# 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("GSE34933", GSEMatrix =TRUE, AnnotGPL=FALSE)
if (length(gset) > 1) idx <- grep("GPL11487", 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 <- "00000111111XXXXX"
sml <- strsplit(gsms, split="")[[1]]

# filter out excluded samples (marked as "X")
sel <- which(sml != "X")
sml <- sml[sel]
gset <- gset[ ,sel]

# 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("PC","NORM"))
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","miRNA_ID","SPOT_ID"))
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=0.5, lfc=1)

# 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
# Please note that the code provided to generate graphs serves as a guidance to
# the users. It does not replicate the exact GEO2R web display due to multitude
# of graphical options.
# 
# The following will produce basic volcano plot using limma function:
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 < 0.5) 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
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 ("GSE34933", "/", 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")

# expression value distribution
par(mar=c(4,4,2,1))
title <- paste ("GSE34933", "/", 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 = 5, random_state = 123)
par(mar=c(3,3,2,6), xpd=TRUE)
plot(ump$layout, main="UMAP plot, nbrs=5", xlab="", ylab="", col=gs, pch=20, cex=1.5)
legend("topright", inset=c(-0.15,0), legend=levels(gs), pch=20,
col=1:nlevels(gs), title="Group", pt.cex=1.5)
library("maptools")  # point labels without overlaps
pointLabel(ump$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, GSE34933")