Run ambient RNA decontamination with decontX

Usage

RunDecontX(
  srt,
  assay = "RNA",
  group.by = NULL,
  batch = NULL,
  background = NULL,
  background_assay = NULL,
  bg_batch = NULL,
  assay_name = "decontXcounts",
  store_assay = TRUE,
  round_counts = FALSE,
  seed = 11,
  ...
)

Arguments

srt: A Seurat object.
assay: The name of the assay to be used for decontamination. Default is "RNA".
group.by: Cell cluster labels passed to decontX::decontX(). Can be NULL, a meta.data column name, or a vector aligned to cells. Default is NULL.
batch: Batch labels passed to decontX::decontX(). Can be NULL, a meta.data column name, or a vector aligned to cells. Default is NULL.
background: Optional background / empty-droplet input passed to decontX::decontX(). Can be a Seurat object, SingleCellExperiment, or count matrix. Default is NULL.
background_assay: Assay name used when background is a Seurat object or SingleCellExperiment. Default is NULL, which falls back to assay for Seurat background and "counts" for SingleCellExperiment background.
bg_batch: Batch labels for background passed to decontX::decontX(). Can be NULL, a metadata column name, or a vector aligned to the background droplets. Default is NULL.
assay_name: Name of the assay used to store decontaminated counts. Default is "decontXcounts".
store_assay: Whether to store decontaminated counts as a new assay. Default is TRUE.
round_counts: Whether to round decontaminated counts before creating the assay. Default is FALSE.
seed: Random seed for reproducibility. Default is 11.
...: Additional arguments passed to decontX::decontX().

Value

Returns a Seurat object with decontX contamination estimates stored in the meta.data, and optional decontaminated counts stored in a new assay.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-03-20 09:25:19] Start standard scop workflow...
#> ℹ [2026-03-20 09:25:20] Checking a list of <Seurat>...
#> ! [2026-03-20 09:25:20] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-03-20 09:25:20] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-03-20 09:25:22] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-03-20 09:25:23] Use the separate HVF from `srt_list`
#> ℹ [2026-03-20 09:25:23] Number of available HVF: 2000
#> ℹ [2026-03-20 09:25:23] Finished check
#> ℹ [2026-03-20 09:25:23] Perform `Seurat::ScaleData()`
#> ℹ [2026-03-20 09:25:24] Perform pca linear dimension reduction
#> ℹ [2026-03-20 09:25:25] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-03-20 09:25:25] Reorder clusters...
#> ℹ [2026-03-20 09:25:25] Perform umap nonlinear dimension reduction
#> ℹ [2026-03-20 09:25:25] Perform umap nonlinear dimension reduction using Standardpca (1:50)
#> ℹ [2026-03-20 09:25:29] Perform umap nonlinear dimension reduction using Standardpca (1:50)
#> ✔ [2026-03-20 09:25:34] Run scop standard workflow completed
pancreas_sub <- RunDecontX(
  pancreas_sub,
  group.by = "CellType"
)
#> ℹ [2026-03-20 09:25:34] Data type is raw counts
#> ℹ [2026-03-20 09:25:35] Running decontX
#> ℹ [2026-03-20 09:25:48] decontX contamination (median/mean/max): 0.0272 / 0.0875 / 0.6737
#> ℹ [2026-03-20 09:25:48] decontX assay stored as decontXcounts
#> ✔ [2026-03-20 09:25:48] decontX decontamination completed

FeatureStatPlot(
  pancreas_sub,
  stat.by = "decontX_contamination"
)


FeatureDimPlot(
  pancreas_sub,
  features = "decontX_contamination"
)