Updated intermediate R data visualization materials

intermediate-r-data-visualization/1. iris.R

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+#Read data in.
+dat <- read.table("iris.csv", header= TRUE, sep = ",")
+
+#Plot using 'base' graphics.
+plot(x = dat$Sepal.Length, y = dat$Petal.Length,
+     xlab = "Sepal Length", ylab = "Petal Length")
+
+#Install ggplot2.
+library(ggplot2)
+
+#Get details of ggplot2 package.
+library(help = "ggplot2")  
+
+#Plot using qplot. 
+qplot(x = Sepal.Length, y = Petal.Length, data = dat, 
+      xlab = "Sepal Length", ylab = "Petal Length")
+
+qplot(x = Sepal.Length, y = Petal.Length, data = dat, 
+      xlab = "Sepal Length", ylab = "Petal Length",
+      color = Species)
+
+#But the journals charge extra for color figures.
+#Can we use shapes to distinguish species?
+qplot(x = Sepal.Length, y = Petal.Length, data = dat, 
+      xlab = "Sepal Length", ylab = "Petal Length", 
+      shape = Species)
+
+#Check the boxplots for all species simultaneously.
+qplot(x = Species, y = Sepal.Length, data = dat, geom = "boxplot",
+      ylab = "Sepal Length")
+
+
+#----------------------------------------
+#----------------------------------------
+#Underlying grammar is not clear. We'll use ggplot2.
+#Specify what goes on which axis. Specify the data.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length))
+
+#Add geometrical representation of data.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length)) +
+  geom_point()
+
+ggplot(data = dat, aes(x = log10(Sepal.Length), y = Petal.Length)) +
+  geom_point()
+
+#Add axis labels.
+ggplot(data = dat, aes(x = log10(Sepal.Length), y = Petal.Length)) +
+  geom_point() +
+  xlab("Sepal Length (log10)") +
+  ylab("Petal Length")
+
+#Explore options with geom_point.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length)) +
+  geom_point(shape =1)
+
+#Explore options with geom_point.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length)) +
+  geom_point(aes(shape = Species))
+
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, shape = Species)) +
+  geom_point()
+
+#Explore other geometrical mapping options.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length)) +
+  geom_line()
+
+#Explore other geometrical mapping options.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_line()
+
+#Add a trendline to data.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point() + geom_smooth(method = lm)
+
+#Limit the axis.
+
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point() + geom_smooth(method = lm) +
+  coord_cartesian(xlim = c(0, 10), ylim = c(0, 10))
+
+#Sepcify where the breaks should be.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point() + geom_smooth(method = lm) +
+  coord_cartesian(xlim = c(0, 10), ylim = c(0, 10))+
+  scale_x_continuous(breaks = c(0, 2, 4, 6, 8, 10))
+
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point() + geom_smooth(method = lm) +
+  coord_cartesian(xlim = c(0, 10), ylim = c(0, 10))+
+  scale_x_continuous(breaks = 0:5*2)
+
+
+#Move legend to top.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point() + geom_smooth(method = lm) +
+  coord_cartesian(xlim = c(0, 10), ylim = c(0, 10))+
+  scale_x_continuous(breaks = 0:5*2)+
+  theme(legend.direction = "horizontal", legend.position = "top",
+        legend.title = element_blank())+
+  xlab("Sepal Length") +
+  ylab("Petal Length")
+
+
+#Add border to plot.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point() + geom_smooth(method = lm) +
+  coord_cartesian(xlim = c(0, 10), ylim = c(0, 10))+
+  scale_x_continuous(breaks = 0:5*2)+
+  theme(legend.direction = "horizontal", legend.position = "top",
+        legend.title = element_blank(),
+        panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                    fill = NA))+
+  xlab("Sepal Length") +
+  ylab("Petal Length")
+
+
+#Specifying limits with xlim instead of coord_cartesian. 
+#Note the different behavior.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point() + geom_smooth(method = lm) +
+  xlim(5, 10) + ylim(0, 10)+
+  theme(legend.direction = "horizontal", legend.position = "top",
+        legend.title = element_blank(),
+        panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                    fill = NA))+
+  xlab("Sepal Length") +
+  ylab("Petal Length")
+
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point() + geom_smooth(method = lm) +
+  coord_cartesian(xlim = c(5, 10), ylim = c(0, 10)) +
+  theme(legend.direction = "horizontal", legend.position = "top",
+        legend.title = element_blank(),
+        panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                    fill = NA))+
+  xlab("Sepal Length") +
+  ylab("Petal Length")
+  
+#----------------------------------------
+#----------------------------------------
+#Adjust more theme elements.
+#Change axis text, axis label, label direction.
+
+#Make figures for publication.
+p1 <- ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
+  geom_point(size = 0.5) + geom_smooth(method = lm) +
+  coord_cartesian(xlim = c(4, 8.5), ylim = c(0, 8))+
+  scale_x_continuous(breaks = 0:5*2)+
+  theme(legend.direction = "horizontal", legend.position = "top",
+        legend.title = element_blank(),
+        panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                    fill = NA))+
+  xlab("Sepal Length") +
+  ylab("Petal Length")
+
+p2 <- ggplot(data= dat, aes(x = Species, y = Sepal.Length)) +
+  geom_boxplot() + ylab("Sepal Length")  +
+  theme(panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                    fill = NA))
+
+#Package to arrange figures.
+library(cowplot)
+theme_set(theme_grey())
+
+#Arrange plots.
+p <- plot_grid(p1, p2, labels = c("a", "b"))
+
+#Save plot to file.
+ggsave("Iris.pdf", 
+       plot = p,
+       width = 17, height = 8, units = "cm")
+
+#----------------------
+#Plots with multiple facet panels.
+#Facets row wise.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length)) +
+  geom_point() + geom_smooth(method = lm) +
+  facet_grid(Species~.)+
+  coord_cartesian(xlim = c(1, 8), ylim = c(1, 8)) +
+  theme(legend.direction = "horizontal", legend.position = "top",
+        legend.title = element_blank(),
+        panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                    fill = NA))+
+  xlab("Sepal Length") +
+  ylab("Petal Length")
+
+
+#Facets column wise.
+ggplot(data = dat, aes(x = Sepal.Length, y = Petal.Length)) +
+  geom_point() + geom_smooth(method = lm) +
+  facet_grid(.~Species)+
+  coord_cartesian(xlim = c(1, 8), ylim = c(1, 8)) +
+  theme(legend.direction = "horizontal", legend.position = "top",
+        legend.title = element_blank(),
+        panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                    fill = NA))+
+  xlab("Sepal Length") +
+  ylab("Petal Length")

intermediate-r-data-visualization/2. nucleotide_resolution.R

@@ -0,0 +1,41 @@
+library(ggplot2)
+
+#Following library will be used to reformat data for plotting.
+library(reshape2)
+
+#Load the data for plotting.
+load("nucleotide_resolution.RData")
+
+#Open a pdf file for plotting.
+pdf("Nucleotide_resolution.pdf")
+
+#Loop through all genes.
+for (i in 1:length(dat)) {
+  
+  #Get data for this gene.
+  this_dat <- dat[[i]]
+  
+  #Reformat data for plotting.
+  this_dat <- melt(this_dat, id.vars = "Position")
+  
+  #Store the plot in a variable.
+  p <- ggplot(this_dat, aes(x = Position, y = value)) +
+    geom_bar(stat = "identity") +
+    facet_grid(variable ~ .)+
+    scale_y_continuous(breaks = c(0, 0.5, 1))+
+    ylab("Signal")+
+    annotate(geom = "rect",
+             xmin = to_annotate$Start[i],
+             xmax = to_annotate$End[i], 
+             ymin = -Inf,
+             ymax = Inf, fill = "red",
+             color = NA, 
+             alpha = 0.3) +
+    ggtitle(names(dat)[i])
+  
+  #Print plot to file.
+  print(p)
+}
+
+#Close file.
+dev.off()

intermediate-r-data-visualization/3. Hill_equation_static.R

@@ -0,0 +1,56 @@
+#making animations.
+
+#Get a range of substrate concentration values. 
+S <- 0:10000*0.01
+      
+#Half-maximal concentration constant.
+K <- 50
+
+#Define a container object for data.
+#One column for substrate concentration and other 50 for Hill coefficients.
+dat <- matrix(, 10001, 51) 
+
+#Assign substrate concentration values to first column.
+dat[, 1] <- S
+
+#Loop through all Hill coefficients of interest.
+for (i in 1:50) {
+  
+  #Get reaction velocity in terms of fraction of max. velocity.
+  this_y <- (S^i)/((S^i)+(K^i)) #For formula refer Wikipedia page for Hill coefficient.
+  
+  #Assign values for this iteration to the appropriate column.
+  dat[, i+1] <- this_y
+}
+
+#Give names to columns.
+colnames(dat) <- c("S", 1:50)
+
+#ggplot2 only accepts data frames. Convert matrix object to data.frame.
+dat <- as.data.frame(dat)
+
+#Open a pdf file for plotting.
+pdf("Hill_equation.pdf")
+#Loop through all Hill coefficients.
+for (i in 1:50) {
+  
+  #Get the substrate and corresponding reaction velocity values for plotting.
+  this_dat <- dat[, c("S", as.character(i))]
+  
+  #Rename columns.
+  colnames(this_dat) <- c("Substrate", "Rate")
+  
+  #Generate figure.
+  p <- ggplot(this_dat, aes(x = Substrate, y = Rate)) + 
+    geom_line() +
+    coord_cartesian(xlim = c(0, 100), ylim = c(0, 1))+
+    theme(panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                      fill = NA))+
+    ggtitle(paste("Hill coefficient =", i))
+  
+  #Display figure in pdf file.
+  print(p)
+}
+
+#Close pdf file.
+dev.off()

intermediate-r-data-visualization/4. Hill_equation_interactive.R

@@ -0,0 +1,53 @@
+library(shiny)
+library(ggplot2)
+
+#Get a range of substrate concentration values. 
+S <- 0:10000*0.01
+
+#Half-maximal concentration constant.
+K <- 50
+
+# Define User Interface for app ----
+ui <- pageWithSidebar(
+  
+  # App title ----
+  headerPanel("Hill equation"),
+  
+  # Sidebar panel for inputs ----
+  sidebarPanel(
+    
+    sliderInput(inputId = "Hill_coef", 
+                label = "Hill coefficient:",
+                min = 1, max = 50,
+                value = 1)
+    
+  ),
+  
+  # Main panel for displaying outputs ----
+  mainPanel(
+    
+    plotOutput(outputId = "Hill_figure")
+    
+  )
+)
+
+# Define server logic to plot various variables against mpg ----
+server <- function(input, output) {
+  
+  
+  output$Hill_figure <- renderPlot({
+    i <- input$Hill_coef
+    y_vals <- (S^i)/((S^i)+(K^i)) #For formula refer Wikipedia page for Hill coefficient.
+    this_dat <- data.frame(Substrate = S, Rate = y_vals)
+    
+    ggplot(this_dat, aes(x = Substrate, y = Rate)) + 
+      geom_line() +
+      coord_cartesian(xlim = c(0, 100), ylim = c(0, 1))+
+      theme(panel.border = element_rect(size = c(1,1,1,1), color = "black", 
+                                        fill = NA))+
+      ggtitle(paste("Hill coefficient =", i))
+  })
+  
+}
+
+shinyApp(ui, server)

intermediate-r-data-visualization/5. Heatmap.R

@@ -0,0 +1,17 @@
+#Read table.
+library(gplots)
+
+dat <- read.table("norm_counts.txt", sep = "\t", header = T)
+dat <- as.matrix(dat)
+
+#Save in pdf format.
+pdf("Heatmap.pdf", width = 10, height = 10)
+heatmap.2(dat, 
+          dendrogram = "column") #Use labRow = "" to turn off row labels.
+dev.off()
+
+#Save in TIFF format.
+tiff("Heatmap.tiff", width = 10, height = 10, units = 'in', res = 300)
+heatmap.2(dat, 
+          dendrogram = "column") #Use labRow = "" to turn off row labels.
+dev.off()

intermediate-r-data-visualization/Additional_generate_random_data.R

@@ -0,0 +1,27 @@
+dat <- list()
+
+
+genes <- c("YPL1", "BRCA", "DBP2", "1p22", "RLF", "DCLRE1B",
+           "ZNF268", "RNF220", "EPHA2", "SDC3")
+to_annotate <- data.frame(Gene = genes, Start = NA, End = NA)
+
+for (i in genes) {
+  rows <- sample(50:100, 1)
+  dat[[i]] <- matrix(, rows, 6)
+  dat[[i]][, 1] <- runif(rows, 0, 1)
+  dat[[i]][, 4] <- runif(rows, 0, 1)
+  dat[[i]][, 2] <- dat[[i]][, 1] + runif(rows, 0, 0.1)
+  dat[[i]][, 3] <- dat[[i]][, 1] + runif(rows, 0, 0.1)
+  dat[[i]][, 5] <- dat[[i]][, 2] + runif(rows, 0, 0.1)
+  dat[[i]][, 6] <- dat[[i]][, 2] + runif(rows, 0, 0.1)
+  dat[[i]][dat[[i]] < 0 ] <- 0
+  dat[[i]][dat[[i]] > 1 ] <- 1
+  dat[[i]] <- cbind(sample(100:1000, 1) + 1:rows, dat[[i]])
+  colnames(dat[[i]]) <- c("Position", "A1", "A2", "A3", "B1", "B2", "B3")
+  dat[[i]] <- as.data.frame(dat[[i]])
+  
+  to_annotate[which(genes == i), "Start"] <- dat[[i]]$Position[1] + sample(1:20, 1)
+  to_annotate[which(genes == i), "End"] <- to_annotate[which(genes == i), "Start"] + sample(1:20, 1)
+}
+
+save(list = c("dat", "to_annotate"), file = "Detailed_plotting_data.RData")

intermediate-r-data-visualization/iris.csv

@@ -0,0 +1,151 @@
+Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species
+5.1,3.5,1.4,0.2,setosa
+4.9,3,1.4,0.2,setosa
+4.7,3.2,1.3,0.2,setosa
+4.6,3.1,1.5,0.2,setosa
+5,3.6,1.4,0.2,setosa
+5.4,3.9,1.7,0.4,setosa
+4.6,3.4,1.4,0.3,setosa
+5,3.4,1.5,0.2,setosa
+4.4,2.9,1.4,0.2,setosa
+4.9,3.1,1.5,0.1,setosa
+5.4,3.7,1.5,0.2,setosa
+4.8,3.4,1.6,0.2,setosa
+4.8,3,1.4,0.1,setosa
+4.3,3,1.1,0.1,setosa
+5.8,4,1.2,0.2,setosa
+5.7,4.4,1.5,0.4,setosa
+5.4,3.9,1.3,0.4,setosa
+5.1,3.5,1.4,0.3,setosa
+5.7,3.8,1.7,0.3,setosa
+5.1,3.8,1.5,0.3,setosa
+5.4,3.4,1.7,0.2,setosa
+5.1,3.7,1.5,0.4,setosa
+4.6,3.6,1,0.2,setosa
+5.1,3.3,1.7,0.5,setosa
+4.8,3.4,1.9,0.2,setosa
+5,3,1.6,0.2,setosa
+5,3.4,1.6,0.4,setosa
+5.2,3.5,1.5,0.2,setosa
+5.2,3.4,1.4,0.2,setosa
+4.7,3.2,1.6,0.2,setosa
+4.8,3.1,1.6,0.2,setosa
+5.4,3.4,1.5,0.4,setosa
+5.2,4.1,1.5,0.1,setosa
+5.5,4.2,1.4,0.2,setosa
+4.9,3.1,1.5,0.2,setosa
+5,3.2,1.2,0.2,setosa
+5.5,3.5,1.3,0.2,setosa
+4.9,3.6,1.4,0.1,setosa
+4.4,3,1.3,0.2,setosa
+5.1,3.4,1.5,0.2,setosa
+5,3.5,1.3,0.3,setosa
+4.5,2.3,1.3,0.3,setosa
+4.4,3.2,1.3,0.2,setosa
+5,3.5,1.6,0.6,setosa
+5.1,3.8,1.9,0.4,setosa
+4.8,3,1.4,0.3,setosa
+5.1,3.8,1.6,0.2,setosa
+4.6,3.2,1.4,0.2,setosa
+5.3,3.7,1.5,0.2,setosa
+5,3.3,1.4,0.2,setosa
+7,3.2,4.7,1.4,versicolor
+6.4,3.2,4.5,1.5,versicolor
+6.9,3.1,4.9,1.5,versicolor
+5.5,2.3,4,1.3,versicolor
+6.5,2.8,4.6,1.5,versicolor
+5.7,2.8,4.5,1.3,versicolor
+6.3,3.3,4.7,1.6,versicolor
+4.9,2.4,3.3,1,versicolor
+6.6,2.9,4.6,1.3,versicolor
+5.2,2.7,3.9,1.4,versicolor
+5,2,3.5,1,versicolor
+5.9,3,4.2,1.5,versicolor
+6,2.2,4,1,versicolor
+6.1,2.9,4.7,1.4,versicolor
+5.6,2.9,3.6,1.3,versicolor
+6.7,3.1,4.4,1.4,versicolor
+5.6,3,4.5,1.5,versicolor
+5.8,2.7,4.1,1,versicolor
+6.2,2.2,4.5,1.5,versicolor
+5.6,2.5,3.9,1.1,versicolor
+5.9,3.2,4.8,1.8,versicolor
+6.1,2.8,4,1.3,versicolor
+6.3,2.5,4.9,1.5,versicolor
+6.1,2.8,4.7,1.2,versicolor
+6.4,2.9,4.3,1.3,versicolor
+6.6,3,4.4,1.4,versicolor
+6.8,2.8,4.8,1.4,versicolor
+6.7,3,5,1.7,versicolor
+6,2.9,4.5,1.5,versicolor
+5.7,2.6,3.5,1,versicolor
+5.5,2.4,3.8,1.1,versicolor
+5.5,2.4,3.7,1,versicolor
+5.8,2.7,3.9,1.2,versicolor
+6,2.7,5.1,1.6,versicolor
+5.4,3,4.5,1.5,versicolor
+6,3.4,4.5,1.6,versicolor
+6.7,3.1,4.7,1.5,versicolor
+6.3,2.3,4.4,1.3,versicolor
+5.6,3,4.1,1.3,versicolor
+5.5,2.5,4,1.3,versicolor
+5.5,2.6,4.4,1.2,versicolor
+6.1,3,4.6,1.4,versicolor
+5.8,2.6,4,1.2,versicolor
+5,2.3,3.3,1,versicolor
+5.6,2.7,4.2,1.3,versicolor
+5.7,3,4.2,1.2,versicolor
+5.7,2.9,4.2,1.3,versicolor
+6.2,2.9,4.3,1.3,versicolor
+5.1,2.5,3,1.1,versicolor
+5.7,2.8,4.1,1.3,versicolor
+6.3,3.3,6,2.5,virginica
+5.8,2.7,5.1,1.9,virginica
+7.1,3,5.9,2.1,virginica
+6.3,2.9,5.6,1.8,virginica
+6.5,3,5.8,2.2,virginica
+7.6,3,6.6,2.1,virginica
+4.9,2.5,4.5,1.7,virginica
+7.3,2.9,6.3,1.8,virginica
+6.7,2.5,5.8,1.8,virginica
+7.2,3.6,6.1,2.5,virginica
+6.5,3.2,5.1,2,virginica
+6.4,2.7,5.3,1.9,virginica
+6.8,3,5.5,2.1,virginica
+5.7,2.5,5,2,virginica
+5.8,2.8,5.1,2.4,virginica
+6.4,3.2,5.3,2.3,virginica
+6.5,3,5.5,1.8,virginica
+7.7,3.8,6.7,2.2,virginica
+7.7,2.6,6.9,2.3,virginica
+6,2.2,5,1.5,virginica
+6.9,3.2,5.7,2.3,virginica
+5.6,2.8,4.9,2,virginica
+7.7,2.8,6.7,2,virginica
+6.3,2.7,4.9,1.8,virginica
+6.7,3.3,5.7,2.1,virginica
+7.2,3.2,6,1.8,virginica
+6.2,2.8,4.8,1.8,virginica
+6.1,3,4.9,1.8,virginica
+6.4,2.8,5.6,2.1,virginica
+7.2,3,5.8,1.6,virginica
+7.4,2.8,6.1,1.9,virginica
+7.9,3.8,6.4,2,virginica
+6.4,2.8,5.6,2.2,virginica
+6.3,2.8,5.1,1.5,virginica
+6.1,2.6,5.6,1.4,virginica
+7.7,3,6.1,2.3,virginica
+6.3,3.4,5.6,2.4,virginica
+6.4,3.1,5.5,1.8,virginica
+6,3,4.8,1.8,virginica
+6.9,3.1,5.4,2.1,virginica
+6.7,3.1,5.6,2.4,virginica
+6.9,3.1,5.1,2.3,virginica
+5.8,2.7,5.1,1.9,virginica
+6.8,3.2,5.9,2.3,virginica
+6.7,3.3,5.7,2.5,virginica
+6.7,3,5.2,2.3,virginica
+6.3,2.5,5,1.9,virginica
+6.5,3,5.2,2,virginica
+6.2,3.4,5.4,2.3,virginica
+5.9,3,5.1,1.8,virginica

intermediate-r-data-visualization/norm_counts.txt

@@ -0,0 +1,41 @@
+A_1	A_2	A_3	A_4	A_5	B_1	B_2	B_3	B_4	B_5
+ARPC3	7.59558146609294	7.49846976129062	7.59529998367982	7.6203365075194	7.31904103706532	6.55663357594353	6.54556043103162	6.63574912322353	6.58767572932965	6.39330806264629
+FGFR1OP	4.64644461589454	4.6693924157643	4.62474944679443	4.68877626938961	4.58423018796967	3.44577752132689	3.35901816080107	3.40677914346611	3.33862196045563	3.35593817284073
+SIRT2	4.92445739092013	5.316799741365	5.13341223530175	4.99602516571753	5.05048760284383	7.94008090075049	7.99061118709423	7.90211693152644	7.90883695321455	7.62559594097964
+LANCL1	6.60606487428115	6.42747738755195	6.51692073944586	6.51923992674692	6.57160703705662	8.70120916242003	8.65143163994986	8.72876791107601	8.6189779405646	8.66937924997385
+FLRT3	2.64719435360698	2.39510220092522	2.38719717311496	2.51831448353084	2.40674950782727	4.8577295844844	4.78004032240787	4.90989538594111	4.85876857110446	4.88965113548575
+C7orf40	4.93733046411367	4.95591801232018	4.98256523179993	4.96029411789811	4.61999492730462	3.57488679784269	3.77943953805702	3.73197865280821	3.64006047513306	3.45718005182581
+CRTC2	5.67862216778546	5.86219564772601	5.80565075538176	5.64859063307111	5.54804137497598	5.60943289089162	5.70204527905956	5.55869587681012	5.67277418412169	5.2262108091942
+ZNF302	4.472625924353	4.2820069589637	4.45011798874336	4.42814513225236	4.46605632130077	5.87633037800356	5.91734421961601	5.98664579154646	5.96633003028719	6.11432366946889
+SERPINB2	5.82587758242433	5.60968864431261	5.70667325800289	5.78128004316828	5.63217979977056	-2.71388807840066	-1.87527187837664	-2.38515755047136	-2.25452633952334	-2.86510870067796
+PTPRH	4.75948843773156	4.92286439926791	4.81024937651803	4.73582817355347	4.43024296706069	3.07807200138873	3.02889968443497	3.09292875267614	3.05426933696421	2.60053455282269
+FRMD6	6.2060490448853	6.06984939692537	6.03707860533071	6.07044086928831	6.21534770908549	4.76212185531076	4.74141557681403	4.77674554308884	4.70317750727097	4.62484669326774
+TAF6L	3.52689809679401	3.83443931370619	3.8496082983369	3.60703374868802	4.34732380891458	3.18372906303334	3.2978370498802	3.14471077229976	3.23665783849564	3.80597860345633
+DCHS1	4.45184297041952	4.9927889239236	4.75147164074289	4.47552182543901	5.04698849647257	5.31844137266938	5.36077804725462	5.32843042313478	5.30313641229261	5.14616710056106
+TTC7B	4.52946312298117	4.60535952923518	4.47939002730984	4.54761868376918	4.67543599718012	7.16417508225587	7.16928989954445	7.10438731027331	7.14635240945745	7.14549286110273
+BEGAIN	1.86132236800167	2.2755100899557	2.22768054514439	1.95710439952454	3.64938567090939	4.60615397747792	4.5747075279291	4.55930577032555	4.58029619924187	5.70337002488191
+POLD2	7.37572991551105	7.69411210371016	7.61026339197932	7.48518734989382	7.58912350645237	5.72568544177573	5.78777662397257	5.65602591043618	5.72764729019362	5.55138028171099
+LOC147804	3.55501121219123	3.53862145894013	3.44381933999874	3.36651910738549	3.15953662026241	2.85345907690799	2.88224007805016	2.84629154802925	2.99135104628193	2.60684381914372
+EFTUD2	7.74267801877932	7.77549680421624	7.7197600256209	7.75428948556685	7.55411679996904	6.21030927885862	6.29785869260566	6.24362100442901	6.19036769629052	5.99131502623479
+CSRP2BP	4.58596355982548	4.52162811334147	4.62765759602832	4.58682988505756	4.29370827186177	5.235813472945	5.24249477565793	5.23297845196279	5.27838305765992	5.01947039619139
+LARP1	8.42357538530304	8.46001847186296	8.37880874537485	8.35734388607509	8.33462197703499	8.33037489253383	8.37353723807741	8.28640449769492	8.31572498269565	8.12253603067032
+GTF3C3	6.11581857824737	5.93820284704422	6.0189186210156	6.06457662670065	6.00872993059013	4.91707301619402	4.88776754937941	4.93364058863644	4.80778573012425	4.84230933658555
+MICAL1	5.41331047299631	5.75761796000296	5.636942989099	5.52008752010806	5.78702808078927	5.44043525313505	5.44731920770732	5.34401958367816	5.45628213389766	5.40338448022129
+C17orf56	3.57711381238703	4.01107109382443	3.89617663619818	3.77576006817934	4.36990571617302	3.45878924623769	3.54797704938263	3.44445621065822	3.47558536695775	3.74149467139438
+NAT14	4.08878492906411	4.42937768703192	4.40506526817572	4.29514780335851	4.67316799381362	4.98535582261247	5.11692426314216	5.04274738773491	5.07142342642747	5.1993242375251
+MTHFR	5.22667046784888	5.47718410314378	5.42344436034696	5.30089573029036	5.32060844216208	5.52483167209336	5.65312474705526	5.54815919051956	5.5711351777225	5.37131641396058
+ZNF766	4.48241831777422	4.40336738417417	4.4583088650064	4.48513261873127	4.30253917781118	3.29915327911502	3.22909889752444	3.41101440524791	3.2733098797113	2.96217237938358
+ST8SIA1	2.42148232448128	2.35318200492278	2.42111667345361	2.61777730337309	2.58621885440015	5.97736675478943	5.94050988736741	6.00427305032184	5.93938923948951	5.85749396540309
+USP22	8.04143685973466	8.23822320523447	8.14560908351191	8.04764706717702	8.23560011575156	9.00202785680828	9.04819192094229	8.98979267399618	9.01106661044758	8.92578659645911
+NDUFA10	6.73310121965991	6.7193408326248	6.65409718187521	6.75464962772014	6.57008415167885	7.22810922751325	7.19173003426591	7.19942339044689	7.19565947678399	7.10121016002089
+PWWP2A	4.92158104166755	4.7224948594453	4.83184559712663	4.94296319989611	5.00251818326635	5.20654195635831	5.12803315628618	5.18926219668643	5.08338129773381	5.30980159439218
+MYH7B	-0.906228001821028	-0.760778940722528	-0.461617269235912	-1.11148330258797	0.224523597989998	4.34278109526268	4.47177500584342	4.29632447180887	4.4235638374348	4.76824987911062
+TMEM138	4.96343644649727	4.97600664056867	4.96082153186695	4.94238190169584	4.62352311679102	3.20961866655317	3.06742704092422	3.01716204414322	3.19611641311196	2.90699821427789
+PHF1	5.93187808700031	6.13624021083259	6.09075300520298	5.99564692656564	5.99970802811721	6.66750118275575	6.64512412787375	6.62849286436461	6.66716167945513	6.52615197925309
+KIF1B	6.5145414019521	6.42972009668638	6.47756854115383	6.42958710006266	6.48730316857347	9.68430778312752	9.68695920306761	9.66164163883209	9.65604067658075	9.55905468361742
+GRIPAP1	5.95034125887873	6.10535704667109	6.10386528960634	5.94750372844159	5.98240940772457	7.29750103926716	7.37266357270565	7.32326636069117	7.35079469016805	7.13079125582983
+GGH	6.04324587144319	5.8324159517873	5.94390264164318	5.99928455076032	5.92065471598996	4.37402541764065	4.3124896378878	4.40754415855853	4.42105312653506	4.56291643872769
+DDX26B	3.9823910370648	3.84119462907143	3.92719458522729	3.95704612215791	3.98545106960047	4.45309614907899	4.35426807746667	4.52552374930751	4.33030663958645	4.34245981734535
+POLD1	5.28937431629658	5.95231145274591	5.88557211187604	5.46974455234214	6.89868783342821	2.76543445167668	2.71896672357285	2.67020703319323	2.75263820147957	3.23488263401234
+DDX20	4.95220529611355	4.81118074733333	4.8947597812606	4.93246382732568	4.83509937063156	3.66184995454936	3.72539179102255	3.75714906395707	3.70341782536984	3.58091640091416
+FAM200A	3.4904831991513	3.22149562934724	3.41380895519386	3.41425247553315	3.52913482235465	3.45662875862778	3.21935544756984	3.50514392570513	3.32256903711831	3.34465716748347