ESTIMATE score plots

Visualize ESTIMATE stromal, immune, combined ESTIMATE, and tumor-purity scores from a RunESTIMATE() result.

Usage

EstimateScorePlot(
  object = NULL,
  score.data = NULL,
  group.by = NULL,
  group.data = NULL,
  plot_type = c("violin", "box", "heatmap", "cor"),
  scores = c("StromalScore", "ImmuneScore", "ESTIMATEScore", "TumorPurity"),
  add_stat = TRUE,
  ...
)

Arguments

object

Optional RunESTIMATE() bundle, SummarizedExperiment, or Seurat object containing ESTIMATE results.

score.data

Optional score matrix or data frame with samples in rows.

group.by

Optional grouping column for grouped violin and box plots.

group.data

Optional named vector or data frame containing sample groups.

plot_type

Plot type.

scores

ESTIMATE score columns to plot.

add_stat

Whether to add group comparison labels to violin or box plots. Requires ggpubr.

...

Additional plotting arguments.

Value

A ggplot object.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-10-11 22:50:08] Start standard processing workflow...
#> ℹ [2026-10-11 22:50:08] Checking a list of <Seurat>...
#> ! [2026-10-11 22:50:08] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 22:50:08] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 22:50:08] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 22:50:08] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 22:50:08] Number of available HVF: 2000
#> ℹ [2026-10-11 22:50:08] Finished check
#> ℹ [2026-10-11 22:50:08] Perform `ScaleData()`
#> ℹ [2026-10-11 22:50:08] Perform pca linear dimension reduction
#> ℹ [2026-10-11 22:50:09] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 22:50:09] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 22:50:09] Reorder clusters...
#> ℹ [2026-10-11 22:50:09] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 22:50:09] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 22:50:15] Standard processing workflow completed
pancreas_sub <- RunESTIMATE(pancreas_sub, group.by = "SubCellType")
#> ℹ [2026-10-11 22:50:15] ESTIMATE input: 15997 genes x 8 samples
#> ℹ [2026-10-11 22:50:15] Use 8011 ESTIMATE common genes for scoring
#> ℹ [2026-10-11 22:50:15] ESTIMATE signature overlap: stromal 79, immune 77
EstimateScorePlot(pancreas_sub, group.by = "SubCellType")