This function performs PCA on the variance-stabilized transformed data and generates a PCA plot. It can visualize sample relationships and identify potential batch effects or outliers.
Usage
run_pca(
vsd,
group_by,
shape = NULL,
pals = NULL,
size = 4,
save_prefix = NULL,
save_data = TRUE,
save_plot = TRUE,
save_dir = getwd()
)Arguments
- vsd
A DESeq2 object containing the normalized gene expression data
- group_by
Column name in colData(vsd) to group by.
- shape
Column name in colData(vsd) to use for shape in the PCA plot. Default is NULL
- pals
Vector of colors to use for groups. If NULL, uses default ggplot2 colors. Default is NULL
- size
Size of points in the PCA plot. Default is 4
- save_prefix
Prefix for the save file. Default is NULL
- save_data
Logical. If TRUE, saves PCA results to TSV. Default is TRUE
- save_plot
Logical. If TRUE, saves the PCA plot to PDF. Default is TRUE
- save_dir
Directory to save the results. Default is the current working directory
Examples
if (FALSE) { # \dontrun{
# Basic PCA plot
p <- run_pca(vsd)
# PCA plot with shape and save results
p <- run_pca(vsd, shape = "Batch", save_data = TRUE)
# PCA plot with custom colors
p <- run_pca(vsd, pals = c("red", "blue", "green"))
} # }