Ambient RNA decontamination with decontX

Usage

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

Arguments

object: A Seurat object.
assay: Assay to decontaminate.
group.by, batch: Cell cluster and batch labels passed to decontX::decontX(). Column name, cell-aligned vector, or NULL.
background: Background / empty-droplet input: a Seurat object, SingleCellExperiment, or count matrix.
background_assay: Assay used when background is a Seurat or SingleCellExperiment. NULL uses assay (Seurat) or "counts" (SCE).
bg_batch: Batch labels for background.
assay_name, store_assay, round_counts: Store rounded decontaminated counts as a new assay.
data_type: Optional CheckDataType() result, used internally to avoid rescanning the count matrix.
seed: Random seed.
...: Passed to decontX::decontX().
verbose: Whether to print messages.
srt: Deprecated alias for object; supply exactly one of the two. It will be removed in scop 1.0.0.

Value

A Seurat object with decontX contamination in meta.data and optional decontaminated counts in a new assay.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-10-11 23:14:05] Start standard processing workflow...
#> ℹ [2026-10-11 23:14:05] Checking a list of <Seurat>...
#> ! [2026-10-11 23:14:05] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:14:05] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:14:05] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:14:05] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:14:05] Number of available HVF: 2000
#> ℹ [2026-10-11 23:14:05] Finished check
#> ℹ [2026-10-11 23:14:05] Perform `ScaleData()`
#> ℹ [2026-10-11 23:14:05] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:14:06] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 23:14:06] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:14:06] Reorder clusters...
#> ℹ [2026-10-11 23:14:06] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:14:06] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 23:14:13] Standard processing workflow completed
pancreas_sub <- RunDecontX(
  pancreas_sub,
  group.by = "CellType"
)
#> ℹ [2026-10-11 23:14:13] Running decontX
#> ℹ [2026-10-11 23:14:13] Data type is raw counts
#> ℹ [2026-10-11 23:14:29] decontX contamination (median/mean/max): 0.0272 / 0.0875 / 0.6737
#> ℹ [2026-10-11 23:14:29] decontX assay stored as decontXcounts
#> ✔ [2026-10-11 23:14:29] decontX decontamination completed

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


FeatureDimPlot(
  pancreas_sub,
  features = "decontX_contamination"
)