513 lines
20 KiB
R
513 lines
20 KiB
R
################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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alpha.LM
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
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plot(yeast)
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hist(yeast)
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hist(g.vec)
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g.pois
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g.mean
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alpha.LM
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alpha.ML
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degree(g)
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sort(degree(g))
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sort(degree(g),decreasing=FALSE)
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sort(degree(g),decreasing=F)
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sort(degree(g),decreasing=false)
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sort(degree(g), decreasing = TRUE)
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head(sort(degree(g), decreasing = TRUE))
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stddev(degree(g))
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sd(degree(g))
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tail(sort(degree(g), decreasing = TRUE))
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plot(log(g.breaks.clean), log(g.probs.clean))
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
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plot(log(g.breaks.clean), log(g.probs.clean))
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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plot(log(g.breaks.clean), log(g.probs.clean))
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if (!require("BiocManager")) install.packages("BiocManager")
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library(BiocManager)
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if (!require("Biostrings")) BiocManager::install("Biostrings")
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library(snpStats)
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# Lab 7 for the University of Tulsa's CS-6643 Bioinformatics Course
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# PDB
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# Professor: Dr. McKinney, Fall 2022
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# Noah L. Schrick - 1492657
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## Set Working Directory to file directory - RStudio approach
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setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
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#### Part A: Obtaining PDB - no supporting R Code
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#### Part B: Visualize the 3D structure
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## Install Rpdb and load the pdb
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if (!require("Rpdb")) install.packages("Rpdb")
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library(Rpdb)
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x<-read.pdb("1TGH.pdb")
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natom(x)
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visualize(x,type="l")
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## Visualize the B and C chains
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B_chain_pdb <- subset(x$atoms, x$atoms$chainid=="B")
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C_chain_pdb <- subset(x$atoms, x$atoms$chainid=="C")
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# remove water:
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C_chain_pdb <- subset(C_chain_pdb,C_chain_pdb$resname!="HOH")
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# visualize chains B and C
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BC_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" | x$atoms$chainid=="C")
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color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)))
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visualize(BC_chains_pdb,col=color.vec)
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addResLab(BC_chains_pdb)
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## Visualize B-C and A Chains
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A_chain_pdb <- subset(x$atoms, x$atoms$chainid=="A")
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# remove water
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A_chain_pdb <- subset(A_chain_pdb, A_chain_pdb$resname!="HOH")
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# visualize complex complex
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BCA_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" |
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x$atoms$chainid=="C" | x$atoms$chainid=="A")
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BCA.color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)),rep("blue",natom(A_chain_pdb)))
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visualize(BCA_chains_pdb,col=BCA.color.vec)
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#### Part C: Primary structure and DNA Palindromes
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# get coordinates of C1' atoms of the C-chain DNA molecule
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C_chain_pdb$resname
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C_chain_resids<-unique(C_chain_pdb$resid)
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C_chain_C1prime <- subset(C_chain_pdb, C_chain_pdb$elename=="C1'")
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# get chain C DNA sequence
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C_chain_sequence_messy <- C_chain_C1prime$resname
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C_chain_sequence <- paste(sapply(C_chain_sequence_messy,function(x) {unlist(strsplit(x,""))[2]}),collapse = "")
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if (!require("BiocManager")) install.packages("BiocManager")
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library(BiocManager)
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if (!require("Biostrings")) BiocManager::install("Biostrings")
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library(snpStats)
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C_chain_DNAString <- DNAString(C_chain_sequence)
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dna.pals <- findPalindromes(C_chain_DNAString, min.armlength=3,
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max.looplength=5, max.mismatch = 0)
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dna.pals
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# Lab 7 for the University of Tulsa's CS-6643 Bioinformatics Course
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# PDB
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# Professor: Dr. McKinney, Fall 2022
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# Noah L. Schrick - 1492657
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## Set Working Directory to file directory - RStudio approach
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setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
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#### Part A: Obtaining PDB - no supporting R Code
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#### Part B: Visualize the 3D structure
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## Install Rpdb and load the pdb
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if (!require("Rpdb")) install.packages("Rpdb")
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library(Rpdb)
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x<-read.pdb("1TGH.pdb")
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natom(x)
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visualize(x,type="l")
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## Visualize the B and C chains
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B_chain_pdb <- subset(x$atoms, x$atoms$chainid=="B")
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C_chain_pdb <- subset(x$atoms, x$atoms$chainid=="C")
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# remove water:
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C_chain_pdb <- subset(C_chain_pdb,C_chain_pdb$resname!="HOH")
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# visualize chains B and C
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BC_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" | x$atoms$chainid=="C")
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color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)))
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visualize(BC_chains_pdb,col=color.vec)
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addResLab(BC_chains_pdb)
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## Visualize B-C and A Chains
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A_chain_pdb <- subset(x$atoms, x$atoms$chainid=="A")
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# remove water
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A_chain_pdb <- subset(A_chain_pdb, A_chain_pdb$resname!="HOH")
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# visualize complex complex
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BCA_chains_pdb <- subset(x$atoms, x$atoms$chainid=="B" |
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x$atoms$chainid=="C" | x$atoms$chainid=="A")
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BCA.color.vec <- c(rep("red",natom(B_chain_pdb)),rep("green",natom(C_chain_pdb)),rep("blue",natom(A_chain_pdb)))
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visualize(BCA_chains_pdb,col=BCA.color.vec)
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#### Part C: Primary structure and DNA Palindromes
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# get coordinates of C1' atoms of the C-chain DNA molecule
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C_chain_pdb$resname
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C_chain_resids<-unique(C_chain_pdb$resid)
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C_chain_C1prime <- subset(C_chain_pdb, C_chain_pdb$elename=="C1'")
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# get chain C DNA sequence
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C_chain_sequence_messy <- C_chain_C1prime$resname
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C_chain_sequence <- paste(sapply(C_chain_sequence_messy,function(x) {unlist(strsplit(x,""))[2]}),collapse = "")
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## Find palindromes
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if (!require("BiocManager")) install.packages("BiocManager")
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library(BiocManager)
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if (!require("Biostrings")) BiocManager::install("Biostrings")
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library(snpStats)
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C_chain_DNAString <- DNAString(C_chain_sequence)
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dna.pals <- findPalindromes(C_chain_DNAString, min.armlength=3,
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max.looplength=5, max.mismatch = 0)
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visualize(x,type="l")
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#### Part D: Find the binding site
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## Get size of C chain coords
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dim(C_chain_C1prime_coords)
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#### Part D: Find the binding site
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## Get Coordinates
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C_chain_C1prime_coords <- coords(C_chain_C1prime)
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dim(C_chain_C1prime_coords)
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?coords
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rownames(C_chain_C1prime_coords)
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colnames(C_chain_C1prime_coords)
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C_chain_C1prime_coords[1][1]
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C_chain_C1prime
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# get coordinates of CA atoms of the A-chain protein molecule
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A_chain_sequence_3letter <- A_chain_pdb$resname
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A_chain_resids<-unique(A_chain_pdb$resid)
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A_chain_CA <- subset(A_chain_pdb, A_chain_pdb$elename=="CA")
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A_chain_CA_coords <- coords(A_chain_CA)
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dim(A_chain_CA_coords)
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outer(1:nrow(chain1),
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1:nrow(chain2),
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Vectorize(function(i,j) {
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dist(rbind(chain1[i,],chain2[j,]))
|
|
}
|
|
))}
|
|
outer(1:nrow(chain1),
|
|
1:nrow(chain2),
|
|
Vectorize(function(i,j) {
|
|
dist(rbind(chain1[i,],chain2[j,]))
|
|
}))}
|
|
outer(1:nrow(chain1),
|
|
1:nrow(chain2),
|
|
Vectorize(function(i,j) {
|
|
dist(rbind(chain1[i,],chain2[j,]))
|
|
}))}
|
|
dist(rbind(chain1[i,],chain2[j,]))}))}
|
|
outer(1:nrow(chain1),
|
|
1:nrow(chain2), Vectorize(function(i,j) {dist(rbind(chain1[i,],chain2[j,]))}))}
|
|
# create distance matrix between chains
|
|
pair.dist <- function(chain1,chain2){outer(1:nrow(chain1),1:nrow(chain2),Vectorize(function(i,j) {dist(rbind(chain1[i,],chain2[j,]))}))}
|
|
prot2DNAdistMat <- pair.dist(A_chain_CA_coords,C_chain_C1prime_coords)
|
|
dim(prot2DNAdistMat)
|
|
rownames(prot2DNAdistMat)
|
|
prot2DNAdistMat[1]
|
|
prot2DNAdistMat
|
|
vectorize
|
|
Vectorize
|
|
dim(A_chain_CA_coords)
|
|
colnames(A_chain_CA_coords)
|
|
rownames(A_chain_CA_coords)
|
|
A_chain_CA_coords[1]
|
|
A_chain_CA
|
|
nrow(A_chain_CA_coords)
|
|
# ij location of min in current matrix (2-elt vector)
|
|
min_dist <- min(prot2DNAdistMat)
|
|
min_dist
|
|
min_ij <- which(prot2DNAdistMat == min_dist, arr.ind = TRUE)
|
|
min_ij
|
|
A_chain_sequence_3letter[min_ij[1]] # closest A-chain residue
|
|
strsplit(C_chain_sequence,"")[[1]][min_ij[2]] # closest C-chain residue
|
|
?visualize
|
|
# color binding residues
|
|
CA_chains_pdb <- subset(x$atoms, x$atoms$chainid == "C" | x$atoms$chainid == "A")
|
|
CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("blue", natom(A_chain_pdb)))
|
|
CA.color.vec[which(CA_chains_pdb$resid == min_ij[1])] <- "purple"
|
|
CA.color.vec[which(CA_chains_pdb$resid == min_ij[2])] <- "purple"
|
|
visualize(CA_chains_pdb, col=CA.color.vec)
|
|
# color binding residues
|
|
CA_chains_pdb <- subset(x$atoms, x$atoms$chainid == "C" | x$atoms$chainid == "A")
|
|
CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("blue", natom(A_chain_pdb)))
|
|
CA.color.vec[which(CA_chains_pdb$resid == min_ij[1])] <- "purple"
|
|
CA.color.vec[which(CA_chains_pdb$resid == min_ij[2])] <- "red"
|
|
visualize(CA_chains_pdb, col=CA.color.vec)
|
|
CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("teal", natom(A_chain_pdb)))
|
|
CA.color.vec[which(CA_chains_pdb$resid == min_ij[1])] <- "purple"
|
|
CA.color.vec[which(CA_chains_pdb$resid == min_ij[2])] <- "red"
|
|
visualize(CA_chains_pdb, col=CA.color.vec)
|
|
CA.color.vec <- c(rep("green", natom(C_chain_pdb)), rep("lightblue", natom(A_chain_pdb)))
|
|
CA.color.vec[which(CA_chains_pdb$resid == min_ij[1])] <- "purple"
|
|
CA.color.vec[which(CA_chains_pdb$resid == min_ij[2])] <- "red"
|
|
visualize(CA_chains_pdb, col=CA.color.vec)
|
|
rgl.postscript("binding_site.pdf", "pdf", drawText=TRUE)
|
|
#### Part E: Palindromes in other organisms
|
|
## Load associated supportive libraries
|
|
if (!require("seqinr")) install.packages("seqinr")
|
|
library(seqinr)
|
|
## Load in the fasta file as a string
|
|
myfasta <- read.fasta(file="sequence.fasta", as.string= TRUE)
|
|
myfasta
|
|
## Load in the fasta file as a string
|
|
myfasta <- read.fasta(file="sequence.fasta", as.string= TRUE)[[1]][1]
|
|
myfasta
|
|
fasta_DNAString <- DNAString(myfasta)
|
|
dna.pals <- findPalindromes(fasta_DNAString, min.armlength=5)
|
|
fasta.dna.pals <- findPalindromes(fasta_DNAString, min.armlength=5)
|
|
fasta.dna.pals
|
|
rc
|
|
BiocManager::install("insect")
|
|
BiocManager::remove("insect")
|
|
BiocManager::uninstall("insect")
|
|
BiocManager::delete("insect")
|
|
remove.packages("insect")
|
|
## Reverse and complement with the "rc" function from insect
|
|
fasta.dna.pals.rev <- rev(fasta.dna.pals)
|
|
dnachars <- strsplit("ACGT", split = "")[[1]]
|
|
comps <- strsplit("TGCA", split = "")[[1]]
|
|
fasta.dna.pals.rev
|
|
fasta.dna.pals.rev[1]
|
|
fasta.dna.pals.rev[4
|
|
]
|
|
fasta.dna.pals.rev[1][4]
|
|
fasta.dna.pals.rev[1][1]
|
|
fasta.dna.pals.rev$views
|
|
class(fasta.dna.pals.rev)
|
|
?Biostrings
|
|
toString(fasta.dna.pals.rev)
|
|
## Reverse and complement with the "rc" function from insect
|
|
fasta.dna.pals.rev <- rev(toString(fasta.dna.pals))
|
|
dnachars <- strsplit("ACGT", split = "")[[1]]
|
|
comps <- strsplit("TGCA", split = "")[[1]]
|
|
fasta.dna.pals.rev
|
|
fasta.dna.pals
|
|
toString(fasta.dna.pals)
|
|
toString(fasta.dna.pals)
|
|
## Reverse and complement with the "rc" function from insect
|
|
fasta.dna.pals.rev <- rev(toString(fasta.dna.pals))
|
|
fasta.dna.pals.rev
|
|
## Reverse and complement with the "rc" function from insect
|
|
rev(strsplit(toString(fasta.dna.pals), split = "")[[1]])
|
|
paste(rev(toString(fasta.dna.pals)),collapse='')
|
|
?rev
|
|
## Reverse and complement with the "rc" function from insect
|
|
paste(rev(strsplit(toString(fasta.dna.pals), split = "")[[1]]), collapse='')
|
|
fasta.dna.pals.rev
|
|
dnachars <- strsplit("ACGT", split = "")[[1]]
|
|
comps <- strsplit("TGCA", split = "")[[1]]
|
|
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dchars]
|
|
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dnachars]
|
|
fasta.dna.pals.rc
|
|
fasta.dna.pals.rc <- dnachars[match(fasta.dna.pals.rc, comps)]
|
|
fasta.dna.pals.rc
|
|
fasta.dna.pals.rc <- paste0(fasta.dna.pals.rc, collapse = "")
|
|
fasta.dna.pals.rc
|
|
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dnachars]
|
|
fasta.dna.pals.rc <- dnachars[match(fasta.dna.pals.rc, comps)]
|
|
fasta.dna.pals.rc <- paste0(fasta.dna.pals.rc, collapse = "")
|
|
fasta.dna.pals.rc
|
|
## Reverse and complement
|
|
#Convert pal to str, split on each char, rev, then join back as a single str
|
|
fasta.dna.pals.rev <- rev(strsplit(toString(fasta.dna.pals),
|
|
split = "")[[1]])
|
|
fasta.dna.pals.rev
|
|
dnachars <- strsplit("ACGT", split = "")[[1]]
|
|
comps <- strsplit("TGCA", split = "")[[1]]
|
|
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dnachars]
|
|
fasta.dna.pals.rc <- dnachars[match(fasta.dna.pals.rc, comps)]
|
|
fasta.dna.pals.rc <- paste0(fasta.dna.pals.rc, collapse = "")
|
|
fasta.dna.pals.rev
|
|
fasta.dna.pals.rc
|
|
# From the rc function in the insect package. Modified for these variables
|
|
dnachars <- strsplit("ACGT", split = "")[[1]]
|
|
comps <- strsplit("TGCA", split = "")[[1]]
|
|
fasta.dna.pals.rc <- fasta.dna.pals.rev[fasta.dna.pals.rev %in% dnachars]
|
|
fasta.dna.pals.rc <- dnachars[match(fasta.dna.pals.rc, comps)]
|
|
fasta.dna.pals.rc <- paste0(fasta.dna.pals.rc, collapse = "")
|
|
fasta.dna.pals.rc
|
|
toString(fasta.dna.pals)
|
|
fasta.dna.pals.rev
|
|
fasta.dna.pals.rc
|
|
fasta.dna.pals == fasta.dna.pals.rc
|
|
toString(fasta.dna.pals) == fasta.dna.pals.rc
|