After data is loaded in as a df it can be utilized by ggvolc. The most basic example:
ggvolc(df)
You can make the same plot with 10 "attention genes" (a df of exactly 10 genes to display gene names for on the plot) It is important to note that the gene names, col_names, row_names, and data values must be identical for it to work
dggvolc(df, attention_genes)
Example "attention genes" df
| genes | baseMean | log2FoldChange | IfcSE | stat | pvalue | padj |
|---|---|---|---|---|---|---|
| gene1 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene2 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene3 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene4 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene5 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene6 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene7 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene8 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene9 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
| gene10 | 343.43 | -0.0344 | 0.45344 | -0.453954 | 0.98343 | 0.9364 |
Customize plot by increasing the point size by log2FoldChange and seqments based on the p-value
ggvolc(df, attention_genes, size_var = "log2FoldChange", add_seg = TRUE)
Customize plot by increasing the point size by log2FoldChange and seqments based on the p-value
ggvolc(df, attention_genes, size_var = "pvalue", add_seg = TRUE)
More customization! Color and specific thresholds
gggvolc(
df,
attention_genes,
size_var = "pvalue",
p_value = 0.05,
fc = 1,
not_sig_color = "grey82",
down_reg_color = "#00798c",
up_reg_color = "#d1495b",
add_seg = FALSE
)