Adding in skeleton R code and given code snippets

Schrick-Noah_CS-6643_Lab-2.R

@@ -2,3 +2,115 @@
 # Hierarchical clustering (upgma and linkage matrix), WGCNA, Multidimensional scaling
 # Professor: Dr. McKinney, Fall 2022
 # Noah L. Schrick - 1492657
+
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+
+#### Part A: hclust
+## 1 - Writing a 2-column linkage matrix
+# No supporting code - see report PDF for work.
+
+## 2 - Plot the tree based on Part 1 results
+# initialize an empty list that will contain fields of our hclust object
+a <- list()  
+#    encoding rules: 
+#    negative numbers are leaves (A,B,...,E) -> (-1,-2,...,-5) 
+#    positive are merged clusters (defined by row number in $merge)
+
+# each merged pair in a row  
+a$merge <- rbind(c(-1, -2),  # +1 (A-B)
+                 c(1,-3)  ,  # +2 (A-B)-C  
+                 _______  ,  # +3 (D-E) 
+                 _______  ,  # +4 ((A-B)-C)-(DE)
+                 _______)    # +5 (((A-B)-C)-(DE))-F
+
+a$height <- c(1, 2, 1.5, 3, 4) # merge heights
+a$order <- 1:6               # order of leaves
+a$labels <- LETTERS[1:6]     # labels of leaves as Letters
+class(a) <- "hclust"         # force a to be hclust object
+# plot the tree
+# plot knows that a is an hclust object and plots accordingly 
+plot(a,hang=-1) # or use plot(as.dendrogram(a))
+
+#### Part B - UPGMA and WGCNA
+## 1 - Read in the distance matrix and plot the dendrogram
+my.dist1 <- read.table("distance_matrix.txt",sep="\t", header=T)
+rownames(my.dist1) = colnames(my.dist1)
+my.dist1[lower.tri(my.dist1)] = t(my.dist1)[lower.tri(my.dist1)]
+hc1 <- hclust(as.dist(my.dist1), method="average")
+plot(hc1,hang=-1)
+
+## 2 - Paste the distance matrix in the report. Use header=T
+
+## 3 - no supporting code
+
+## 4 - install and run WGCNA
+# Install
+install.packages("BiocManager")
+library(BiocManager)
+BiocManager::install("WGCNA")
+
+# load WGCNA and run on the hc1 dendrogram
+cutree(hc1,k=3)  # hclust cluster labels with k=3 clusters
+library(WGCNA)
+dynamicMods = cutreeDynamic(
+  dendro = hc1, 
+  distM =	my.dist1,
+  deepSplit = 2, 
+  pamRespectsDendro = FALSE,
+  minClusterSize = 2)
+names(dynamicMods) <- colnames(my.dist1)
+
+## 5 - no supporting code
+
+## 6 - show clustered data
+dynamicColors = labels2colors(dynamicMods) # colors might not work
+table(dynamicColors)
+# Plot the dendrogram and colors underneath
+sizeGrWindow(8,6)
+plotDendroAndColors(hc1, 
+                    dynamicColors,
+                    "Dynamic Tree Cut",
+                    dendroLabels = NULL,
+                    hang = -1,
+                    addGuide = TRUE, 
+                    #guideHang = 0.05,
+                    main = "dynamic cut clustering")
+
+#### Part C - Distance Matrix
+## 1 - no supporting code
+
+## 2 - no supporting code
+
+## 3 - Plot the dendrogram
+
+## 4 - Verify results
+# create distance matrix
+my.dist2 <- matrix(data=rep(0,16),ncol=4)
+my.dist2[1,2]<-.14; my.dist2[1,3]<-.48; my.dist2[1,4]<-.33;
+my.dist2[2,3]<-.50; my.dist2[2,4]<-.28;
+my.dist2[3,4]<-.55;
+# make matrix symmetric
+# make the lower triangle equal to the upper triangle 
+my.dist2[lower.tri(my.dist2)] = t(my.dist2)[lower.tri(my.dist2)]
+diag(my.dist2)<-999 # big number to guarantee it’s not the min 
+# compare with hclust
+test <- hclust(as.dist(my.dist2), method="average") # average linkage, UPGMA
+test$merge
+test$height
+test$order
+test$labels<-LETTERS[1:4]
+plot(test,hang=-1)
+
+#### Part D - MDS
+## 1 - Show the 2D MDS
+## MDS, you might need to refresh (sweep) the plot window
+locs<-cmdscale(as.dist(my.dist2), k=2) # k is number of mds components
+x<-locs[,1]
+y<-locs[,2]
+# pch=NA hides plot symbols:
+plot(x,y,main="Multi-dimensional Scaling", 
+     xlab="MDS dimension-1", ylab="MDS dimension-2", pch=NA,asp=1)
+# pch specifies plot symbol; we want null because we are using letter next
+# plot the text labels instead of symbols:
+text(x,y,test$labels,cex=1)