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Finalizing multidimensional scaling

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Noah L. Schrick 2022-11-21 18:30:56 -06:00
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commit 0b75bc95d6
5 changed files with 218 additions and 164 deletions
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@ -1,107 +1,3 @@
################# 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)
@ -406,6 +302,13 @@ 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))
BiocManager::install()
BiocManager::install()
BiocManager::install("stringi")
# Lab 10 for the University of Tulsa's CS-6643 Bioinformatics Course
# Phylogenetic Analysis
# Professor: Dr. McKinney, Fall 2022
# Noah L. Schrick - 1492657
## Set Working Directory to file directory - RStudio approach
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
#### Part A: GenBank sequences and a multiple fasta file
@ -420,17 +323,15 @@ mtDNA.MultiSeqs.list<-read.GenBank(c("AF011222","AF254446","X90314","AF089820",
mtDNA.Species<-attr(mtDNA.MultiSeqs.list,"species")
# use species as name instead of genbank id
names(mtDNA.MultiSeqs.list)<-mtDNA.Species
names(mtDNA.MultiSeqs.list)
# need to fix some names
names(mtDNA.MultiSeqs.list)[1] <- paste("German_Neanderthal",sep="")
names(mtDNA.MultiSeqs.list)[2] <- paste("Russian_Neanderthal",sep="")
names(mtDNA.MultiSeqs.list)[3] <- paste("Human")
names(mtDNA.MultiSeqs.list)[6] <- paste("Puti_Orangutan",sep="")
names(mtDNA.MultiSeqs.list)[12] <- paste("Jari_Orangutan",sep="")
names(mtDNA.MultiSeqs.list)
length(mtDNA.MultiSeqs.list$Human)
# look at one of the sequences using $
mtDNA.MultiSeqs.list$Human
length(mtDNA.MultiSeqs.list$Human)
## Convert to Biostrings object for the sequences
if (!require("BiocManager")) install.packages("BiocManager")
library(BiocManager)
@ -450,63 +351,162 @@ names(mtDNA.multSeqs.bstr) <- Names.vec # count nucs and sequence lengths
num.nts <- alphabetFrequency(mtDNA.multSeqs.bstr)[,1:4]
mtDNA.lengths <- rowSums(num.nts)
proportion.nts <- num.nts/mtDNA.lengths
num.nts
names(mtDA.multSeqs.bstr)
names(mtDNA.multSeqs.bstr)
mtDNA.multSeqs.bstr
mtDNA.multSeqs.bstr
mtDNA.multSeqs.bstr
mtDNA.multSeqs.bstr --help
print(mtDNA.multSeqs.bstr)
print(mtDNA.multSeqs.bstr, -n40)
print(mtDNA.multSeqs.bstr, -n 40)
print(mtDNA.multSeqs.bstr, n=20)
class(mtDNA.multSeqs.bstr)
print(mtDNA.multSeqs.bstr)
?print()
?print
table(mtDNA.multSeqs.bstr)
mtDNA.multSeqs.bstr
mtDNA.multSeqs.bstr$width
mtDNA.multSeqs.bstr[,1]$width
mtDNA.multSeqs.bstr[1,]$width
mtDNA.multSeqs.bstr[1]$width
mtDNA.multSeqs.bstr[1]
mtDNA.multSeqs.bstr[1]$seq
mtDNA.multSeqs.bstr[1]$width
mtDNA.multSeqs.bstr[1]$names
mtDNA.multSeqs.bstr$names
# Obtain name and length of species with longest sequence
nlengthnames <- cbind(mtDNA.lengths, Names.vec)
idx <- which.max(nlengthnames[,1])
nlengthnames[idx,]
#### Part B: Multiple Sequence Alignment
if (!require("BiocManager")) install.packages("BiocManager")
library(BiocManager)
if (!require("msa")) BiocManager::install("msa")
library(msa)
msa(mtDNA.multSeqs.bstr,method="ClustalOmega")
msa(mtDNA.multSeqs.bstr,method="Muscle")
# Lab 10 for the University of Tulsa's CS-6643 Bioinformatics Course
# Phylogenetic Analysis
# Professor: Dr. McKinney, Fall 2022
# Noah L. Schrick - 1492657
## Set Working Directory to file directory - RStudio approach
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
#### Part A: GenBank sequences and a multiple fasta file
if (!require("ape")) install.packages("ape")
library(ape) # needed for read.GenBank
# fetch the mtDNA sequences
mtDNA.MultiSeqs.list<-read.GenBank(c("AF011222","AF254446","X90314","AF089820",
"AF176766","AF451972", "AY079510",
"AF050738","AF176722","AF315498",
"AF176731","AF451964"), as.character=TRUE)
# look at species names
mtDNA.Species<-attr(mtDNA.MultiSeqs.list,"species")
# use species as name instead of genbank id
names(mtDNA.MultiSeqs.list)<-mtDNA.Species
# need to fix some names
names(mtDNA.MultiSeqs.list)[1] <- paste("German_Neanderthal",sep="")
names(mtDNA.MultiSeqs.list)[2] <- paste("Russian_Neanderthal",sep="")
names(mtDNA.MultiSeqs.list)[3] <- paste("Human")
names(mtDNA.MultiSeqs.list)[6] <- paste("Puti_Orangutan",sep="")
names(mtDNA.MultiSeqs.list)[12] <- paste("Jari_Orangutan",sep="")
length(mtDNA.MultiSeqs.list$Human)
# look at one of the sequences using $
mtDNA.MultiSeqs.list$Human
## Convert to Biostrings object for the sequences
if (!require("BiocManager")) install.packages("BiocManager")
library(BiocManager)
if (!require("Biostrings")) BiocManager::install("Biostrings")
library(Biostrings)
# loop through the list to create vector of strings for Biostrings input
Names.vec <- c() # initialize speices names string vector
Seqs.vec <- c() # initialize sequence string vector
for (mtDNA.name in names(mtDNA.MultiSeqs.list))
{
Names.vec <- c(Names.vec,mtDNA.name) # concatenate vector
Seqs.vec <-c(Seqs.vec,paste(mtDNA.MultiSeqs.list[[mtDNA.name]],collapse=""))
}
mtDNA.multSeqs.bstr <- DNAStringSet(Seqs.vec) # convert to Biostring
# name the Biostring sequences and compute stats
names(mtDNA.multSeqs.bstr) <- Names.vec # count nucs and sequence lengths
mtDNA.multSeqs.bstr$names
mtDNA.multSeqs.bstr$Names
mtDNA.lengths
table(mtDNA.lengths, Names.vec)
cbind(mtDNA.lengths, Names.vec)
sort(cbind(mtDNA.lengths, Names.vec))
cbind(mtDNA.lengths, Names.vec)
cbind(mtDNA.lengths, Names.vec)
table(cbind(mtDNA.lengths, Names.vec))
rbind(cbind(mtDNA.lengths, Names.vec))
sort(rbind(cbind(mtDNA.lengths, Names.vec)))
rbind(mtDNA.lengths, Names.vec)
cbind(mtDNA.lengths, Names.vec)
max(cbind(mtDNA.lengths, Names.vec))
max(cbind(mtDNA.lengths, Names.vec))[,1]
max(cbind(mtDNA.lengths, Names.vec)[,1])
max(cbind(mtDNA.lengths, Names.vec)[1,])
max(cbind(mtDNA.lengths, Names.vec)[,1])
max(cbind(mtDNA.lengths, Names.vec))
cbind(mtDNA.lengths, Names.vec)
nlengthnames <- cbind(mtDNA.lengths, Names.vec)
max(nlengthnames[,1])
nlengthnames <- cbind(mtDNA.lengths, Names.vec)
nlengthnames[which.max(nlengthnames[,1])]
idx <- which.max(nlengthnames[,1])
idx
nlengthnames[idx, idx]
nlengthnames[idx]
nlengthnames
nlengthnames[idx,]
num.nts <- alphabetFrequency(mtDNA.multSeqs.bstr)[,1:4]
mtDNA.lengths <- rowSums(num.nts)
proportion.nts <- num.nts/mtDNA.lengths
proportion.nts
# Obtain name and length of species with longest sequence
nlengthnames <- cbind(mtDNA.lengths, Names.vec)
idx <- which.max(nlengthnames[,1])
nlengthnames[idx,]
#### Part B: Multiple Sequence Alignment
if (!require("BiocManager")) install.packages("BiocManager")
library(BiocManager)
if (!require("msa")) BiocManager::install("msa")
library(msa)
mtDNA.msa <- msa(mtDNA.multSeqs.bstr,method="ClustalOmega")
msaPrettyPrint(mtDNA.msa, file="mtDNA.pdf", output="pdf", showNames="left",
showLogo="none", askForOverwrite=FALSE, verbose=TRUE )
## loop to make results data frame
num_seqs <- length(Names.vec)
# initialize data frame
align.stats.df <- data.frame(species=rep(NA,num_seqs), seqlen=rep(0,num_seqs),
numgaps=rep(0,num_seqs), nt_a=rep(NA,num_seqs),
nt_c=rep(NA,num_seqs), nt_g=rep(NA,num_seqs),
nt_t=rep(NA,num_seqs))
# DNAbin type required for dist.dna and helpful for other calculations
mtDNA.msa.DNAbin <- as.DNAbin(mtDNA.msa)
for (i in 1:num_seqs){
seq_name <- Names.vec[i]
seq.vec <- as.character(mtDNA.msa.DNAbin[i,])
num.gaps <- sum(seq.vec=="-")
prop.nt.i <- proportion.nts[i,]
align.stats.df[i,] <- c(seq_name, mtDNA.lengths[i], num.gaps,
round(prop.nt.i[1],digits=2), round(prop.nt.i[2],digits=2),
round(prop.nt.i[3],digits=2), round(prop.nt.i[4],digits=2))
}
# write to file
write.table(align.stats.df,file="alignstats.tab",sep = "\t", row.names=FALSE, quote=FALSE)
# you can use $ operator to grab a named column from a data.frame
# similar to grabbing a named variable from a list
align.stats.df$species
align.stats.df$nt_a # strings by default
as.numeric(align.stats.df$nt_a) # convert to numeric
align.stats.df[1]
align.stats.df[1,]
#### Part C: DNA distance matrices and phylogenetic trees
# Compute Distances
mtDNA.dist <- dist.dna(mtDNA.msa.DNAbin,model="K80")
# manually find closest species
mtDNA.dist.mat <-as.matrix(mtDNA.dist)
diag(mtDNA.dist.mat)<-1 # force diagonal to be 1, not 0
which(mtDNA.dist.mat == min(mtDNA.dist.mat), arr.ind = TRUE)
min(mtDNA.dist.mat)
## Make tree from distance matrix
hc<- hclust(as.dist(mtDNA.dist.mat)) # transform to dist object first
plot(hc,xlab="species",ylab="distance")
## UPGMA
if (!require("phangorn")) install.packages("phangorn")
library(phangorn)
mtDNA.tree.nj <- NJ(mtDNA.dist) # phangorn function
plot(mtDNA.tree.nj, main="Neighbor Joining Tree (rooted) for primates")
mtDNA.tree.upgma <- upgma(mtDNA.dist)
plot(mtDNA.tree.upgma, show.node.label = TRUE, main="UPGMA Tree for Primates")
source("msaUtils.R") # load msaConvert function into memory
mtDNA.msa.phangorn <-msaConvert(mtDNA.msa,type="phangorn::phyDat")
parsimony(mtDNA.tree.nj, mtDNA.msa.phangorn)
# bootstrap to show support for tree edges
# creates trees from bootstrap samples and checks how often
# that edge appears. Show consistency of tree edge.
bs.trees <- bootstrap.phyDat(mtDNA.msa.phangorn, FUN=function(x)NJ(dist.dna(as.DNAbin(x),model="K80")), bs=100)
plotBS(mtDNA.tree.nj, bs.trees, "phylogram", main="Neighbor Joining")
parsimony(mtDNA.tree.upgma, mtDNA.msa.phangorn)
bs.upgma.trees <- bootstrap.phyDat(mtDNA.msa.phangorn, FUN=function(x)upgma(dist.dna(as.DNAbin(x),model="K80")), bs=100)
plotBS(mtDNA.tree.upgma, bs.upgma.trees, "phylogram", main="UPGMA")
#### Part D: Multidimensional Scaling
# 2d MDS viz
locs<-cmdscale(as.dist(myDist))
#### Part D: Multidimensional Scaling
# 2d MDS viz
locs<-cmdscale(as.dist(mtDNA.dist))
x<-locs[,1]
y<-locs[,2]
plot(x,y,main="Multi-dimensional Scaling",xlab="MDS dimension-1",ylab="MDS dimension-2", xlim=c(-.3,.35))
text(x,y,rownames(locs),cex=1.5)
?text()
plot(x,y,main="Multi-dimensional Scaling",xlab="MDS dimension-1",ylab="MDS dimension-2", xlim=c(-.3,.35))
text(x,y,rownames(locs),cex=0.5)
locs<-cmdscale(as.dist(mtDNA.dist),k=3)
x<-locs[,1]
y<-locs[,2]
z<-locs[,3]
plot3d(x,y,z)
text3d(x=x,y=y,z=z,texts=rownames(locs),cex=1.5)
plot3d(x,y,z)
library(rgl)
locs<-cmdscale(as.dist(mtDNA.dist),k=3)
x<-locs[,1]
y<-locs[,2]
z<-locs[,3]
plot3d(x,y,z)
text3d(x=x,y=y,z=z,texts=rownames(locs),cex=1.5)
play3d(spin3d(axis=c(0,1,1), rpm=3), duration=30)
q
plot3d(x,y,z)
text3d(x=x,y=y,z=z,texts=rownames(locs),cex=1.5)
play3d(spin3d(axis=c(0,1,1), rpm=3), duration=30)
plot3d(x,y,z)
text3d(x=x,y=y,z=z,texts=rownames(locs),cex=1.5)

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54
Schrick-Noah_CS-6643_Lab-10.R
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@ -93,3 +93,57 @@ write.table(align.stats.df,file="alignstats.tab",sep = "\t", row.names=FALSE, qu
align.stats.df$species
align.stats.df$nt_a # strings by default
as.numeric(align.stats.df$nt_a) # convert to numeric
#### Part C: DNA distance matrices and phylogenetic trees
# Compute Distances
mtDNA.dist <- dist.dna(mtDNA.msa.DNAbin,model="K80")
# manually find closest species
mtDNA.dist.mat <-as.matrix(mtDNA.dist)
diag(mtDNA.dist.mat)<-1 # force diagonal to be 1, not 0
which(mtDNA.dist.mat == min(mtDNA.dist.mat), arr.ind = TRUE)
## Make tree from distance matrix
hc<- hclust(as.dist(mtDNA.dist.mat)) # transform to dist object first
plot(hc,xlab="species",ylab="distance")
## UPGMA
if (!require("phangorn")) install.packages("phangorn")
library(phangorn)
mtDNA.tree.nj <- NJ(mtDNA.dist) # phangorn function
plot(mtDNA.tree.nj, main="Neighbor Joining Tree (rooted) for primates")
mtDNA.tree.upgma <- upgma(mtDNA.dist)
plot(mtDNA.tree.upgma, show.node.label = TRUE, main="UPGMA Tree for Primates")
source("msaUtils.R") # load msaConvert function into memory
mtDNA.msa.phangorn <-msaConvert(mtDNA.msa,type="phangorn::phyDat")
parsimony(mtDNA.tree.nj, mtDNA.msa.phangorn)
# bootstrap to show support for tree edges
# creates trees from bootstrap samples and checks how often
# that edge appears. Show consistency of tree edge.
bs.trees <- bootstrap.phyDat(mtDNA.msa.phangorn, FUN=function(x)NJ(dist.dna(as.DNAbin(x),model="K80")), bs=100)
plotBS(mtDNA.tree.nj, bs.trees, "phylogram", main="Neighbor Joining")
parsimony(mtDNA.tree.upgma, mtDNA.msa.phangorn)
bs.upgma.trees <- bootstrap.phyDat(mtDNA.msa.phangorn, FUN=function(x)upgma(dist.dna(as.DNAbin(x),model="K80")), bs=100)
plotBS(mtDNA.tree.upgma, bs.upgma.trees, "phylogram", main="UPGMA")
#### Part D: Multidimensional Scaling
# 2d MDS viz
locs<-cmdscale(as.dist(mtDNA.dist))
x<-locs[,1]
y<-locs[,2]
plot(x,y,main="Multi-dimensional Scaling",xlab="MDS dimension-1",ylab="MDS dimension-2", xlim=c(-.3,.35))
text(x,y,rownames(locs),cex=0.5)
library(rgl)
locs<-cmdscale(as.dist(mtDNA.dist),k=3)
x<-locs[,1]
y<-locs[,2]
z<-locs[,3]
plot3d(x,y,z)
text3d(x=x,y=y,z=z,texts=rownames(locs),cex=1.5)
play3d(spin3d(axis=c(0,1,1), rpm=3), duration=30)

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