plot PCA of cluster abundances

The function creates a PCA plot of the cluster abundances and saves the plot in results/abundance folder

Usage

pcaSeurat(
  object,
  cluster,
  sample,
  condition,
  width = 20,
  height = 5,
  dir_output = "."
)

Arguments

object

A Seurat object

cluster

A character string indicating the cluster column in the metadata of the Seurat object

sample

A character string indicating the sample column in the metadata of the Seurat object

condition

A character string indicating the condition column in the metadata of the Seurat object

width

The width of the plot

height

The height of the plot

dir_output

directory to save the output plot (default: ".")

Value

Create and save PCA plot of Seurat cluster abundances.

Examples

library(Seurat)
# Setup example data
set.seed(123)
pbmc_small$cluster <- sample(c("Cluster1", "Cluster2"), ncol(pbmc_small), replace = TRUE)
pbmc_small$sample <- sample(c("CSF_P01", "CSF_P02", "CSF_P03", "CSF_P04"), 
                           ncol(pbmc_small), replace = TRUE)

# Create lookup table for conditions
lookup <- data.frame(
  sample = c("CSF_P01", "CSF_P02", "CSF_P03", "CSF_P04"),
  condition = c(rep("control", 2), rep("treatment", 2))
)

# Add condition information to metadata
pbmc_small@meta.data <- pbmc_small@meta.data |>
  tibble::rownames_to_column("barcode") |>
  dplyr::left_join(lookup, by = "sample") |>
  tibble::column_to_rownames("barcode")

# Generate PCA plot
pcaSeurat(
  object = pbmc_small,
  cluster = "cluster",
  sample = "sample", 
  condition = "condition",
  width = 20,
  height = 5
)
#> Joining with `by = join_by(cluster)`

unlink("pbmc_small_condition_cluster.pdf")