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Dimension estimate diagnostic plot

Source: R/DimsEstimate.R
DimsEstimatePlot.Rd

Dimension estimate diagnostic plot

Usage

DimsEstimatePlot(
  object,
  max_pcs = 50,
  variance_thresholds = c(0.6, 0.7, 0.8, 0.9),
  reduction = NULL,
  palcolor = c("#D70440", "#0AA344", "#1772B4"),
  aspect.ratio = NULL,
  title = NULL,
  subtitle = NULL,
  xlab = "Principal component",
  theme_use = "theme_scop",
  theme_args = list(),
  seed = 11,
  verbose = TRUE,
  srt = NULL
)

Arguments

object

A Seurat object with a PCA-like reduction computed.

max_pcs

Maximum number of PCs to visualize.

variance_thresholds

Numeric vector of variance thresholds to mark.

reduction

Reduction name to inspect. Default is NULL, which automatically selects a PCA-like reduction via DefaultReduction() with pattern = "pca".

palcolor

Colors for the selected-PC line, curves, and bars, respectively.

aspect.ratio

Aspect ratio of the plot.

title

Plot title. When NULL (default), reports the selected number of PCs.

subtitle

Plot subtitle. Default is NULL.

xlab

X-axis label.

theme_use

Theme function used to style the plot.

theme_args

Other arguments passed to the theme_use.

seed

Random seed.

verbose

Whether to print the message. Default is TRUE.

srt

Deprecated alias for object; supply exactly one of the two. It will be removed in scop 1.0.0.

Value

A ggplot object showing per-PC explained variance (bars, left axis) and cumulative explained variance (line, right axis).

See also

RunDimsEstimate

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-10-11 22:44:37] Start standard processing workflow...
#> ℹ [2026-10-11 22:44:37] Checking a list of <Seurat>...
#> ! [2026-10-11 22:44:37] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 22:44:37] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 22:44:37] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 22:44:37] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 22:44:37] Number of available HVF: 2000
#> ℹ [2026-10-11 22:44:37] Finished check
#> ℹ [2026-10-11 22:44:37] Perform `ScaleData()`
#> ℹ [2026-10-11 22:44:37] Perform pca linear dimension reduction
#> ℹ [2026-10-11 22:44:38] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 22:44:38] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 22:44:38] Reorder clusters...
#> ℹ [2026-10-11 22:44:38] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 22:44:38] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 22:44:44] Standard processing workflow completed
DimsEstimatePlot(pancreas_sub)