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PCA plot

get_pca_plot.Rd

Uses normalized counts to compute principal components and plot samples, optionally restricting to selected groups or genes.

Usage

get_pca_plot(
  x,
  sample_group = NULL,
  group_column = NULL,
  genes = NULL,
  top_n_genes = NULL,
  label_replicates = FALSE,
  label_size = 3,
  circle_size = 10,
  sample_colors = TRUE,
  shape_by = NULL,
  shape_values = NULL,
  sample.seed = 123,
  show_clusters = FALSE
)

Arguments

x

A VISTA object containing normalized counts.

sample_group

Optional character vector of group labels (taken from the column specified by group_column, defaulting to the stored grouping column) used to subset samples prior to PCA. Use NULL to include all samples.

group_column

Optional column name in sample_info to use for grouping. Defaults to the stored grouping column.

genes

Optional character vector of gene identifiers to restrict the PCA input matrix. When NULL, all genes are used.

top_n_genes

Optional integer selecting the top most variable genes to include. Ignored when genes is supplied.

label_replicates

Logical; if TRUE, sample names are drawn next to the points.

label_size

Numeric size of replicate labels when label_replicates = TRUE.

circle_size

Numeric size of the plotted points.

sample_colors

Logical; if TRUE, apply the stored group colors to the points.

shape_by

Optional column name in sample_info used to map point shape. When NULL, shapes are not mapped.

shape_values

Optional vector of shapes passed to scale_shape_manual() when shape_by is set. Use a named vector to map shapes to specific levels.

sample.seed

Deprecated/unused; retained for backward compatibility.

show_clusters

Logical; add normal ellipses per group when TRUE.

Value

A ggplot object showing the first two PCs.

Examples

# Create VISTA object
data("count_data", package = "VISTA")
data("sample_metadata", package = "VISTA")

vista <- create_vista(
  counts = count_data[1:200, ],
  sample_info = sample_metadata[1:6, ],
  column_geneid = "gene_id",
  group_column = "cond_long",
  group_numerator = "treatment1",
  group_denominator = "control"
)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing

# Basic PCA plot
get_pca_plot(vista)


# With sample labels
get_pca_plot(vista, label_replicates = TRUE)


# Using top variable genes
get_pca_plot(vista, top_n_genes = 100)


# With confidence ellipses
get_pca_plot(vista, show_clusters = TRUE)
#> Too few points to calculate an ellipse
#> Too few points to calculate an ellipse
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_path()`).