGTG" # top sequence
+match_score <- 1
+mismatch_score <- -2
+gap_penalty <- -1
+align.list3 <- make.alignment.matrices(x_str3, y_str3, match_score,
+mismatch_score, gap_penalty)
+align.list3$Fmat
+align.list3$Tmat
+align.list3$score_out
+col = c("black","blue","red","yellow","green")
+breaks = seq(min(Fmat3),max(Fmat3),len=length(col)+1)
+heatmap.2(Fmat3[-1,-1], dendrogram='none', density.info="none",
+Rowv=FALSE, Colv=FALSE, trace='none',
+breaks = breaks, col = col,
+sepwidth=c(0.01,0.01),
+sepcolor="black",
+colsep=1:ncol(Fmat3),
+rowsep=1:nrow(Fmat3))
+Fmat3 <- align.list3$Fmat
+align.list3$Fmat
+Fmat3 <- align.list3$Fmat
+align.list3$Tmat
+align.list3$score_out
+o
+heatmap.2(Fmat3[-1,-1], dendrogram='none', density.info="none",
+Rowv=FALSE, Colv=FALSE, trace='none',
+breaks = breaks, col = col,
+sepwidth=c(0.01,0.01),
+sepcolor="black",
+colsep=1:ncol(Fmat3),
+rowsep=1:nrow(Fmat3))
+alignment3 <- show.alignment(x_str3,y_str3,align.list3$Tmat)
+alignment3
+write.table(alignment3,row.names=F,col.names=F,quote=F)
diff --git a/Schrick-Noah_CS-6643_Lab-9.R b/Schrick-Noah_CS-6643_Lab-9.R
index cfb6b89..2d97a34 100644
--- a/Schrick-Noah_CS-6643_Lab-9.R
+++ b/Schrick-Noah_CS-6643_Lab-9.R
@@ -163,3 +163,67 @@ heatmap.2(Fmat2[-1,-1], dendrogram='none', density.info="none",
           sepcolor="black",
           colsep=1:ncol(Fmat2),
           rowsep=1:nrow(Fmat2))
+
+#### Part E: Traceback Matrix
+show.alignment <- function(x_str,y_str,Tmat){ 
+  ################ create the alignment
+  # input Tmat and the two sequences: x side seq and y is top seq
+  # make character vectors out of the strings
+  x<-unlist(strsplit(x_str,""))
+  y<-unlist(strsplit(y_str,""))
+  
+  n<-nrow(Tmat) # start at bottom right of Tmat
+  m<-ncol(Tmat)
+  alignment<-character()
+  while( (n+m)!=2 ){
+    if (Tmat[n,m]==1){
+      # subtract 1 from x and y indices because they are
+      # one row/col smaller than Tmat
+      curr_align_col <- rbind(x[n-1],y[m-1]) 
+      alignment <- cbind(curr_align_col,alignment)
+      n=n-1; m=m-1; # move back diagonally
+    }else if(Tmat[n,m]==2){ 
+      curr_align_col <- rbind(x[n-1],"-") # put gap in top seq
+      alignment <- cbind(curr_align_col,alignment)
+      n=n-1 # move up
+    }else{
+      curr_align_col <- rbind("-",y[m-1]) # put gap in side seq
+      alignment <- cbind(curr_align_col,alignment)
+      m=m-1 # move left
+    }           
+  } # end while
+  return(alignment)
+} # end function
+
+alignment2 <- show.alignment(x_str2,y_str2,align.list2$Tmat)
+alignment2
+write.table(alignment2,row.names=F,col.names=F,quote=F)
+
+## Input 3
+x_str3 <- "ATCGT"  # side sequence
+y_str3 <- "TGGTG" # top sequence
+match_score <- 1
+mismatch_score <- -2
+gap_penalty <- -1 
+
+align.list3 <- make.alignment.matrices(x_str3, y_str3, match_score,
+                                       mismatch_score, gap_penalty)
+align.list3$Fmat
+Fmat3 <- align.list3$Fmat
+align.list3$Tmat
+align.list3$score_out 
+
+col = c("black","blue","red","yellow","green")
+breaks = seq(min(Fmat3),max(Fmat3),len=length(col)+1)
+
+heatmap.2(Fmat3[-1,-1], dendrogram='none', density.info="none", 
+          Rowv=FALSE, Colv=FALSE, trace='none', 
+          breaks = breaks, col = col,
+          sepwidth=c(0.01,0.01),
+          sepcolor="black",
+          colsep=1:ncol(Fmat3),
+          rowsep=1:nrow(Fmat3))
+
+alignment3 <- show.alignment(x_str3,y_str3,align.list3$Tmat)
+alignment3
+write.table(alignment3,row.names=F,col.names=F,quote=F)
diff --git a/Schrick-Noah_CS-6643_Lab-9.docx b/Schrick-Noah_CS-6643_Lab-9.docx
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