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Generate a UMAP plot for samples in a VISTA object

get_umap_plot.Rd

Runs UMAP on normalized counts, optionally restricting to selected groups or genes. UMAP is intended for exploratory sample-level structure.

Usage

get_umap_plot(
  x,
  sample_group = NULL,
  group_column = NULL,
  color_by = 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,
  n_neighbors = 15,
  min_dist = 0.1,
  metric = "euclidean",
  seed = 123
)

Arguments

x

A VISTA object.

sample_group

Optional character vector of groups to include (based on group_column).

group_column

Optional column name in sample_info used for filtering/grouping. Defaults to the stored grouping column.

color_by

Optional column name in sample_info used for point color. Defaults to group_column.

genes

Optional character vector of gene identifiers to restrict the matrix.

top_n_genes

Optional integer selecting top variable genes to include.

label_replicates

Logical; draw sample labels when TRUE.

label_size

Numeric label size when label_replicates = TRUE.

circle_size

Numeric point size.

sample_colors

Logical; when TRUE, apply VISTA group colors if coloring by the grouping column. Otherwise generate a qualitative palette.

shape_by

Optional column name in sample_info used to map point shape.

shape_values

Optional vector passed to scale_shape_manual() when shape_by is set.

n_neighbors

UMAP n_neighbors parameter.

min_dist

UMAP min_dist parameter.

metric

UMAP distance metric.

seed

Integer random seed passed to UMAP.

Value

A ggplot object with UMAP1/UMAP2 coordinates.

Examples

if (requireNamespace("uwot", quietly = TRUE)) {
  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"
  )

  get_umap_plot(vista)
  get_umap_plot(vista, color_by = "cell")
}
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> Warning: `n_neighbors` (15) must be smaller than sample size (6); using 5.
#> Warning: `n_neighbors` (15) must be smaller than sample size (6); using 5.