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01_Differential_Expression_Analysis/Limma/mRNA/GSE23400_mRNA.R

+# Version info: R 4.2.2, Biobase 2.58.0, GEOquery 2.66.0, limma 3.54.0
+################################################################
+#   Differential expression analysis with limma
+library(GEOquery)
+library(limma)
+library(umap)
+
+# load series and platform data from GEO
+
+gset <- getGEO("GSE23400", GSEMatrix =TRUE, AnnotGPL=TRUE)
+if (length(gset) > 1) idx <- grep("GPL96", attr(gset, "names")) else idx <- 1
+gset <- gset[[idx]]
+
+# make proper column names to match toptable 
+fvarLabels(gset) <- make.names(fvarLabels(gset))
+
+# group membership for all samples
+gsms <- paste0("00000000000000000000000000000000000000000000000000",
+               "00011111111111111111111111111111111111111111111111",
+               "111111")
+sml <- strsplit(gsms, split="")[[1]]
+
+# log2 transformation
+ex <- exprs(gset)
+qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
+LogC <- (qx[5] > 100) ||
+  (qx[6]-qx[1] > 50 && qx[2] > 0)
+if (LogC) { ex[which(ex <= 0)] <- NaN
+exprs(gset) <- log2(ex) }
+
+# assign samples to groups and set up design matrix
+gs <- factor(sml)
+groups <- make.names(c("normal","cancer"))
+levels(gs) <- groups
+gset$group <- gs
+design <- model.matrix(~group + 0, gset)
+colnames(design) <- levels(gs)
+
+gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
+
+fit <- lmFit(gset, design)  # fit linear model
+
+# set up contrasts of interest and recalculate model coefficients
+cts <- paste(groups[1], groups[2], sep="-")
+cont.matrix <- makeContrasts(contrasts=cts, levels=design)
+fit2 <- contrasts.fit(fit, cont.matrix)
+
+# compute statistics and table of top significant genes
+fit2 <- eBayes(fit2, 0.01)
+tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
+
+tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","GB_ACC","SPOT_ID","Gene.Symbol","Gene.symbol","Gene.title"))
+write.table(tT, file=stdout(), row.names=F, sep="\t")
+
+# Visualize and quality control test results.
+# Build histogram of P-values for all genes. Normal test
+# assumption is that most genes are not differentially expressed.
+tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
+hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
+     ylab = "Number of genes", main = "P-adj value distribution")
+
+# summarize test results as "up", "down" or "not expressed"
+dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=0)
+
+# Venn diagram of results
+vennDiagram(dT, circle.col=palette())
+
+# create Q-Q plot for t-statistic
+t.good <- which(!is.na(fit2$F)) # filter out bad probes
+qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
+
+# volcano plot (log P-value vs log fold change)
+colnames(fit2) # list contrast names
+ct <- 1        # choose contrast of interest
+volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
+            highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
+
+# MD plot (log fold change vs mean log expression)
+# highlight statistically significant (p-adj < 0.05) probes
+plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
+abline(h=0)
+
+################################################################
+# General expression data analysis
+ex <- exprs(gset)
+
+# box-and-whisker plot
+dev.new(width=3+ncol(gset)/6, height=5)
+ord <- order(gs)  # order samples by group
+palette(c("#1B9E77", "#7570B3", "#E7298A", "#E6AB02", "#D95F02",
+          "#66A61E", "#A6761D", "#B32424", "#B324B3", "#666666"))
+par(mar=c(7,4,2,1))
+title <- paste ("GSE23400", "/", annotation(gset), sep ="")
+boxplot(ex[,ord], boxwex=0.6, notch=T, main=title, outline=FALSE, las=2, col=gs[ord])
+legend("topleft", groups, fill=palette(), bty="n")
+dev.off()
+
+# expression value distribution
+par(mar=c(4,4,2,1))
+title <- paste ("GSE23400", "/", annotation(gset), " value distribution", sep ="")
+plotDensities(ex, group=gs, main=title, legend ="topright")
+
+# UMAP plot (dimensionality reduction)
+ex <- na.omit(ex) # eliminate rows with NAs
+ex <- ex[!duplicated(ex), ]  # remove duplicates
+ump <- umap(t(ex), n_neighbors = 15, random_state = 123)
+par(mar=c(3,3,2,6), xpd=TRUE)
+plot(ump$layout, main="UMAP plot, nbrs=15", xlab="", ylab="", col=gs, pch=20, cex=1.5)
+legend("topright", inset=c(-0.15,0), legend=levels(gs), pch=20,
+       col=1:nlevels(gs), title="Group", pt.cex=1.5)
+library("maptools")  # point labels without overlaps
+pointLabel(ump$layout, labels = rownames(ump$layout), method="SANN", cex=0.6)
+
+# mean-variance trend, helps to see if precision weights are needed
+plotSA(fit2, main="Mean variance trend, GSE23400")
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