microarray_analysis.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)
library(tidyr)
library(dplyr)
library(ggplot2)
library(Biobase)

# load series and platform data from GEO
# 
# gset <- tryCatch({
#   getGEO("GSE76250", GSEMatrix = TRUE, AnnotGPL = TRUE)
# }, error = function(e) e)


gset <- getGEO("GSE76250", GSEMatrix =TRUE, AnnotGPL=TRUE)
if (length(gset) > 1) idx <- grep("GPL17586", 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",
               "00000000000000000000000000000000000000000000000000",
               "00000000000000000000000000000000000000000000000000",
               "000000000000000111111111111111111111111111111111")



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) }

ex <- exprs(gset)



ex_df <- as.data.frame(ex)
feature_data <- fData(gset)
pheno_data <- pData(phenoData(gset))

# Assuming 'feature_data' is your dataframe and 'gene_assignment' is the column with the data


feature_data$Gene.Symbol <- sapply(strsplit(as.character(feature_data$gene_assignment), " // "), function(x) x[2])
feature_data$Gene.ID <- sapply(strsplit(as.character(feature_data$gene_assignment), " // "), function(x) x[5])

feature_data$Gene.ID1 <- sapply(strsplit(as.character(feature_data$gene_assignment), " // "), function(x) x[1])
feature_data$Gene.Symbol1 <- sapply(strsplit(as.character(feature_data$gene_assignment), " // "), function(x) x[2])

feature_data$Gene.ID <- sapply(strsplit(as.character(feature_data$Gene.ID), " /// "), function(x) x[1])


count_missing <- function(x) {
  sum(is.na(x) | x %in% c("--", "nan", "NA", "", " ", "---"))
}

# Apply this function to each of the specified columns

missing_counts <- sapply(feature_data[c("Gene.ID", "Gene.Symbol", "Gene.ID1", "Gene.Symbol1")], count_missing)

print(missing_counts)


feature_data <- feature_data %>%
  filter(!is.na(Gene.Symbol) & !Gene.Symbol %in% c("--", "nan", "NA", "", " ", "---"))


# feature_data <- feature_data[, c("ID", "Gene.ID", "Gene.symbol", "Platform_SPOTID")]
feature_data <- feature_data[, c("ID", "Gene.Symbol")]

combined_data <- merge(ex_df, feature_data, by.x = "row.names", by.y = "ID")
# Rename the 'Row.names' column to 'ID' in the resulting data frame for clarity
names(combined_data)[1] <- "ID"




aggregated_data <- combined_data %>%
  group_by(Gene.Symbol) %>%
  summarise(across(starts_with("GSM"), mean, na.rm = TRUE))


aggregated_data <- as.data.frame(aggregated_data)


feature_data <- feature_data[!duplicated(feature_data$Gene.Symbol), ]

aggregated_data_with_ids <- aggregated_data %>%
  left_join(feature_data, by = "Gene.Symbol")

# Check the first few rows of the new dataframe to confirm the merge
head(aggregated_data_with_ids)



fs_data <- aggregated_data_with_ids[, c("ID", "Gene.Symbol")]
row.names(aggregated_data_with_ids) <- aggregated_data_with_ids$Gene.Symbol

aggregated_data_with_ids <- aggregated_data_with_ids %>%
  select(-ID, -Gene.Symbol)



expression_matrix <- as.matrix(aggregated_data_with_ids)

rownames(fs_data) <- fs_data$Gene.Symbol


new_gset <- ExpressionSet(
  assayData = expression_matrix,       # Replace 'expression_matrix' with your actual matrix of expression data
  phenoData = phenoData(gset),         # Use the 'phenoData' directly if it is already an AnnotatedDataFrame
  featureData = AnnotatedDataFrame(fs_data)  # Replace 'fs_data' with your feature data
)

# Inspecting the new ExpressionSet

gset <- new_gset





# assign samples to groups and set up design matrix
gs <- factor(sml)
groups <- make.names(c("TNBC","normal"))
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" ,"Gene.Symbol","adj.P.Val","P.Value","t","B","logFC"))
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
# 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 < 0.05) 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 ("GSE76250", "/", 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 ("GSE76250", "/", 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, GSE76250")




gene_data <- as.data.frame(t(exprs(gset)))
pheno_data <- pData(phenoData(gset))

complete <- merge(gene_data, pheno_data, by.x = "row.names", by.y = "row.names")

data_to_plot <- complete %>%
  select(TOP2A, group)  

# Create the plot
ggplot(data_to_plot, aes(x = group, y = TOP2A, color = group)) +
  geom_boxplot() +
  labs(title = "Expression of TOP2A in TNBC and Normal Samples",
       x = "Sample Group",
       y = "Expression Level") +
  theme_minimal()  # Adds a minimalistic theme to the plot

# Print the plot
print(ggplot_object)