513 lines
17 KiB
R
513 lines
17 KiB
R
if (!require("dplyr")) install.packages("dplyr")
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library(dplyr)
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# create a phenotype vector
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# grab X (subject ids) and Diag (Diagnosis) from subject.attrs that
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# intersect %in% with the RNA-Seq data
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phenos.df <- subject.attrs %>%
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filter(X %in% colnames(sense.filtered.cpm)) %>%
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dplyr::select(X, Diag)
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colnames(phenos.df) # $Diag is mdd diagnosis
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# grab Diag column and convert character to factor
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mddPheno <- as.factor(phenos.df$Diag) # this is our phenotype/class vector
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summary(mddPheno) # MDD -- major depressive disorder, HC -- healthy control
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#### Part B: Normalization
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## 1: log2 transformation
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# raw cpm boxplots and histogram of one sample
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boxplot(sense.filtered.cpm,range=0,
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ylab="raw probe intensity", main="Raw", names=mddPheno)
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hist(sense.filtered.cpm[,1], freq=F, ylab="density",
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xlab="raw probe intensity", main="Raw Data Density for Sample 1")
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# log2 transformed boxplots and histogram
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boxplot(log2(sense.filtered.cpm), range=0,
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ylab="log2 intensity", main="Log2 Transformed", names=mddPheno)
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hist(log2(sense.filtered.cpm[,1]), freq=F, ylab="density",
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xlab="log2 probe intensity", main="log2 Data Density for Sample 1")
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getmode <- function(v) {
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uniqv <-unique(v)
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uniqv[which.max(tabulate(match(v, uniqv)))]
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}
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data <- data.frame(Mean = c(mean(sense.filtered.cpm[,1]),
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mean(log2(sense.filtered.cpm[,1]))),
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Mode = c(getmode(sense.filtered.cpm[,1]),
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getmode(log2(sense.filtered.cpm[,1]))),
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Median = c(median(sense.filtered.cpm[,1]))
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)
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rownames(data) = c("Original", "Log2 Transformed")
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data
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## 2: Quantile Normalization
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# install quantile normalize
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#install.packages("BiocManager")
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if (!require("BiocManager")) install.packages("BiocManager")
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library(BiocManager)
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if (!require("preprocessCore"))
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BiocManager::install("preprocessCore")
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library(preprocessCore) # replace with custom function?
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# apply quantile normalization
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mddExprData_quantile <- normalize.quantiles(sense.filtered.cpm)
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boxplot(mddExprData_quantile,range=0,ylab="raw intensity", main="Quantile Normalized")
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sapply(mddExprData_quantile, function(x) quantile(x, probs = seq(0, 1, 1/4)))
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quantile(mddExprData_quantile, probs = seq(0, 1, 1/4)))
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quantile(mddExprData_quantile, probs = seq(0, 1, 1/4))
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length(mddExprData_quantile)
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sapply(mddExprData_quantile, function(x) quantile(mddExprData_quantile, probs = seq(0, 1, 1/4)))
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head(mddExprData_quantile)
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head(mddExprData_quantile)
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head(mddExprData_quantile[,3])
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head(mddExprData_quantile[,1-3])
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head(mddExprData_quantile)
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### 3: Log2 on quantile normalized data
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mddExprData_quantileLog2 <- log2(mddExprData_quantile)
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# add phenotype names to matrix
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colnames(mddExprData_quantileLog2) <- mddPheno
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boxplot(mddExprData_quantileLog2,range=0,
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ylab="log2 intensity", main="Quantile Normalized Log2")
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hist(log2(mddExprData_quantileLog2[,1]), freq=F,
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ylab="density", xlab="log2 probe intensity",
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main="log2 Quantile Normalized for Sample 1")
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## 4: Means
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mean(mddExprData_quantileLog2[,1])
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colMeans(mddExprData_quantileLog2)
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## 4: Means
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mean(mddExprData_quantileLog2[,1])
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expr_SxG <- data.frame(t(mddExprData_quantileLog2)) # Subject x Gene
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colnames(expr_SxG) <- rownames(sense.filtered.cpm) # add gene names
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## MDS of subjects
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#d<-dist(expr_SxG) # Euclidean metric
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mddCorr<-cor(t(expr_SxG)) # distance based on correlation
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d <- sqrt(1-mddCorr)
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mdd.mds <- cmdscale(d, k = 2)
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x <- mdd.mds[,1]
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y <- mdd.mds[,2]
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mdd.mds.df <- data.frame(mdd.mds)
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colnames(mdd.mds.df) <- c("dim1","dim2")
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if (!require("ggplot2"))
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BiocManager::install("ggplot2")
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library(ggplot2)
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p <- ggplot() # initialize empty ggplot object
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p <- p + geom_point(data=mdd.mds.df, aes(x=dim1, y=dim2, color=mddPheno, shape=mddPheno), size=3)
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p <- p + ggtitle("MDS") + xlab("Dim 1") + ylab("Dim 2")
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print(p)
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expr_SxG <- data.frame(t(mddExprData_quantileLog2)) # Subject x Gene
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colnames(expr_SxG) <- rownames(sense.filtered.cpm) # add gene names
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## MDS of subjects
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#d<-dist(expr_SxG) # Euclidean metric
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mddCorr<-cor(t(expr_SxG)) # distance based on correlation
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d <- sqrt(1-mddCorr)
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mdd.mds <- cmdscale(d, k = 2)
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x <- mdd.mds[,1]
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y <- mdd.mds[,2]
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mdd.mds.df <- data.frame(mdd.mds)
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colnames(mdd.mds.df) <- c("dim1","dim2")
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if (!require("ggplot2"))
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BiocManager::install("ggplot2")
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library(ggplot2)
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p <- ggplot() # initialize empty ggplot object
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p <- p + geom_point(data=mdd.mds.df, aes(x=dim1, y=dim2, color=mddPheno, shape=mddPheno), size=3)
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p <- p + ggtitle("MDS") + xlab("Dim 1") + ylab("Dim 2")
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print(p)
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dim(mdd.mds)
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dim(mdd.mds.df)
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library(umap)
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mddTree = hclust(as.dist(d))
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mddTree$labels <- mddPheno
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plot(mddTree)
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num.clust <- 5
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mddCuts <- cutree(mddTree,k=num.clust)
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table(names(mddCuts),mddCuts)
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if (!require("dendextend")) install.packages("dendextend")
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library(dendextend)
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if (!require("dendextend")) install.packages("dendextend")
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library(dendextend)
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## 4: UMAP
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if (!require("umap")) install.packages("umap")
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library(umap)
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## 2: hierarchical cluster of subjects
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mddTree = hclust(as.dist(d))
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mddTree$labels <- mddPheno
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plot(mddTree)
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## 2: hierarchical cluster of subjects
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mddTree = hclust(as.dist(d))
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mddTree$labels <- mddPheno
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plot(mddTree)
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table(mddTree)
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num.clust <- 5
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mddCuts <- cutree(mddTree,k=num.clust)
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table(names(mddCuts),mddCuts)
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?table
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prop.table(names(mddCuts),mddCuts)
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mddCutstable <- table(names(mddCuts),mddCuts)
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prop.table(mddCutstable)
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prop.table(mddCutstable, margin=1)
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prop.table(mddCutstable, margin=2)
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if (!require("dendextend")) install.packages("dendextend")
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library(dendextend)
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dend <- as.dendrogram(mddTree)
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dend=color_labels(dend, k=num.clust)
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#dend <- dend %>% color_branches(k = 4)
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plot(dend) # selective coloring of branches AND labels
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## 3: Clusters
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if (!require("WGCNA"))
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BiocManager::install("WGCNA")
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library(WGCNA)
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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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table(mddColors,names(mddCuts))
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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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mddColorstable <- table(mddColors,names(mddCuts))
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prop.table(mddColorstable)
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prop.table(mddColorstable, margin = 1)
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## 4: UMAP
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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$n_epochs = 500
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# umap to cluster observations
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obs_umap = umap(expr_SxG, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(expr_SxG))
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#colors[dats[,ncol(dats)]==1]="red"
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dim(obs_umap$layout)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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#add colors for MDD/HC
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colors = rep("black",nrow(expr_SxG))
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colors[dats[,ncol(dats)]==1]="red"
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# colors[dats[,ncol(dats)]==1]="red"
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dim(obs_umap$layout)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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# umap to cluster observations
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obs_umap = umap(expr_SxG, config=custom.config)
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#add colors for MDD/HC
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colors = rep("black",nrow(expr_SxG))
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# colors[dats[,ncol(dats)]==1]="red"
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dim(obs_umap$layout)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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legend("bottomleft", legend = c("class -1","class +1"),
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col=c("black","red"), pch=19)
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# colors[dats[,ncol(dats)]==1]="red"
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colors[expr_SxG[,ncol(expr_SxG)]==1]="red"
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dim(obs_umap$layout)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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legend("bottomleft", legend = c("class -1","class +1"),
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col=c("black","red"), pch=19)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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# colors[dats[,ncol(dats)]==1]="red"
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colors = rep("red",ncol(expr_SxG))
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dim(obs_umap$layout)
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plot(obs_umap$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",nrow(expr_SxG), "red",ncol(expr_SxG))
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dim(obs_umap$layout)
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plot(obs_umap$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",nrow(expr_SxG))
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nrow(expr_SxG)
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obs_umap
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obs_umap$data
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obs_umap
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obs_umap$layout
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#add colors for MDD/HC
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colors = rep("black",nrow(expr_SxG))
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# colors[dats[,ncol(dats)]==1]="red"
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colors[obs_umap[,ncol(obs_umap)]>0]="red"
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# colors[dats[,ncol(dats)]==1]="red"
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colors[obs_umap$layout[,nrow(obs_umap$layout)]>0]="red"
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# colors[dats[,ncol(dats)]==1]="red"
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colors[obs_umap[,nrow(obs_umap$layout)]>0]="red"
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obs_umap$layout
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# colors[dats[,ncol(dats)]==1]="red"
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colors[obs_umap$layout[,1]>0]="red"
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dim(obs_umap$layout)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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obs_umap
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obs_umap$knn
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obs_umap$layout
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obs_umap$layout[0]
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obs_umap$layout[1]
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obs_umap$layout$name
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obs_umap$layout$names
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obs_umap$layout
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obs_umap$layout$HC
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obs_umap
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obs_umap$config
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obs_umap$data
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obs_umap
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# umap to cluster observations
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obs_umap = umap(expr_SxG, config=custom.config)
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dim(obs_umap$layout)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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legend("bottomleft", legend = c("class -1","class +1"),
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col=c("black","red"), pch=19)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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plot(obs_umap$layout, #col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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rownames(expr_SxG)
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labels2colors(obs_umap)
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labels2colors(as.df(obs_umap))
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labels2colors(as.dataframe(obs_umap))
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rownames(expr_SxG)
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colors = rownames(expr_SxG)
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colors
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sapply(colors, ifelse(grep(rownames(expr_SxG), "MDD", fixed=TRUE), "black", "red"))
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sapply(colors[i], ifelse(grep(rownames(expr_SxG[i]), "MDD", fixed=TRUE), "black", "red"))
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colors <- sapply(expr_SxG, ifelse(grep(rownames(expr_SxG), "MDD", fixed=TRUE), "black", "red"))
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colors <- sapply(expr_SxG, (ifelse(grep(rownames(expr_SxG), "MDD", fixed=TRUE), "black", "red")))
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colors <- sapply(expr_SxG, (ifelse(grep(rownames(expr_SxG), "MDD", fixed=TRUE)), "black", "red"))
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colors <- sapply(expr_SxG, (ifelse(grep(rownames(expr_SxG), "MDD", fixed=TRUE)) "black", "red"))
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colors <- sapply(expr_SxG, (ifelse(grep(rownames(expr_SxG), "MDD", fixed=TRUE)), "black", "red"))
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colors <- sapply(expr_SxG, 1, function(x) {ifelse(grep(rownames(expr_SxG), "MDD", fixed=TRUE)), "black", "red"})
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colors <- sapply(expr_SxG, 1, function(x) {ifelse(grep(rownames(x), "MDD", fixed=TRUE)), "black", "red"})
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colors <- sapply(expr_SxG, 1, function(x) {ifelse(grep(rownames(x), "MDD", fixed=TRUE), "black", "red")})
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colors <- sapply(expr_SxG, function(x) {ifelse(grep(rownames(x), "MDD", fixed=TRUE), "black", "red")})
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?sapply
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colors <- sapply(expr_SxG, function(x) {ifelse(grep(rownames(x), MDD, fixed=TRUE), "black", "red")})
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colors <- sapply(expr_SxG, function(x) {ifelse(grep(rownames(x), "MDD"", fixed=TRUE), "black", "red")})
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colors <- sapply(expr_SxG, function(x) {ifelse(grep(rownames(x), "MDD", fixed=TRUE), "black", "red")})
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grep(rownames(expr_SxG), "MDD")
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grep(rownames(expr_SxG), "HC")
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grep(rownames(expr_SxG), "HC", fixed=TRUE)
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colors <- sapply(expr_SxG, function(x) {ifelse(grep(rownames(x), "MDD", fixed=TRUE), "black", "red")})
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rownames(expr_SxG[1])
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rownames(expr_SxG[1][1])
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rownames(expr_SxG[1],[1])
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rownames(expr_SxG[1,1])
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rownames(expr_SxG[2,2])
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rownames(expr_SxG[2])
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rownames(expr_SxG)[1]
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rownames(expr_SxG)[2]
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colors <- sapply(expr_SxG, function(x) {ifelse(grep(rownames(expr_SxG)[x], "MDD", fixed=TRUE), "black", "red")})
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colors
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#add colors for MDD/HC
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colors = rep("black",nrow(expr_SxG))
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colors <- sapply(expr_SxG, function(x) {ifelse(grep(rownames(expr_SxG)[x], "MDD", fixed=TRUE), "black", "red")})
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colors
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{ifelse(grep(x,
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"MDD", fixed=TRUE),
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"black", "red")})
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# colors[dats[,ncol(dats)]==1]="red"
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colors <- sapply(expr_SxG, function(x)
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{ifelse(
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grep(x, "MDD", fixed=TRUE),
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"black", "red")
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}
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)
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colors
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ifelse(grep(expr_SxG, "MDD", fixed=TRUE), "black", "red"
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)
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colors <- ifelse(grep(expr_SxG, "MDD", fixed=TRUE), "black", "red"
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)
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colors
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colors <- ifelse(grepl(expr_SxG, "MDD", fixed=TRUE), "black", "red")
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colors
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# colors[dats[,ncol(dats)]==1]="red"
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colors <- sapply(expr_SxG, function(x)
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{ifelse(
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grepl(x, "MDD", fixed=TRUE),
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"black", "red")
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}
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)
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colors
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dim(obs_umap$layout)
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plot(obs_umap$layout, col=colors,
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main="umap of observations", xlab="umap dim1", ylab="umap dim2")
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{ifelse(
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grepl(expr_SxG[x], "MDD", fixed=TRUE),
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"black", "red")
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}
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expr_SxG
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{ifelse(
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grepl(rownames(x), "MDD", fixed=TRUE),
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"black", "red")
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}
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{ifelse(
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grepl(rownames(SxG)[x], "MDD", fixed=TRUE),
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"black", "red")
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}
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{ifelse(
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grepl(rownames(expr_SxG)[x], "MDD", fixed=TRUE),
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"black", "red")
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}
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for (i in numrows(expr_SxG)) {
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colors.append() <- ifelse(grepl(rownames(expr_SxG[i]), "MDD", fixed=TRUE), "black", "red")
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}
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for (i in nrow(expr_SxG)) {
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colors.append() <- ifelse(grepl(rownames(expr_SxG[i]), "MDD", fixed=TRUE), "black", "red")
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}
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expr_SxG
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expr_SxG[1]
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for (i in nrow(expr_SxG)) {
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colors.append() <- ifelse(grepl(rownames(expr_SxG[i]), "MDD", fixed=TRUE), "black", "red")
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}
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colors
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colors[i] <- ifelse(grepl(rownames(expr_SxG[i]), "MDD", fixed=TRUE), "black", "red")
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colors
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expr_SxG
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expr_SxG[1][1]
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expr_SxG[1]
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expr_SxG[2]
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expr_SxG[1]
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expr_SxG[1,1]
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expr_SxG[0,1]
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expr_SxG[0,0]
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expr_SxG[1,0]
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expr_SxG[1,2]
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expr_SxG[3,2]$name
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class(expr_SxG[3,2])
|
|
class(expr_SxG)
|
|
expr_SxG[3,2]$label
|
|
expr_SxG[3,2]$labels
|
|
labels(expr_SxG)
|
|
labels(expr_SxG[1])
|
|
labels(expr_SxG[1,1])
|
|
labels(expr_SxG[1,2])
|
|
labels(expr_SxG[3,2])
|
|
labels(expr_SxG[1,])
|
|
labels(expr_SxG[,1])
|
|
colnames(expr_SxG)
|
|
rownames(expr_SxG)
|
|
rownames(expr_SxG[1])
|
|
rownames(expr_SxG[1])[1]
|
|
rownames(expr_SxG[1])[2]
|
|
rownames(expr_SxG[1])[31]
|
|
for (i in nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl(rownames(expr_SxG)[i], "MDD", fixed=TRUE), "black", "red")
|
|
}
|
|
colors
|
|
colors <- list()
|
|
for (i in nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl(rownames(expr_SxG)[i], "MDD", fixed=TRUE), "black", "red")
|
|
}
|
|
colors
|
|
rownames(expr_SxG[1])[31]
|
|
grepl(rownames(expr_SxG)[31], "MDD", fixed=TRUE)
|
|
grepl(rownames(expr_SxG)[31], MDD, fixed=TRUE)
|
|
grepl(rownames(expr_SxG)[31], "MDD", fixed=TRUE)
|
|
grepl(rownames(expr_SxG)[31], "MDD")
|
|
class(rownames(expr_SxG)[31])
|
|
grepl("MDD", rownames(expr_SxG)[31])
|
|
colors <- list()
|
|
for (i in nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl("MDD", rownames(expr_SxG)[i]), "black", "red")
|
|
}
|
|
colors
|
|
grepl("MDD", rownames(expr_SxG)[157])
|
|
rownames(expr_SxG)[157]
|
|
rownames(expr_SxG)[1565]
|
|
rownames(expr_SxG)[156]
|
|
ifelse(grepl("MDD", rownames(expr_SxG)[156]), "black", "red")
|
|
ifelse(grepl("MDD", rownames(expr_SxG)[155]), "black", "red")
|
|
ifelse(grepl("MDD", rownames(expr_SxG)[1]), "black", "red")
|
|
colors[1]
|
|
colors[2]
|
|
colors[1,1]
|
|
colors[,1]
|
|
colors[1,]
|
|
nrow(expr_SxG)
|
|
ifelse(grepl("MDD", rownames(expr_SxG)[2]), "black", "red")
|
|
ifelse(grepl("MDD", rownames(expr_SxG)[3]), "black", "red")
|
|
ifelse(grepl("MDD", rownames(expr_SxG)[155]), "black", "red")
|
|
for i in nrow(expr_SxG){ print(i) {
|
|
for i in nrow(expr_SxG){ print(i) {
|
|
for (i in nrow(expr_SxG)){ print(i) {
|
|
for (i in nrow(expr_SxG)){ print(i) }
|
|
for (i in 1:nrow(expr_SxG)){ print(i) }
|
|
for (i in 1:nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl("MDD", rownames(expr_SxG)[i]), "black", "red")
|
|
}
|
|
colors
|
|
# umap to cluster observations
|
|
obs_umap = umap(expr_SxG, config=custom.config)
|
|
colors <- list()
|
|
for (i in 1:nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl("MDD", rownames(expr_SxG)[i]), "black", "red")
|
|
}
|
|
dim(obs_umap$layout)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
class(colors)
|
|
dim(colors)
|
|
for (i in 1:nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl("MDD", rownames(expr_SxG)[i]), "black", "red")
|
|
}
|
|
colors
|
|
colors[157]
|
|
colors[157][1]
|
|
class(colors)
|
|
?plot
|
|
?plot
|
|
class(sample(letters[1:3]),10)
|
|
class(sample(letters[1:3], 10, replace=TRUE))
|
|
colors
|
|
dim
|
|
?dim
|
|
dim(colors)
|
|
#add colors for MDD/HC
|
|
colors = rep("black",nrow(expr_SxG))
|
|
class(colors)
|
|
colors
|
|
for (i in 1:nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl("MDD", rownames(expr_SxG)[i]), "black", "red")
|
|
}
|
|
colors
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
# change umap config parameters
|
|
custom.config = umap.defaults
|
|
custom.config$random_state = 123
|
|
custom.config$n_epochs = 500
|
|
# umap to cluster observations
|
|
obs_umap = umap(expr_SxG, config=custom.config)
|
|
#add colors for MDD/HC
|
|
colors = rep("black",nrow(expr_SxG))
|
|
for (i in 1:nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl("MDD", rownames(expr_SxG)[i]), "black", "red")
|
|
}
|
|
dim(obs_umap$layout)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
legend("bottomleft", legend = c("class -1","class +1"),
|
|
col=c("black","red"), pch=19)
|
|
custom.config = umap.defaults
|
|
custom.config$random_state = 123
|
|
custom.config$n_epochs = 500
|
|
# umap to cluster observations
|
|
obs_umap = umap(expr_SxG, config=custom.config)
|
|
#add colors for MDD/HC
|
|
colors = rep("black",nrow(expr_SxG))
|
|
for (i in 1:nrow(expr_SxG)) {
|
|
colors[i] <- ifelse(grepl("MDD", rownames(expr_SxG)[i]), "black", "red")
|
|
}
|
|
dim(obs_umap$layout)
|
|
plot(obs_umap$layout, col=colors,
|
|
main="umap of observations", xlab="umap dim1", ylab="umap dim2")
|
|
legend("bottomleft", legend = c("MDD","HC"),
|
|
col=c("black","red"), pch=19)
|
|
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]),
|
|
median(log2(sense.filtered.cpm[,1])))
|
|
)
|
|
rownames(data) = c("Original", "Log2 Transformed")
|
|
data
|