Hello everyone, This is my first time performing DESeq2, and even PCA analysis. I have 8 samples. (4 WT and 4 mutant) I have done PCA Analysis, and this is my output. There is not much variance; what can I conclude from this?

I can see the WT clustering together but the mutant samples are not far apart from each other. I expected the WT samples to cluster together and the mutants to do the same (but on opposite sides)

Someone pls help, I need to present this data in my presentation but I cannot wrap my head around this. Is this bad quality data? What does it say?

The code that I executed is this:

# Load necessary libraries
library(DESeq2)
library(ggplot2)

# Enhance the synthetic count data by increasing the difference
set.seed(123)  # Ensure reproducibility
base_counts <- matrix(sample(500:1000, 100 * 4, replace = TRUE), ncol = 4, nrow = 100)

# Increase variability for mutants: use larger subtractive modifiers
mutant_modifiers <- matrix(sample(300:500, 100 * 4, replace = TRUE), ncol = 4, nrow = 100)
mutant_counts <- pmax(base_counts - mutant_modifiers, 1)  # Ensure no zero counts

# Combine wild type and mutant counts
countData <- cbind(base_counts, mutant_counts)

rownames(countData) <- paste("Gene", 1:100, sep=" ")
colnames(countData) <- paste("Sample", 1:8, sep=" ")

# Create colData with sample information
sampleCondition <- c(rep("WildType", 4), rep("Mutant", 4))
colData <- DataFrame(condition = factor(sampleCondition, levels = c("WildType", "Mutant")))

# Construct DESeq2 dataset
dds <- DESeqDataSetFromMatrix(countData = countData,
                              colData = colData,
                              design = ~ condition)
dds <- DESeq(dds)

# Perform rlog transformation with blind = FALSE to utilize condition information
rld <- rlog(dds, blind = FALSE)

# PCA focusing on increased contrast
pcaData <- prcomp(t(assay(rld)))

# Create data frame for ggplot
pcaResults <- as.data.frame(pcaData$x)
pcaResults$Sample <- rownames(pcaResults)
pcaResults$Condition <- colData$condition

# Plotting the PCA with enhanced contrast
ggplot(pcaResults, aes(x = PC1, y = PC2, color = Condition, label = Sample)) +
  geom_point(size = 5) +
  geom_text(vjust = 2, hjust = 0.5) +
  labs(title = "Enhanced PCA Plot of RNA-seq Data", x = "Principal Component 1", y = "Principal Component 2") +
  theme_minimal()