Differential Expression Analysis of SS vs PBMC10X+PBMC

1. load libraries

2. Significat genes and up and down

# Filter significant genes
significant_genes <- data %>% filter(p_val_adj < 0.05)

# Identify upregulated genes
upregulated_genes <- significant_genes %>% filter(avg_log2FC > 1.5)

# Identify downregulated genes
downregulated_genes <- significant_genes %>% filter(avg_log2FC < -1.5)

3. VolcanoPlot

# Create a volcano plot
ggplot(data, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  geom_point(aes(color = avg_log2FC > 1 | avg_log2FC < -1)) +
  scale_color_manual(values = c("grey", "red")) +
  labs(title = "Volcano Plot", x = "Log2 Fold Change", y = "-Log10 Adjusted P-value") +
  theme_minimal()

NA
NA
NA
NA
NA

4. VolcanoPlot2

# Highlight interesting genes
interesting_genes <- c("UBE2C", "PCLAF", "TYMS", "RPS4Y1", "LINC00861", "IL7R", "TCF7")

ggplot(data, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  geom_point(aes(color = avg_log2FC > 1 | avg_log2FC < -1)) +
  geom_text(data = filter(data, gene %in% interesting_genes),
            aes(label = gene), size = 3, hjust = 0, vjust = 0, nudge_x = 0.1, nudge_y = 0.1) +
  scale_color_manual(values = c("grey", "red")) +
  labs(title = "Volcano Plot with Highlighted Genes", x = "Log2 Fold Change", y = "-Log10 Adjusted P-value") +
  theme_minimal()

NA
NA