513 lines
16 KiB
R
513 lines
16 KiB
R
plot(mddTree)
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d
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sort(d)
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d
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mddCorr
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max(mddCorr)
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min(mddCorr)
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d
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max(d)
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mddCorr[0.3660906]
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avg(d)
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average(d)
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mean(d)
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which(d>mean(d))
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d[8]
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which(d>2*mean(d))
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which(d>3*mean(d))
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quartile(d)
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quantile(d)
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quantile(d)[3]
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quantile(d)[4]
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which(d>1.5*quantile(d)[4])
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which(d>1.5*quantile(d)[1])
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quanitle(d)
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quantile(d)
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quantile(d)[1]
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quantile(d)[2]
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which(d<1.5*quantile(d)[2])
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summary(d)
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max(d)
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min(d)
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hist(d)
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boxplot(d)
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hist(d)
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geom_vline(xintercept=quantile, color="red", linetype="dashed", size=1)
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geom_vline(xintercept=quantile, color="red", linetype="dashed", size=1)
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ApproxQuantile(d)
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ApproxMean(d)
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quantile(d)
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max(d)
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which(d=09.3660906)
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which(d=0.3660906)
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which(d, d=0.3660906)
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which(d=0)
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which(d d=0)
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d[0]
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d[1]
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max(d)
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which(max(d))
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plot(mddTree)
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d(AA365)
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mddCorr
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mddTree[AA365]
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mddTree
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mddTree$labels
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mddTree$AA365
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mddTree[AA365]
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mddTree
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mddTree$height
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d2 <- d<0.35
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d2
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d2 <- which(d<0.35)
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d2
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d2 <- d[<0.35]
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d2 <- d[d<0.35]
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d2
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d2 <- d[d<0.35]
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rownames(d2)
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mddTree2 = hclust(as.dist(d2))
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mddTree2$labels <- phenos.df$X
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if (!require("umap")) install.packages("umap")
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obs_mds = cmdscale(d, k=2)
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#add colors for MDD/HC
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colors = rep("black",nrow(SxG.df))
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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#### Optional 2: Compare MDS and UMAP clustering
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obs_mds = cmdscale(d, k=2)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.covfilter))
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colors[startsWith(rownames(GxS.covfilter),"MDD")] <- "red"
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plot(obs_mds, col=colors,
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main="mds of observations", xlab="mds dim1", ylab="mds dim2")
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if (!require("umap")) install.packages("umap")
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library(umap)
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SxG.df <- data.frame(t(GxS.covfilter))
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# change umap config parameters
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custom.config = umap.defaults
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custom.config$random_state = 123
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custom.config$n_epochs = 500
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obs_umap = umap(SxG.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(top_gene_data))
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#add colors for MDD/HC
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colors = rep("black",nrow(top_genes))
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nrow(top_genes)
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top_genes <- as.character(ttest_allgene.sorted[1:top_cutoff,1])
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nrow(top_genes)
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top_genes
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ncol(top_genes)
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numrow
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count(top_genes)
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length(top_genes)
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#add colors for MDD/HC
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colors = rep("black",length(top_genes))
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rownames(top_genes)
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colors[startsWith(top_genes,"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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colors
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top_genes
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top_genes$labels
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top_genes
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#### Optional 2: Compare MDS and UMAP clustering
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if (!require("umap")) install.packages("umap")
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library(umap)
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# change umap config parameters
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custom.config = umap.defaults
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custom.config$random_state = 123
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custom.config$n_epochs = 500
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SxG.df <- data.frame(t(GxS.covfilter))
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obs_mds = cmdscale(d, k=2)
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#add colors for MDD/HC
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colors = rep("black",nrow(SxG.df))
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colors[startsWith(rownames(SxG.df),"MDD")] <- "red"
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plot(obs_mds, col=colors,
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main="mds of observations", xlab="mds dim1", ylab="mds dim2")
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obs_umap = umap(SxG.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(top_gene_data))
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obs_umap = umap(SxG.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(SxG.df))
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colors[startsWith(rownames(SxG.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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#### Optional 3: WGCNA and UMAP of Genes
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if (!require("BiocManager")) install.packages("BiocManager")
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library(BiocManager)
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if (!require("WGCNA")) BiocManager::install("WGCNA")
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library(WGCNA)
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obs_mds = cmdscale(d, k=2)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.covfilter))
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colors[startsWith(rownames(GxS.covfilter),"MDD")] <- "red"
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plot(obs_mds, col=colors,
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main="mds of subject observations", xlab="mds dim1", ylab="mds dim2")
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nrow(SxG.dgf)
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nrow(SxG.df)
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#add colors for MDD/HC
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colors = rep("black",nrow(data.frame(GxS.covfilter)))
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colors[startsWith(rownames(data.frame(GxS.covfilter)),"MDD")] <- "red"
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plot(obs_mds, col=colors,
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main="mds of subject observations", xlab="mds dim1", ylab="mds dim2")
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colors
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GxS.df <- data.frame(GxS.covfilter)
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GxS.df
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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colors
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colors[startsWith(GxS.df),"MDD")] <- "red"
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colors[startsWith(rownames(GxS.df),"MDD")] <- "red"
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colors
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rownames(GxS.df)
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rownames(SxG.df)
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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colors
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obs_mds = cmdscale(d, k=1)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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plot(obs_mds, col=colors,
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main="mds of subject observations", xlab="mds dim1", ylab="mds dim2")
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obs_umap = umap(GxS.df, config=custom.config)
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?umap
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# Plot the dendrogram and colors underneath
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sizeGrWindow(8,6)
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dynamicMods = cutreeDynamic(dendro = mddTree, distM = d,
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deepSplit = 2, pamRespectsDendro = FALSE,
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minClusterSize = 2)
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mddColors = labels2colors(dynamicMods)
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table(mddColors)
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mddColorstable <- table(mddColors,names(mddCuts))
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# Plot the dendrogram and colors underneath
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mddCuts <- cutree(mddTree,k=num.clust)
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sizeGrWindow(8,6)
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dynamicMods = cutreeDynamic(dendro = mddTree, distM = d,
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deepSplit = 2, pamRespectsDendro = FALSE,
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minClusterSize = 2)
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mddColors = labels2colors(dynamicMods)
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table(mddColors)
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mddColorstable <- table(mddColors,names(mddCuts))
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# Plot the dendrogram and colors underneath
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mddCuts <- cutree(mddTree,k=num.clust)
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# Plot the dendrogram and colors underneath
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num.clust <- 5
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mddCuts <- cutree(mddTree,k=num.clust)
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sizeGrWindow(8,6)
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dynamicMods = cutreeDynamic(dendro = mddTree, distM = d,
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deepSplit = 2, pamRespectsDendro = FALSE,
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minClusterSize = 2)
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mddColors = labels2colors(dynamicMods)
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table(mddColors)
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mddColorstable <- table(mddColors,names(mddCuts))
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prop.table(mddColorstable, margin = 1)
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plotDendroAndColors(mddTree, mddColors, "Dynamic Clusters",
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dendroLabels = NULL, # hang = -1,
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addGuide = TRUE, #guideHang = 0.05,
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main = "Clustering with WGCNA")
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names(dynamicMods)
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# Plot the dendrogram and colors underneath
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num.clust <- 2
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mddCuts <- cutree(mddTree,k=num.clust)
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sizeGrWindow(8,6)
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dynamicMods = cutreeDynamic(dendro = mddTree, distM = d,
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deepSplit = 2, pamRespectsDendro = FALSE,
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minClusterSize = 2)
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mddColors = labels2colors(dynamicMods)
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table(mddColors)
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mddColorstable <- table(mddColors,names(mddCuts))
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prop.table(mddColorstable, margin = 1)
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plotDendroAndColors(mddTree, mddColors, "Dynamic Clusters",
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dendroLabels = NULL, # hang = -1,
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addGuide = TRUE, #guideHang = 0.05,
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main = "Clustering with WGCNA")
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custom.config
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custom.config$random_state = 123
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custom.config$n_epochs = 100
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custom.config$n_neighbors=50
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$n_epochs = 15
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custom.config$n_neighbors=10
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of subject observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$n_neighbors=15
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plot(obs_umap$layout, col=colors,
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main="umap of subject observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$random_state = 123
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custom.config$n_epochs = 15
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custom.config$n_neighbors=15
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of subject observations", xlab="umap dim1", ylab="umap dim2")
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plot(obs_umap$layout, col=colors,
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main="umap of subject observations", xlab="umap dim1", ylab="umap dim2")
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# change umap config parameters
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custom.config = umap.defaults
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custom.config$random_state = 123
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custom.config$n_epochs = 15
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custom.config$n_neighbors=15
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of subject observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$n_neighbors=30
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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main="umap of subject observations", xlab="umap dim1", ylab="umap dim2")
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plot(obs_umap$layout, col=colors,
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main="umap of subject observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$n_epochs = 50
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custom.config$n_neighbors=30
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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main="umap of subject observations", xlab="umap dim1", ylab="umap dim2")
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mddCuts
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dynamicMods
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dynamicMods = cutreeDynamic(dendro = mddTree, distM = d,
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deepSplit = 2, pamRespectsDendro = FALSE,
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minClusterSize = 2, maxClusterSize = 2)
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mddCuts
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mddTree
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?cutree
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?cutreeDynamic
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dynamicMods = cutreeDynamic(dendro = mddTree, distM = d,
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deepSplit = 2, pamRespectsDendro = FALSE,
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minClusterSize = 2, method = "hybrid")
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mddColors = labels2colors(dynamicMods)
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table(mddColors)
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mddColorstable <- table(mddColors,names(mddCuts))
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prop.table(mddColorstable, margin = 1)
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plotDendroAndColors(mddTree, mddColors, "Dynamic Clusters",
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dendroLabels = NULL, # hang = -1,
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addGuide = TRUE, #guideHang = 0.05,
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main = "Clustering with WGCNA")
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mnist_labels = colnames(GxS.df)
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mnist_labels
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mnist_labels = c("MDD", "HC")
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mnist_labels
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umap_dat <- umap(GxS.df)
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plot(umap_dat)
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obs_umap = umap(SxG.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(SxG.df))
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colors[startsWith(rownames(SxG.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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obs_umap = umap(GxS.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(SxG.df))
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colors[startsWith(rownames(SxG.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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# Plot the dendrogram and colors underneath
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num.clust <- 2
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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GxS.df
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rownames(GxS.df)
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colnames(GxS.df)
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rownames(GxS.df)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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#add colors for MDD/HC
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colors = rep("black",ncol(GxS.df))
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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?umap
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custom.config
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custom.config$random_state = 123
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custom.config$n_epochs = 50
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custom.config$n_neighbors=30
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custom.config$min_dist = 0.001
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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obs_umap
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obs_umap$layout
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obs_umap
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obs_umap$knn
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obs_umap$data
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custom.config$random_state = 123
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custom.config$n_epochs = 50
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custom.config$n_neighbors=30
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custom.config$min_dist = 0.5
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custom.config$metric = "sl_dist"
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$metric = 'sl_dist'
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$metric = 'correlation'
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$metric = "correlation"
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config
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f
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obs_umap = umap(GxS.df, config=custom.config)
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# change umap config parameters
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custom.config = umap.defaults
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custom.config$random_state = 123
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custom.config$n_epochs = 50
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custom.config$n_neighbors=30
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custom.config$min_dist = 0.5
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obs_umap = umap(GxS.df, config=custom.config, metric=correlation)
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obs_umap = umap(GxS.df, config=custom.config, metric="correlation")
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?umap
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umap.defaults
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custom.config$metric = "correlation"
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$metric = "manhattan"
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obs_umap = umap(GxS.df, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(GxS.df))
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colors[startsWith(colnames(GxS.df),"MDD")] <- "red"
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$metric = "chebyshev"
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$metric = "hamming"
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$metric = "minkowski"
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$metric = "canberra"
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obs_umap = umap(GxS.df, config=custom.config)
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config
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custom.config$metric
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?umap.defaults
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custom.config$metric = "pearson"
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$min_dist = 0.2
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custom.config$metric = "pearson"
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config
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custom.config$input = "dist"
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$min_dist = 0.5
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$alpha = 0.75
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custom.config$gamma = 0.5
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$spread = 5
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custom.config$verbose = "integer"
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$verbose = integer
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$verbose = "logical"
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obs_umap = umap(GxS.df, config=custom.config)
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custom.config$verbose
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obs_umap = umap(GxS.df, config=custom.config)
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# change umap config parameters
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custom.config = umap.defaults
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custom.config$random_state = 123
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custom.config$n_epochs = 50
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custom.config$n_neighbors=30
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custom.config$min_dist = 0.5
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custom.config$metric = "pearson"
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custom.config$input = "dist"
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custom.config$alpha = 0.75
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custom.config$gamma = 0.5
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custom.config$spread = 5
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$spread = 0.25
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custom.config$a = 1
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custom.config$b = 1
|
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# change umap config parameters
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custom.config = umap.defaults
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custom.config$random_state = 123
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custom.config$n_epochs = 50
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custom.config$n_neighbors=30
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custom.config$metric = "pearson"
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custom.config$input = "dist"
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custom.config$a = 1
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custom.config$b = 1
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GxS.df <- data.frame(GxS.covfilter)
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obs_umap = umap(GxS.df, config=custom.config)
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plot(obs_umap$layout, col=colors,
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|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$a = 1.5
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obs_umap = umap(GxS.df, config=custom.config)
|
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plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$a = 0.5
|
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obs_umap = umap(GxS.df, config=custom.config)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$a = 1.5
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custom.config$b = 1.2
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obs_umap = umap(GxS.df, config=custom.config)
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|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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custom.config$b = 0.8
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|
obs_umap = umap(GxS.df, config=custom.config)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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|
custom.config$b = 1.3
|
|
obs_umap = umap(GxS.df, config=custom.config)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
custom.config$b = 1.5
|
|
obs_umap = umap(GxS.df, config=custom.config)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
custom.config$a = 2.5
|
|
custom.config$b = 1.5
|
|
obs_umap = umap(GxS.df, config=custom.config)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
custom.config$a = 7.5
|
|
custom.config$b = 1.5
|
|
obs_umap = umap(GxS.df, config=custom.config)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
custom.config$a = 9.0
|
|
custom.config$b = 1.5
|
|
obs_umap = umap(GxS.df, config=custom.config)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
custom.config$a = 9.5
|
|
custom.config$b = 0.5
|
|
obs_umap = umap(GxS.df, config=custom.config)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|