Completion of Part B

.Rhistory

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+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$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+# load gene expression data
+load("sense.filtered.cpm.Rdata")
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+# load gene expression data
+load("sense.filtered.cpm.Rdata")
+dim(sense.filtered.cpm)
+colnames(sense.filtered.cpm)
+length(sense.filtered.cpm)
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+## 2: Loading Demographic Data
+subject.attrs <- read.csv("Demographic_symptom.csv", stringsAsFactors = FALSE)
+dim(subject.attrs)  # 160 subjects x 40 attributes
+colnames(subject.attrs)  # interested in X (sample ids) and Diag (diagnosis)
+subject.attrs$X
+subject.attrs$Diag
+dim(sense.filtered.cpm)
+colnames(sense.filtered.cpm)
+rownames(sense.filtered.cpm)
+# Matching gene expression samples with their diagnosis
+if (!require("dplyr")) install.packages("dplyr")
+# Matching gene expression samples with their diagnosis
+if (!require("dplyr")) install.packages("dplyr")
+# create a phenotype vector
+# grab X (subject ids) and Diag (Diagnosis) from subject.attrs that
+#   intersect %in% with the RNA-Seq data
+phenos.df <- subject.attrs %>%
+filter(X %in% colnames(sense.filtered.cpm)) %>%
+dplyr::select(X, Diag)
+# Matching gene expression samples with their diagnosis
+if (!require("dplyr")) install.packages("dplyr")
+# create a phenotype vector
+# grab X (subject ids) and Diag (Diagnosis) from subject.attrs that
+#   intersect %in% with the RNA-Seq data
+phenos.df <- subject.attrs %>%
+filter(X %in% colnames(sense.filtered.cpm)) %>%
+dplyr::select(X, Diag)
+library(dplyr)
+# create a phenotype vector
+# grab X (subject ids) and Diag (Diagnosis) from subject.attrs that
+#   intersect %in% with the RNA-Seq data
+phenos.df <- subject.attrs %>%
+filter(X %in% colnames(sense.filtered.cpm)) %>%
+dplyr::select(X, Diag)
+colnames(phenos.df) # $Diag is mdd diagnosis
+# grab Diag column and convert character to factor
+mddPheno <- as.factor(phenos.df$Diag)  # this is our phenotype/class vector
+summary(mddPheno) # MDD -- major depressive disorder, HC -- healthy control
+#### Part B: Normalization
+## 1: log2 transformation
+# raw cpm boxplots and histogram of one sample
+boxplot(sense.filtered.cpm,range=0,
+ylab="raw probe intensity", main="Raw", names=mddPheno)
+# log2 transformed boxplots and histogram
+boxplot(log2(sense.filtered.cpm), range=0,
+ylab="log2 intensity", main="Log2 Transformed", names=mddPheno)
+hist(sense.filtered.cpm[,1], freq=F, ylab="density",
+xlab="raw probe intensity", main="Raw Data Density for Sample 1")
+hist(log2(sense.filtered.cpm[,1]), freq=F, ylab="density",
+xlab="log2 probe intensity", main="log2 Data Density for Sample 1")
+median(sense.filtered.cpm[,1])
+mode(sense.filtered.cpm[,1])
+average(sense.filtered.cpm[,1])
+mean(sense.filtered.cpm[,1])
+getmode <- function(v) {
+uniqv <-unique(v)
+uniqv[which.max(tabulate(match(v, uniqv)))]
+}
+getmode(sense.filtered.cpm[,1])
+median(sense.filtered.cpm[,1])
+mean(sense.filtered.cpm[,1])
+getmode(sense.filtered.cpm)
+median(sense.filtered.cpm)
+mean(sense.filtered.cpm)
+getmode(log2(sense.filtered.cpm))
+median(log2(sense.filtered.cpm))
+mean(log2(sense.filtered.cpm))
+log2(sense.filtered.cpm)
+log2(sense.filtered.cpm[,1])
+getmode(log2(sense.filtered.cpm[,1]))
+median(log2(sense.filtered.cpm[,1]))
+mean(log2(sense.filtered.cpm[,1]))
+data <- data.frame(Mean = c(mean(sense.filtered.cpm[,1]),
+data <- data.frame(Mean = c(mean(sense.filtered.cpm[,1]),
+mean(log2(sense.filtered.cpm[,1]))),
+Mode = c(getmode(sense.filtered.cpm[,1]),
+getmode(log2(sense.filtered.cpm[,1]))),
+Median = c(median(sense.filtered.cpm[,1])),
+)
+data
+data <- data.frame(Mean = c(mean(sense.filtered.cpm[,1]),
+mean(log2(sense.filtered.cpm[,1]))),
+Mode = c(getmode(sense.filtered.cpm[,1]),
+getmode(log2(sense.filtered.cpm[,1]))),
+Median = c(median(sense.filtered.cpm[,1])),
+)
+data <- data.frame(Mean = c(mean(sense.filtered.cpm[,1]),
+mean(log2(sense.filtered.cpm[,1]))),
+Mode = c(getmode(sense.filtered.cpm[,1]),
+getmode(log2(sense.filtered.cpm[,1]))),
+Median = c(median(sense.filtered.cpm[,1]))
+)
+data
+rownames(data) = c("Original", "Log2 Transformed")
+data
+table(data)
+data <- data.frame(Mean = c(mean(sense.filtered.cpm[,1]),
+mean(log2(sense.filtered.cpm[,1]))),
+Mode = c(getmode(sense.filtered.cpm[,1]),
+getmode(log2(sense.filtered.cpm[,1]))),
+Median = c(median(sense.filtered.cpm[,1]))
+)
+rownames(data) = c("Original", "Log2 Transformed")
+data
+## 2: Quantile Normalization
+# install quantile normalize
+#install.packages("BiocManager")
+if (!require("BiocManager")) install.packages("BiocManager")
+library(BiocManager)
+if (!require("preprocessCore"))
+BiocManager::install("preprocessCore")
+library(preprocessCore)    # replace with custom function?
+# apply quantile normalization
+mddExprData_quantile <- normalize.quantiles(sense.filtered.cpm)
+boxplot(mddExprData_quantile,range=0,ylab="raw intensity", main="Quantile Normalized")
+sapply(mddExprData_quantile, function(x) quantile(x, probs = seq(0, 1, 1/4)))

.~lock.lab3_expression.docx#

@@ -1 +1 @@
-,noah,NovaArchSys,22.09.2022 16:59,file:///home/noah/.config/libreoffice/4;
+,noah,NovaArchSys,22.09.2022 19:18,file:///home/noah/.config/libreoffice/4;

Schrick-Noah_CS-6643_Lab-3.R

@@ -35,3 +35,65 @@ mddPheno <- as.factor(phenos.df$Diag)  # this is our phenotype/class vector
 
 summary(mddPheno) # MDD -- major depressive disorder, HC -- healthy control
 
+#### Part B: Normalization
+## 1: log2 transformation
+# raw cpm boxplots and histogram of one sample
+boxplot(sense.filtered.cpm,range=0,
+        ylab="raw probe intensity", main="Raw", names=mddPheno)
+
+hist(sense.filtered.cpm[,1], freq=F, ylab="density", 
+     xlab="raw probe intensity", main="Raw Data Density for Sample 1")
+
+# log2 transformed boxplots and histogram
+boxplot(log2(sense.filtered.cpm), range=0,
+        ylab="log2 intensity", main="Log2 Transformed", names=mddPheno)
+
+hist(log2(sense.filtered.cpm[,1]), freq=F, ylab="density", 
+     xlab="log2 probe intensity", main="log2 Data Density for Sample 1")
+
+getmode <- function(v) {
+  uniqv <-unique(v)
+  uniqv[which.max(tabulate(match(v, uniqv)))]
+}
+
+data <- data.frame(Mean = c(mean(sense.filtered.cpm[,1]),
+                            mean(log2(sense.filtered.cpm[,1]))),
+                   Mode = c(getmode(sense.filtered.cpm[,1]),
+                            getmode(log2(sense.filtered.cpm[,1]))),
+                   Median = c(median(sense.filtered.cpm[,1]))
+                   )
+rownames(data) = c("Original", "Log2 Transformed")
+data
+
+## 2: Quantile Normalization
+# install quantile normalize
+#install.packages("BiocManager")
+if (!require("BiocManager")) install.packages("BiocManager")
+library(BiocManager)
+if (!require("preprocessCore"))
+  BiocManager::install("preprocessCore")
+library(preprocessCore)    # replace with custom function?
+
+# apply quantile normalization
+mddExprData_quantile <- normalize.quantiles(sense.filtered.cpm)
+
+boxplot(mddExprData_quantile,range=0,
+        ylab="raw intensity", main="Quantile Normalized")
+
+head(mddExprData_quantile)
+
+## 3: Log2 on quantile normalized data
+mddExprData_quantileLog2 <- log2(mddExprData_quantile)
+# add phenotype names to matrix
+colnames(mddExprData_quantileLog2) <- mddPheno  
+
+boxplot(mddExprData_quantileLog2,range=0,
+        ylab="log2 intensity", main="Quantile Normalized Log2")
+
+hist(log2(mddExprData_quantileLog2[,1]), freq=F, 
+     ylab="density", xlab="log2 probe intensity", 
+     main="log2 Quantile Normalized for Sample 1")
+
+## 4: Means
+mean(mddExprData_quantileLog2[,1])
+colMeans(mddExprData_quantileLog2)