Gene and ESTIMATE score relationship plots

Compare ESTIMATE scores between target-gene high and low expression groups, or draw continuous gene-expression correlations with ESTIMATE scores.

Usage

EstimateGenePlot(
  object = NULL,
  score.data = NULL,
  gene.data = NULL,
  features,
  assay = NULL,
  layer = "data",
  plot_type = c("violin", "scatter"),
  split = c("median", "quantile"),
  quantile = 0.5,
  cor_method = c("spearman", "pearson", "kendall"),
  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.

gene.data

Optional gene expression matrix with genes in rows and samples in columns.

features

Target genes to plot.

assay

Assay used for `Seurat` or `SummarizedExperiment` expression.

layer

Assay layer used for `Seurat` expression.

plot_type

Plot type.

split

Split method for high/low groups in violin plots.

quantile

Quantile cutoff used when `split = "quantile"`.

cor_method

Correlation method for scatter plots.

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 22:49:58] Start standard processing workflow...
#> ℹ [2026-10-11 22:49:58] Checking a list of <Seurat>...
#> ! [2026-10-11 22:49:58] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 22:49:58] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 22:49:58] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 22:49:58] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 22:49:58] Number of available HVF: 2000
#> ℹ [2026-10-11 22:49:58] Finished check
#> ℹ [2026-10-11 22:49:58] Perform `ScaleData()`
#> ℹ [2026-10-11 22:49:58] Perform pca linear dimension reduction
#> ℹ [2026-10-11 22:49:59] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 22:49:59] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 22:49:59] Reorder clusters...
#> ℹ [2026-10-11 22:49:59] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 22:49:59] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 22:50:06] Standard processing workflow completed
pancreas_sub <- RunESTIMATE(pancreas_sub, group.by = "SubCellType")
#> ℹ [2026-10-11 22:50:06] ESTIMATE input: 15997 genes x 8 samples
#> ℹ [2026-10-11 22:50:06] Use 8011 ESTIMATE common genes for scoring
#> ℹ [2026-10-11 22:50:06] ESTIMATE signature overlap: stromal 79, immune 77
EstimateGenePlot(
  pancreas_sub,
  features = c("GCG", "INS"),
  group.by = "SubCellType"
)