From fb9b7b8e5e879fd7734dc07cffa27cdb12b9ef0f Mon Sep 17 00:00:00 2001
From: noah <noah@theschricks.com>
Date: Mon, 21 Mar 2022 23:29:20 -0500
Subject: [PATCH] TOM and Dynamic Tree Cut

---
 Schrick-Noah_CS-7863_Homework-3.R | 30 ++++++++++++++++++++++++++++--
 1 file changed, 28 insertions(+), 2 deletions(-)

diff --git a/Schrick-Noah_CS-7863_Homework-3.R b/Schrick-Noah_CS-7863_Homework-3.R
index e00592e..f2370f7 100644
--- a/Schrick-Noah_CS-7863_Homework-3.R
+++ b/Schrick-Noah_CS-7863_Homework-3.R
@@ -40,9 +40,9 @@ x2_clusters <- ifelse(x2>0,1,-1)
 
 # Plotting
 V(g1)$color <- ifelse(x1_clusters>0,"green","yellow")
-V(g1)$size <- 50*abs(x1)
+#V(g1)$size <- 80*abs(x1)
 V(g2)$color <- ifelse(x2_clusters>0,"green","yellow")
-V(g2)$size <- 50*abs(x2)
+V(g2)$size <- 10
 plot(g1, main=paste(g1.netname, " Laplace Spectral Clustering"))
 plot(g2, main=paste(g2.netname, " Laplace Spectral Clustering"),
      vertex.label=NA)
@@ -77,6 +77,32 @@ plot(karate,vertex.size=10,
 
 
 ###################### Part 4: TOM and Dynamic Tree Cut ######################
+# Next smallest eig
+y1 <- eigen(g1.Lap)$vectors[,n1-2] # get n-2 column
+names(y1) <- names(V(g1))
+y1_val <- eigen(g1.Lap)$values[n1-2]
+y2 <- eigen(g2.Lap)$vectors[,n2-2] # get n-2 column
+names(y2) <- names(V(g2))
+y2_val <- eigen(g2.Lap)$values[n2-2]
 
+# Distance in the 2D Laplacian Eig-Space
+lap_dist1 <- dist(cbind(x1,y1))
+lap_dist2 <- dist(cbind(x2,y2))
+
+# Use hierachical clustering of distance matrix
+lap_tree1 <- hclust(lap_dist1)
+lap_tree2 <- hclust(lap_dist2)
+
+# Use WGCNA to dynamically decide how many clusters
+cutreeDynamicTree(lap_tree1)
+cutreeDynamicTree(lap_tree2)
+
+# TOM
+g1.TOM <- TOMsimilarity(as.matrix(g1.adj))
+g2.TOM <- TOMsimilarity(as.matrix(g2.adj))
+g1.TOM
+g2.TOM
+TOMplot(g1.TOM, lap_tree1, main=paste(g1.netname, " Heatmap Plot"))
+TOMplot(g2.TOM, lap_tree2, main=paste(g2.netname, " Heatmap Plot"))
 
 ################################ Part 5: UMAP ################################
\ No newline at end of file